Patent Publication Number: US-2003224422-A1

Title: Pre-and post therapy gene expression profiling to identify drug targets

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
     [0001] This application claims the benefit of U.S. Provisional Application No. 60/370,835, filed Apr. 8, 2002 and U.S. Provisional Application No. 60/449,893, filed Feb. 25, 2003, each of which is hereby incorporated in its entirety by reference herein. 
    
    
     FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT  
     [0002] This invention was made in part with U.S. Government support under National Institutes of Health grant nos. R37 CA36401, R01 CA78224, RO1 CA51001 RO1 CA71907, U01 GM61393, U01 GM61394, and Cancer Center Support Grant CA21765. The U.S. Government may have certain rights in this invention. 
    
    
     
       FIELD OF THE INVENTION  
       [0003] The present invention relates generally to drug discovery and more specifically to the identification of biological targets for drug intervention to improve current therapies and to methods of predicting the therapeutic efficacy of combination therapies.  
       BACKGROUND OF THE INVENTION  
       [0004] Modern drug discovery efforts rely heavily on the screening of compounds for activity against biological targets; proteins (and the genes which encode them) whose presence, absence or abnormal regulation has been associated with a particular disease or condition. Biological targets are used in standard screening assays for drugs to treat their associated condition. Such assays may be designed to identify compounds that can directly interact and modulate the target protein activity or compounds that affect expression of the target protein.  
       [0005] The major limiting step in this process used to be the availability of a sufficient number and variety of compounds to screen for biological activity. With the advent of combinatorial chemistry and accumulation of vast chemical libraries, the actual screening process oftentimes became the rate-limiting step in this process. The development of high-throughput and ultra-high throughput screening assays has largely removed screening itself as a rate limiting step and allowed entire compound libraries to be screened in a relatively short periods.  
       [0006] With these advances in the number of compounds available for screening and the speed with which screening can be accomplished, attention has become focused on new biological targets. Significant effort has been expended on identifying targets for various diseases and a number of approaches have been created to identify such targets. Still, there remains a significant need for methods to identify potential biological targets to treat diseases or improve current therapy and for methods to predict the therapeutic efficacy of new therapies.  
       SUMMARY OF THE INVENTION  
       [0007] The present invention provides methods for identifying biological targets for drug screening to improve currently available therapies for any desired condition. The biological targets are identified based on their response to therapy. According to the invention, genes whose expression prior to a selected therapy are found to be significantly different from their expression subsequent to therapy are identified, along with their expression products, as candidate screening targets for modulating drugs which may be used to improve treatment of the condition.  
       [0008] In another aspect, changes in pre-therapy vs. post-therapy gene expression are further associated with response to therapy. According to this aspect, genes whose change in expression before and after therapy are significantly different in those patients which did not respond favorably to therapy compared to patients which did respond favorably are identified, along with their expression products, as screening targets for drugs which may be used to improve treatment of the selected condition.  
       [0009] The present invention also provides methods for comparing therapies and predicting whether a first therapy will have greater therapeutic efficacy than a second therapy. The method comprises determining the expression levels of one or more genes in a sample from patients before and after treatment with the first therapy and the second therapy, where changes in the expression levels of the genes are correlated with a favorable or unfavorable response to therapy. The changes in the expression levels of the genes before and after treatment with the first therapy are then compared with the changes in the expression levels of the genes before and after treatment with the second therapy to predict whether the first therapy will have greater therapeutic efficacy than the second therapy.  
       [0010] In another aspect, the present invention provides methods for predicting whether a first therapy will have greater deleterious effects in a patient than a second therapy. The method comprises determining the expression levels of one or more genes in a sample from patients before and after treatment with the first therapy and the second therapy, where changes in the expression levels of the genes whose expression levels are determined are correlated with deleterious effects of therapy in a patient. The changes in the expression levels of the genes before and after treatment with the first therapy is then compared with the changes in the expression levels of the genes before and after treatment with the second therapy to predict whether the first therapy will have greater deleterious effects in a patient than the second therapy.  
       [0011] The methods of the invention have been applied to acute lymphoblastic leukemia (ALL) to identify candidate targets for improving currently available therapies. Drug screening using the candidate target genes identified through practice of these methods, along with their expression products, represent a further aspect of the invention. 
     
    
    
     DESCRIPTION OF THE FIGURES  
     [0012]FIGS. 1A and 1B schematic representation of the process described in Example 1 to obtain pre- and post treatment gene expression data from acute lymphoblastic leukemia (ALL) patients.  
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
     [0013] The present inventions now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. Indeed, these inventions may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.  
     [0014] Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.  
     [0015] The present invention utilizes gene expression profiling in a unique way to identify genes and their expression products as biological targets for drug intervention to improve currently available therapies. This approach comprises two basic measurements:  
     [0016] 1) determining the expression level of one or more genes in a sample from a patient affected by a selected condition prior to treatment with an available therapy; and  
     [0017] 2) determining the expression level of the same genes in a corresponding sample following treatment with the therapy.  
     [0018] These measurements can then be compared to determine the effect the therapy has upon the expression of a particular gene. Those genes whose expression is not significantly affected by therapy are excluded as candidate targets for screening. Those genes whose expression is significantly increased or significantly decreased after therapy are identified as candidate targets for drug screening, along with their expression products. Such expression products include RNA and protein products naturally expressed from the subject gene.  
     [0019] The identified candidate targets may then be prioritized according to their attractiveness as screening targets. This assessment can be based on the identity of the target and its function, if known. Targets which have a known and easily assayable function, such as a kinase, a phosphatase, receptors (G-protein coupled receptors, cytokine receptors, etc), apoptotic proteins, hydroxylation, oxidation, conjugation and other enzyme reactions, protein-protein or protein-DNA or RNA interactions, and a series of others will generally be preferred for screening relative to targets which have no known function or whose function is not easily assayable. Targets which are found to play a role in biological pathways known to be directly affected by the subject condition will be particularly preferred.  
     [0020] The methods of the present invention may be applied to any condition where there is an available therapy for which improvement is needed. This includes, but is not limited to, cancers, genetic disorders, infectious diseases, hematological disorders, cardiovascular diseases, dermatological diseases, endocrine diseases, gastrointestinal disorders, etc.  
     [0021] In some embodiments, the present invention provides methods for comparing therapies and predicting whether a first therapy will have greater therapeutic efficacy or greater deleterious effects in a patient than a second therapy. The method comprises determining the expression levels of one or more genes in a sample from patients before and after treatment with the first therapy and the second therapy, where changes in the expression levels of the genes are correlated with therapeutic effects or deleterious effects of therapy in a patient. The changes in the expression levels of the genes before and after treatment for the first and second therapies are then compared to predict whether the first therapy will have greater deleterious effects in a patient than the second therapy.  
     [0022] In some embodiments, the first therapy comprises one or more therapeutic agents of interest while the second therapy does not comprise the therapeutic agent or therapeutic agents of interest. Accordingly, the methods of the invention may be used to determine whether a first therapy comprising one or more therapeutic agents of interest will have greater therapeutic efficacy or have an increased risk of deleterious effects in comparison with a second therapy that does not comprise the therapeutic agent or therapeutic agents of interest. In alternate embodiments, both the first therapy and the second therapy comprise the same therapeutic agents, but the dosage of one or more of the therapeutic agents in the first therapy differs from the dosage of the same therapeutic agent in the second therapy. Thus, the methods of the invention may also be used to determine whether a first therapy comprising a particular dosage of one or more therapeutic agent or therapeutic agents of interest will have increased therapeutic efficacy or increased risk of deleterious effects in comparison with a second therapy that comprises a different dosage of the therapeutic agent or therapeutic agents of interest. As used herein, a “therapeutic agent” is any compound or agent which is used or contemplated for use in the treatment of a selected condition.  
     [0023] Expression Levels and Expression Profiles  
     [0024] As used herein, an “expression level” or “level of expression” is a value that corresponds to a measurement of the abundance of a gene expression product. Such values may include measurements of RNA levels or protein abundance. Thus, an expression level can be a value that reflects the transcriptional state or the translation state of a gene. The transcriptional state of a sample includes the identities and abundance of the RNA species, especially mRNAs present in the sample. The transcriptional state can be conveniently determined by measuring transcript abundance by any of several existing gene expression technologies. Translational state includes the identities and abundance of the constituent protein species in the sample. As is known to those of skill in the art, the transcriptional state and translational state are related.  
     [0025] In some embodiments, the methods of the present invention comprise providing an expression profile from a sample from a patient. As used herein, an “expression profile” comprises one or more values corresponding to a measurement of the abundance of one or more gene expression products. See, for example, U.S. Pat. Nos. 6,040,138, 5,800,992, 6,020135, 6,344,316, and 6,033,860, which are hereby incorporated by reference in their entireties.  
     [0026] The samples used to determine the expression levels for genes and to generate expression profiles of the present invention can be derived from a variety of sources including, but not limited to, single cells, a collection of cells, tissue, cell culture, bone marrow, blood, or other bodily fluids. The tissue or cell source may include a tissue biopsy sample, a cell sorted population, cell culture, or a single cell. In some embodiments, the samples of the invention are derived from a human patient, while in other embodiments, the samples are derived from a model organism useful for studying a particular disease. Examples of such model organisms include, but are not limited to, mammalian model organisms including rodent model systems and primate model systems.  
     [0027] In selecting a sample, the percentage of the sample that constitutes cells having differential gene expression pre- vs. post therapy (i.e., the cells that are affected by the condition being treated or affected by the selected therapy) should be considered. Samples may comprise at least 20%, at least 30%, at least 40%, at least 50%, at least 55%, at least 60%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% cells having expression changes following therapy, with a preference for samples having a higher percentage of such cells.  
     [0028] Where the goal is to find a target for improving activity against a selected condition, samples are preferably taken from cells affected by the selected condition. For example, where the selected condition is a type of solid tumor the sample will preferably be derived from tumor tissue and will comprise tumor cells. Such samples may comprise at least 20%, at least 30%, at least 40%, at least 50%, at least 55%, at least 60%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% cells affected by the selected condition with a preference for samples having a higher percentage of such cells. The targets identified based on the differential expression from such samples pre- and post-therapy are used to screen for compounds that synergize or enhance the effect of the selected therapy on expression of the identified target. The identified targets may also be used to screen for compounds that interact with targets downstream of the target of the selected therapy, where such compounds may be useful as a therapeutic agent for the treatment of the condition. Target genes identified from such samples based on a reduction in expression following therapy are used to screen for compounds that will further reduce expression of the target gene and enhance the associated therapeutic effect. Alternatively, target genes identified based on an increase in expression following therapy are used to screen for compounds that can further enhance expression of the target gene.  
     [0029] Where the goal is to find a target to screen for compounds that lessen the deleterious effects caused by the selected therapy, samples are preferably taken from cells that are affected by the deleterious effect. The targets identified based on the differential expression from such samples pre- and post-therapy are used to screen for compounds that inhibit the effect of the selected therapy on expression of the identified target and thereby inhibit the associated deleterious effect. Target genes identified from such samples based on a reduction in expression following therapy are used to screen for compounds that will enhance expression of the target gene and lessen the deleterious effect. Alternatively, target genes identified from such samples based on an increase in expression following therapy are used to screen for compounds that can inhibit expression of the target gene and lessen the side effect.  
     [0030] In some embodiments of the invention, it is preferable but not essential to determine the pre-therapy gene expression level from a sample taken immediately preceding administration of therapy, although any sample taken after the onset of the condition and prior to therapy may be used. When performing the method with a cohort of patients whose differential expression is to be compared, samples should be taken at about the same time relative to therapy administration.  
     [0031] Determination of the post-therapy gene expression levels may be made from a sample taken at any time following treatment with the therapy. Samples will preferably be taken within one to thirty days of therapy administration. The optimum time for taking this sample is contemplated to vary depending on the selected condition, therapy used, and timing of additional confounding therapies. The preferred time may be determined by taking samples at various intervals of time following therapy (and before any additional confounding therapy is administered) and determining which sample provides the largest differential in expression relative to the pre-therapy sample. Accordingly, in some embodiments the sample is taken from the patient within one hour, within two hours, within four hours, within eight hours, within twelve hours, within eighteen hours, within twenty-four hours, within thirty-six hours, within forty-eight hours, within sixty hours, within seventy-two hours, or within ninety-six hours after treatment with the selected therapy. In other embodiments, the sample is taken from the patient within one week, within two weeks, within three weeks, within four weeks, within five weeks, within six weeks, within seven weeks, or within eight weeks after treatment. In still other embodiments, the sample is taken from the patient within two months, within three months, within four months, within six months, within eight months, within ten months, or within a year after treatment.  
     [0032] The expression profiles of the invention comprise one or more values representing the expression level of a gene that is differentially expressed before and after treatment of a selected condition with a selected therapy. By “differentially expressed” it is intended that the expression level of the gene changes significantly after treatment with the selected therapy in comparison with the expression level of the gene before the selected therapy. The expression level may be significantly increased after therapy or significantly decreased after therapy. By a “significant” change in expression level, it is intended a change in expression level that is statistically significant. A statistical test may be used to test whether a change in expression level measured for a gene after treatment is more likely to result from an actual change in the expression of the gene rather than from any variability present in the experimental system.  
     [0033] In an additional aspect of the invention, a patient&#39;s response to the subject therapy is also used as a factor in identifying candidate targets. In this aspect, a gene whose pre- vs. post-therapy change in expression is significantly different in patients who did not respond favorably to said therapy (i.e. unresponsive patients, e.g. patients who relapse) compared to patients who did respond favorably to the therapy (i.e. responsive patients) is identified, along with its expression products, as a screening target for drugs which may be used to improve treatment of said selected condition with said selected therapy. Thus, a gene whose expression is increased after therapy in patients who did not respond to therapy and is decreased or unchanged after therapy in responsive patients is identified as a screening target for drugs which can inhibit this increase and lessen the risk of nonresponsiveness to this therapy. Alternatively, a gene whose expression is decreased after therapy in nonresponsive patients and is increased or unchanged after therapy in responsive patients is identified as a screening target for drugs which can prevent this decrease. As yet another example, a gene whose expression is unchanged after therapy in nonresponsive patients and is increased or decreased after therapy in responsive patients is identified as a screening target for drugs which can cause this gene to respond in the same manner observed for responsive patients.  
     [0034] Thus, in some embodiments, the methods of the present invention encompass identifying genes whose expression levels are correlated with a particular treatment outcome or response to treatment with a selected therapy and expression profiles comprising these genes. For example, genes whose levels of expression are associated with a favorable or unfavorable response to a therapy in a patient, or with a deleterious effect of a therapy in a patient may be identified. By a “favorable response” to treatment, it is intended any mitigation or reduction of at least one of symptom associated with the condition to be treated. For example, in the case of cancer, any decrease in the number of cells showing the characteristics of cancer cells would be considered a favorable response to the treatment. By an “unfavorable response” to treatment, it is intended that the treatment does not mitigate or reduce any symptom of the condition. For example, in the case of cancer, an unfavorable response to treatment would include one in which the number of cells showing characteristics of cancer cells did not decrease.  
     [0035] By a gene whose expression level is “correlated with” a particular treatment outcome, it is intended a gene whose expression shows a statistically significant correlation with the treatment outcome. The significance of the correlation between the expression level of a differentially expressed gene and a particular physiologic state such as a favorable or unfavorable response to therapy may be determined by a statistical test of significance. Such methods are known in the art and examples are provided elsewhere herein. Methods for determining the strength of a correlation between the expression level of a differentially-expressed gene and a particular physiologic state are also reviewed in Holloway et al. (2002)  Nature Genetics Suppl.  32:481-89, Churchill (2002)  Nature Genetics Suppl.  32:490-95, Quackenbush (2002)  Nature Genetics Suppl.  32: 496-501; Slonim (2002)  Nature Genetics Suppl.  32:502-08; and Chuaqui et al. (2002)  Nature Genetics Suppl.  32:509-514; each of which is herein incorporated by reference in its entirety. Such methods may be used to select the genes whose expression levels have the greatest correlation with a particular treatment outcome in order to increase the predictive accuracy of the methods of the invention.  
     [0036] The expression profiles of the invention comprise values representing the absolute or the relative expression level of one or more differentially expressed genes. The expression levels of these genes may be determined by any method known in the art for assessing the expression level of an RNA or protein molecule in a sample. For example, expression levels of RNA may be monitored using a membrane blot (such as used in hybridization analysis such as Northern, Southern, dot, and the like), or microwells, sample tubes, gels, beads or fibers (or any solid support comprising bound nucleic acids). See U.S. Pat. Nos. 5,770,722, 5,874,219, 5,744,305, 5,677,195 and 5,445,934, which are expressly incorporated herein by reference. The gene expression monitoring system may also comprise nucleic acid probes in solution.  
     [0037] In one embodiment of the invention, microarrays are used to measure the values to be included in the expression profiles. Microarrays are particularly well suited for this purpose because of the reproducibility between different experiments. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of genes. Each array consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning. Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See, the Examples section. See also, U.S. Pat. Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860, and 6,344,316, which are incorporated herein by reference. High-density oligonucleotide arrays are particularly useful for determining the gene expression profile for a large number of RNA&#39;s in a sample.  
     [0038] Determination of the gene expression profile of a sample may be accomplished by any standard means available in the art. One standard way of simultaneously determining the expression profile of a multitude of genes is through the use of arrays. Arrays comprise capture probes for detecting the differentially expressed genes. By “array” is intended a solid support or substrate with peptide or nucleic acid probes attached to said support or substrate. Arrays typically comprise a plurality of different nucleic acid or peptide capture probes that are coupled to a surface of a substrate in different, known locations. These arrays, also described as “microarrays” or colloquially “chips” have been generally described in the art, for example, in U.S. Pat. Nos. 5,143,854, 5,445,934, 5,744,305, 5,677,195, 6,040,193, 5,424,186, 6,329,143, and 6,309,831 and Fodor et al.  Science  251:767-77 (1991), each of which is incorporated by reference in its entirety. These arrays may generally be produced using mechanical synthesis methods or light directed synthesis methods that incorporate a combination of photolithographic methods and solid phase synthesis methods.  
     [0039] Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261, incorporated herein by reference in its entirety for all purposes. Although a planar array surface is preferred, the array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be peptides or nucleic acids on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is hereby incorporated in its entirety for all purposes. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation of an all-inclusive device. See, for example, U.S. Pat. Nos. 5,856,174 and 5,922,591 herein incorporated by reference.  
     [0040] The arrays used to practice the methods of the present invention comprise capture probes that can specifically bind a nucleic acid molecule that is differentially expressed in pre-therapy patient samples vs. post-therapy patient samples, or a nucleic acid molecule that is differentially regulated after therapy in patients who relapse after a selected therapy compared to patients who respond favorably to the selected therapy. These arrays can be used to measure the expression levels of nucleic acid molecules to thereby create an expression profile for use in methods of identifying screening targets for drugs that can be used to improve the selected therapy.  
     [0041] In one approach, total mRNA isolated from the sample is converted to labeled cRNA and then hybridized to an oligonucleotide array. Each sample is hybridized to a separate array. Relative transcript levels may be calculated by reference to appropriate controls present on the array and in the sample. See, for example, the Examples.  
     [0042] In another embodiment, the values in the expression profile are obtained by measuring the abundance of the protein products of the differentially-expressed genes. The abundance of these protein products can be determined, for example, using antibodies specific for the protein products of the differentially-expressed genes. The term “antibody” as used herein refers to an immunoglobulin molecule or immunologically active portion thereof, i.e., an antigen-binding portion. Examples of immunologically active portions of immunoglobulin molecules include F(ab) and F(ab′)2 fragments which can be generated by treating the antibody with an enzyme such as pepsin. The antibody can be a polyclonal, monoclonal, recombinant, e.g., a chimeric or humanized, fully human, non-human, e.g., murine, or single chain antibody. In a preferred embodiment it has effector function and can fix complement. The antibody can be coupled to a toxin or imaging agent.  
     [0043] A full-length protein product from a differentially-expressed gene, or an antigenic peptide fragment of the protein product can be used as an immunogen. Preferred epitopes encompassed by the antigenic peptide are regions of the protein product of the differentially expressed gene that are located on the surface of the protein, e.g., hydrophilic regions, as well as regions with high antigenicity. The antibody can be used to detect the protein product of the differentially expressed gene in order to evaluate the abundance and pattern of expression of the protein. These antibodies can also be used diagnostically to monitor protein levels in tissue as part of a clinical testing procedure, e.g., to, for example, determine the efficacy of a given therapy.  
     [0044] Detection can be facilitated by coupling (i.e., physically linking) the antibody to a detectable substance (i.e., antibody labeling). Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, b-galactosidase, or acetylcholinesterase;  
     [0045] examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include  125 I,  131 I,  35 S or  3 H.  
     [0046] The present invention encompasses methods in which the expression level or expression profile for a patient are measured before and after treatment. The present invention also provides methods comparing the changes in pre- and post-treatment expression levels for populations of patients. Such populations of patients may comprise two or more patients. Methods are known in the art for comparing two or more data sets to detect similarity between them. To determine whether two or more gene expression levels, fold changes in gene expression or expression profiles show statistically significant similarity, statistical tests may be performed to determine whether any differences between the expression levels, fold changes in gene expression, or expression profile are likely to have been achieved by a random event. Methods for comparing gene expression profiles to determine whether they share statistically significant similarity are known in the art and also reviewed in Holloway et al. (2002)  Nature Genetics Suppl.  32:481-89, Churchill (2002)  Nature Genetics Suppl.  32:490-95, Quackenbush (2002)  Nature Genetics Suppl.  32: 496-501; Slonim (2002)  Nature Genetics Suppl.  32:502-08; and Chuaqui et al. (2002)  Nature Genetics Suppl.  32:509-514; each of which is herein incorporated by reference in its entirety.  
     [0047] Methods of Identifying Genes and Their Expression Products as Targets in Drug Screening.  
     [0048] The present invention demonstrates that patients affected by the same condition show different expression profiles in response to treatment with different therapeutic regimens. In addition, patients share common pathways of genomic response to the same treatment. Accordingly, the present invention provides methods for identifying one or more genes and their expression products as screening targets for drugs that may be used to treat a selected condition or to improve treatment of a selected condition with a selected therapy. The methods involve measuring gene expression levels of one or more genes in a subject affected by a condition of interest before and after treatment.  
     [0049] In some embodiments, the methods comprise the steps of:  
     [0050] 1. determining the expression level of one or more genes in a first sample from a subject affected by the selected condition prior to treatment with the selected therapy;  
     [0051] 2. determining the expression level of said one or more genes in a second sample from said subject following said treatment with the selected therapy; and  
     [0052] 3. for each of said one or more genes, comparing the expression level measured in step 1 with the expression level measured in step (2).  
     [0053] In the methods, a gene whose expression level is significantly increased or significantly decreased following treatment with the selected therapy is identified, along with its expression products, as a screening target for drugs which may be used to improve treatment of the selected condition with the selected therapy.  
     [0054] In some embodiments of the invention, pre- and post-therapy expression levels are measured in a population of patients. By a “population of patients” is intended one or more patient affected by the same conditions. The number of patients to be included in the population varies according to the selected condition and selected therapy. In some embodiments, it will be sufficient to compare pre-and post-therapy levels in a single patient in order to identify genes whose expression level changes after treatment with the therapy. In other embodiments, a larger population of patients may be used to increase the accuracy for identifying genes that are differentially expressed pre- and post-therapy. Accordingly, the population of patients comprises at least one patient, and may also comprise at least two patients, at least three patients, at least four patients, at least five patients, at least six patients, at least eight patients, at least ten patients, at least fifteen patients, at least twenty-five patients, at least fifty patients, at least one hundred patients, at least two hundred patients, and least three hundred patients, at least five hundred patients, at least one thousand patients, or at least ten thousand patients.  
     [0055] Thus, in some embodiments of the invention, the methods comprise the additional steps of repeating steps 1, 2, and 3 of the method recited above for each subject in a population of subjects affected by the selected condition and comparing the genes whose levels of expression are significantly increased or significantly decreased following treatment with the selected therapy for the subjects in the population of patients affected by the selected condition to thereby identify genes whose levels of expression are correlated with the selected therapy, where a gene whose expression level is correlated with the selected therapy is identified, along with its expression products, as a screening target for drugs which may be used to treat the selected condition or to improve treatment of the selected condition with the selected therapy. Accordingly, in some embodiments, the screening targets identified by the methods are used to identify drugs that can be used in combination with the selected therapy to improve the patient response to selected therapy, while in other embodiments, the screening targets are used to identify drugs that can replace the selected therapy (e.g., drugs that act down stream of the selected therapy) and can be used independently of the selected therapy to treat the condition.  
     [0056] In other embodiments of the invention, the methods comprise the additional steps of determining which subjects responded favorably to the selected therapy and which subjects did not respond favorably to the selected therapy; and comparing the genes showing a change in expression level following treatment with the selected therapy in subjects who responded favorably to the selected therapy and genes showing a change in expression level following treatment with the selected therapy in subjects who did not respond favorably to the selected therapy, to thereby identify genes whose expression level is correlated with a favorable response to the selected therapy. In accordance with the method, a gene whose expression level is correlated with favorable response in a patient to the selected therapy is identified, along with its expression products, as a screening target for drugs that may be used to improve treatment of the selected condition with the selected therapy.  
     [0057] The invention also provides methods for using expression profiles to identify genes and their expression products as screening targets for drugs that may be used to improve treatment of a selected condition with a selected therapy. The methods comprise the steps of:  
     [0058] 1. providing a first expression profile comprising values representing the expression levels of one or more genes from a first sample from a subject affected by the selected condition prior to treatment with the selected therapy;  
     [0059] 2. providing a second expression profile comprising values representing the expression levels of said one or more genes from a second sample from said subject, wherein said second sample is taken from said patient following treatment with the selected therapy;  
     [0060] 3. comparing the values comprised in the first expression profile with those comprised in the second expression profile;  
     [0061] According to the method, a gene whose expression level is significantly increased or significantly decreased following treatment with the therapy is identified, along with its expression products, as a screening target for drugs which may be used to improve treatment of the selected condition with the selected therapy.  
     [0062] The invention provides methods for identifying genes and their expression products as screening targets for inhibitors that may be used to treat a selected condition or to improve treatment of a selected condition with a selected therapy. The methods comprise determining expression levels of one or more genes before and after treatment with a selected therapy for a population of subjects to identify genes whose expression level is significantly increased following therapy, determining which subjects responded favorably to the selected therapy and which subjects did not respond favorably to the selected therapy; and comparing the genes whose expression level is significantly increased following treatment with the selected therapy in subjects who responded favorably to the selected therapy with the genes whose expression level is significantly increased following treatment with the selected therapy in subjects who did not respond favorably to the selected therapy, to thereby identify genes for which a significant increase in expression level following treatment with the selected therapy is correlated with a failure to respond favorably to the selected therapy. A gene whose expression level is correlated with an unfavorable response to the selected therapy is identified, along with its expression products, as a screening target for inhibitors that may be used to improve treatment of the selected condition with the selected therapy.  
     [0063] In other embodiments, the invention provides methods for identifying genes and their expression products as screening targets for mimics or activators that may be used to treat a selected condition or improve treatment of a selected condition with a selected therapy comprising. The methods comprise determining expression levels of one or more genes before and after treatment with a selected therapy for a population of subjects to identify genes whose expression level is decreased following treatment with the therapy, determining which subjects responded favorably to the selected therapy and which subjects did not respond favorably to the selected therapy; and comparing the genes whose expression level is significantly decreased following treatment with the selected therapy in subjects who responded favorably to the selected therapy with the genes whose expression level is significantly decreased following treatment with the selected therapy in subjects who did not respond favorably to the selected therapy, to thereby identify genes for which a significant decrease in expression level following treatment with the selected therapy is correlated with a failure to respond favorably to the selected therapy. A gene whose expression level is correlated with a failure to respond favorably to the selected therapy is identified, along with its expression products, as a screening target for mimics or activators which may be used to treat the selected condition or to improve treatment of the selected condition with the selected therapy.  
     [0064] In other embodiments, the invention provides methods for identifying genes and their expression products as screening targets for modulators that may be used to treat a selected condition or improve treatment of a selected condition with a selected therapy comprising. Such methods comprise determining expression levels of one or more genes before and after treatment with a selected therapy for a population of subjects to identify genes whose expression level is significantly changed after treatment, determining which patients responded favorably to the selected therapy and which subjects did not respond favorably to the selected therapy; and comparing the genes whose expression level is significantly changed following treatment with the selected therapy in subjects who responded favorably to the selected therapy with the genes whose expression level is significantly changed following treatment with the selected therapy in subjects who did not respond favorably to the selected therapy to thereby identify genes for which a significant change in expression level following treatment with the selected therapy is correlated with a failure to respond favorably to the selected therapy. According to the method, a gene whose expression level is significantly changed post-treatment in patients who responded favorably to the selected therapy but whose expression level did not significantly change post-treatment in patients who did not respond favorably to the selected therapy is identified, along with its expression products, as a screening target for modulators which may be used to improve treatment of the selected condition with the selected therapy.  
     [0065] In each method, pre-and post-treatment gene expression levels may be compared by determining the expression levels of one or more genes, or by comparing expression profiles derived from samples taken before and after treatment. The condition for which treatment is provided in the methods may be any condition, including, as non-limiting examples, cancers, genetic disorders, infectious diseases (including viral and bacterial infections), hematological disorders, cardiovascular diseases, dermatological diseases, endocrine diseases and gastrointestinal disorders. The samples from the subjects will typically comprise cells having differential gene expression pre- and post-therapy, for example cells that are affected by the condition being treated or the therapy being used.  
     [0066] Methods of Evaluating Therapies  
     [0067] It is the novel finding of the present invention that administration of a combination therapy comprising multiple therapeutic agents can alter the nature of cellular genomic response when compared with the response to any of the therapeutic agents given alone, and that this cellular genomic response is distinct from the sum of the individual therapeutic agents. Accordingly, the present invention provides methods for predicating the therapeutic efficacy and the likelihood for deleterious effects for therapies based on pre- and post-therapy gene expression levels. By “therapeutic efficacy” it is intended the ability of the therapy to alleviate (e.g., mitigate, decrease, reduce) at least one of the symptom associated with the condition to be treated. By “deleterious effects” of a therapy, it is intended any change in the physiologic state of the patient caused by the therapy that does not contribute to the therapeutic efficacy of the therapy.  
     [0068] In one embodiment, the invention provides a method for predicting whether a first therapy will have increased therapeutic efficacy in a patient in comparison with a second therapy. The method comprises the steps of:  
     [0069] 1) determining the expression level of one or more genes in a first sample from a subject affected by the selected condition prior to treatment with the first therapy, wherein increased or decreased expression of said one or more genes after treatment is correlated with a favorable response in a subject to treatment;  
     [0070] 2) determining the expression level of said one or more genes in a second sample from the subject of (1) following said treatment with the first therapy;  
     [0071] 3) for each of said one or more genes, comparing the expression level measured in step (1) with the expression level measured in step (2) to determine the change in the expression level of said genes following treatment with the selected therapy;  
     [0072] 4) repeating steps (1), (2), and (3) for each patient in a population of patients affected by the selected condition; and  
     [0073] 5) determining the expression level of said one or more genes in a first sample from a subject affected by the selected condition prior to treatment with the second therapy;  
     [0074] 6) determining the expression level of said one or more genes in a second sample from the subject of (5) following said treatment with the second therapy;  
     [0075] 7) for each of said one or more genes, comparing the expression level measured in step (5) with the expression level measured in step (6) to determine the change in the expression level of said genes following treatment with the selected therapy;  
     [0076] 8) repeating steps (5), (6), and (7) for each patient in a population of patients affected by the selected condition; and  
     [0077] 9) for each of said one or more genes, comparing the change in expression level following treatment with the first therapy with the change in expression level following treatment with the second therapy combination therapy to thereby determine whether the expression levels of the one or more genes show a greater increase in expression levels following treatment with the first therapy than following treatment with the second therapy. For genes for which increased expression following treatment is correlated with a favorable response in a subject to treatment, a greater increase in expression levels for one or more of the genes following treatment with the first therapy in comparison with the expression level for the one or more genes following treatment with the second therapy results in a prediction that the first therapy will have increased therapeutic efficacy in a patient in comparison with the second. For genes for which decreased expression following treatment is correlated with a favorable response in a subject to treatment, a greater decrease in expression levels for one or more of the genes following treatment with the first therapy in comparison with the expression level for the one or more genes following treatment with the second therapy results in a prediction that the first therapy will have increased therapeutic efficacy in a patient in comparison with the second therapy.  
     [0078] The genes whose expression levels are measured in the method may be any genes showing differential expression following treatment of the condition with any therapy. In some embodiments, a change in the expression of the genes following treatment is correlated with a favorable response following treatment with the first therapy. In other embodiments, a change in the expression of the genes following treatment is correlated with a favorable response following treatment with the second therapy. In still other embodiments, a change in the expression of the genes following treatment is correlated with a favorable response to treatment of in response to a therapy other than the first therapy or second therapy to be tested.  
     [0079] In another embodiment, the invention provides a method for predicting whether a first therapy will have increased deleterious effects in a patient in comparison with a second therapy. The method comprises the steps of:  
     [0080] 1) determining the expression level of one or more genes in a first sample from a subject affected by the selected condition prior to treatment with the first therapy, wherein increased or decreased expression of said one or more genes after treatment is correlated with deleterious effects in a subject to in response to treatment;  
     [0081] 2) determining the expression level of said one or more genes in a second sample from the subject of (1) following said treatment with the first therapy;  
     [0082] 3) for each of said one or more genes, comparing the expression level measured in step (1) with the expression level measured in step (2) to determine the change in the expression level of said genes following treatment with the selected therapy;  
     [0083] 4) repeating steps (1), (2), and (3) for each subject in a population of subjects affected by the selected condition; and  
     [0084] 5) determining the expression level of said one or more genes in a first sample from a subject affected by the selected condition prior to treatment with the second therapy;  
     [0085] 6) determining the expression level of said one or more genes in a second sample from the subject of (5) following said treatment with the second therapy;  
     [0086] 7) for each of said one or more genes, comparing the expression level measured in step (5) with the expression level measured in step (6) to determine the change in the expression level of said genes following treatment with the selected therapy;  
     [0087] 8) repeating steps (5), (6), and (7) for each subject in a population of subjects affected by the selected condition; and  
     [0088] 9) for each of said one or more genes, comparing the change in expression level following treatment with the first therapy with the change in expression level following treatment with the second therapy to thereby determine whether the expression levels of said one or more genes show a greater increase in expression levels following treatment with said first therapy than following treatment with the second therapy. For genes for which increased expression following treatment is correlated with a deleterious effects in a patient to treatment, a greater increase in expression levels for one or more of the genes following treatment with the first therapy in comparison with the expression level for the one or more genes following treatment with the second therapy results in a prediction that the first will have increased deleterious effects in a patient in comparison with the second therapy. For genes for which increased expression following treatment is correlated with deleterious effects in a patient to treatment, a greater decrease in expression levels for one or more of the genes following treatment with the first therapy in comparison with the expression level for the one or more genes following treatment with the second therapy results in a prediction that the first therapy will have increased deleterious effects in a patient in comparison with the second therapy  
     [0089] The genes whose expression levels are measured in the method may be any genes showing differential expression following treatment of the condition with the any therapy. In some embodiments, a change in the expression of the genes following treatment is correlated with deleterious effects following treatment with the first therapy. In other embodiments, a change in the expression of the genes following treatment is correlated with deleterious effects following treatment with the second therapy. In still other embodiments, a change in the expression of the genes following treatment is correlated with deleterious effects following treatment with a therapy other than the first therapy or second therapy to be tested.  
     [0090] In some embodiments of the methods of the invention, the genes for which increased or decreased expression after therapy is correlated with a favorable response in a patient to treatment with said a combination therapy are identified by a method comprising:  
     [0091] 1) determining the expression level of one or more genes in a first sample from a subject affected by from the selected condition prior to treatment with a first therapy;  
     [0092] 2) determining the expression level of said one or more genes in a second sample from said subject following said treatment with said first therapy;  
     [0093] 3) for each of said one or more genes, comparing the expression level measured in step (1) with the expression level measured in step (2) to identify genes whose expression level changed significantly following treatment with said first therapy;  
     [0094] 4) repeating steps (1)-(3) for each patient in a population of subjects affected by the selected condition;  
     [0095] 5) determining which subjects responded favorably to said first therapy and which subjects did not respond favorably to said first therapy; and  
     [0096] 6) comparing the genes whose expression level increased significantly or decreased significantly following treatment with said first therapy in subjects who responded favorably to said first therapy with the genes whose expression level did not change significantly following treatment with said first therapy in subjects who did not respond favorably to the said first therapy, to thereby identify genes for which an increase or decrease in expression following treatment with said first therapy is correlated with a favorable response in a subject to said first therapy.  
     [0097] In each method, pre-and post-treatment gene expression levels may be compared by determining the expression levels of one or more genes, or by comparing expression profiles derived from patient samples before and after treatment. The condition for which treatment is provided in the methods may be any condition, including, as non-limiting examples, cancers, genetic disorders, infectious diseases (including viral and bacterial infections), hematological disorders, cardiovascular diseases, dermatological diseases, endocrine diseases and gastrointestinal disorders. The samples from the subjects will typically comprise cells having differential gene expression pre- and post-therapy, for example cells that are affected by the condition being treated or the sample being used.  
     [0098] Methods of Screening for Drugs that Modulate Therapeutic Targets  
     [0099] The differentially expressed genes and their expression products identified as targets in accordance with the invention may be used in conventional biochemical assays or in cell-based screening assays. Johnston, P. A. and Johnston, P. A., “Cellular Platforms for HTS: three case studies”,  Drug Discovery Today  7(6): 353-363 (March 2002); Drews, J., “Drug discovery: a historical perspective”,  Science  287: 1960-1965 (2000); Valler, M. J. and Green, D., “Diversity screening versus focused screening in drug discovery”,  Drug Discovery Today  5(7): 286-293 (2000); Grepin, C. and Pernelle, C., “High-throughput screening”,  Drug Discovery Today  5(5): 212-214 (2000); “Recent patents in high-throughput screening”,  Nat. Biotechnol.  18(7): 797 (2000); White, R. E., “High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery”,  Ann. Rev. Pharmacol. Toxicol.  40: 133-157 (2000); Broach, J. R. and Thorner, J., “High-throughput screening for drug discovery”,  Nature  384 (Suppl): 14-16 (1996), Silverman, L. et al., “New assay technologies for high-throughput screening”,  Curr. Opin. Chem. Biol.  2:397-403 (1998). Such biochemical assays are based on the activity of the expression product and include standard kinase assays, phosphatase assays, binding assays, assays for apoptosis, hydroxylation, oxidation, conjugation and other enzyme reactions, and assays for protein-protein or protein-DNA or RNA interactions. Cell-based screening assays utilize recombinant host cells expressing the differentially expressed gene product. The recombinant host cells are screened to identify compounds that can activate the product of the differentially expressed gene or increase expression of the gene (i.e. agonists), or inactivate the product of the differentially expressed gene or decrease expression of the gene (i.e. antagonists).  
     [0100] Alternatively, a chimeric gene comprising the coding sequence for a reporter protein, such as green fluorescence protein or luciferase, placed under the regulatory of the promoter of a differentially expressed gene can be made. Such a chimeric gene can be used in a cell-based assay to screen for compounds that enhance or inhibit expression of the reporter gene through regulation of the promoter of the differentially expressed gene. Dhundale, A. and Goddard, C., “Reporter assays in the high throughput screening laboratory: a rapid and robust first look”,  J. Biomol. Screening  1:115-118 (1996); Goetz, A. S. et al., “Development of a facile method for high throughput screening with reporter gene assays”,  J. Biomol. Screening  5: 377-384 (2000).  
     [0101] Candidate compounds which may be screened for activity against targets identified by practice of the present invention include, for example, 1) peptides such as soluble peptides, including Ig-tailed fusion peptides and members of random peptide libraries (see, e.g., Lam et al. (1991)  Nature  354:82-84; Houghten et al. (1991)  Nature  354:84-86) and combinatorial chemistry-derived molecular libraries made of D- and/or L-configuration amino acids; 2) phosphopeptides (e.g., members of random and partially degenerate, directed phosphopeptide libraries, see, e.g., Songyang et al. (1993)  Cell  72:767-778); 3) antibodies (e.g., polyclonal, monoclonal, humanized, anti-idiotypic, chimeric, and single chain antibodies as well as Fab, F(ab′) 2 , Fab expression library fragments, and epitope-binding fragments of antibodies); 4) small organic and inorganic molecules (e.g., molecules obtained from combinatorial and natural product libraries; 5) zinc analogs; 6) leukotriene A 4  and derivatives; 7) classical aminopeptidase inhibitors and derivatives of such inhibitors, such as bestatin and arphamenine A and B and derivatives; 8) and artificial peptide substrates and other substrates, such as those disclosed herein above and derivatives thereof.  
     [0102] The compounds used for screening against targets identified in accordance with the present invention can be obtained using any of the numerous approaches in combinatorial library methods known in the art, including: biological libraries; spatially addressable parallel solid phase or solution phase libraries; synthetic library methods requiring deconvolution; the ‘one-bead one-compound’ library method; and synthetic library methods using affinity chromatography selection. The biological library approach is limited to polypeptide libraries, while the other four approaches are applicable to polypeptide, non-peptide oligomer or small molecule libraries of compounds (Lam (1997)  Anticancer Drug Des.  12:145).  
     [0103] Examples of methods for the synthesis of molecular libraries can be found in the art, for example in DeWitt et al. (1993)  Proc. Natl. Acad. Sci. USA  90:6909; Erb et al. (1994)  Proc. Natl. Acad. Sci. USA  91:11422; Zuckermann et al. (1994).  J. Med. Chem.  37:2678; Cho et al. (1993)  Science  261:1303; Carell et al. (1994)  Angew. Chem. Int. Ed. Engl.  33:2059; Carell et al. (1994)  Angew. Chem. Int. Ed. Engl.  33:2061; and in Gallop et al. (1994)  J. Med. Chem.  37:1233. Libraries of compounds may be presented in solution (e.g., Houghten (1992)  Biotechniques  13:412-421), or on beads (Lam (1991)  Nature  354:82-84), chips (Fodor (1993)  Nature  364:555-556), bacteria (U.S. Pat. No. 5,223,409), spores (U.S. Pat. No. 5,223,409), plasmids (Cull et al. (1992)  Proc. Natl. Acad. Sci. USA  89:1865-1869) or on phage (Scott and Smith (1990)  Science  249:386-390); (Devlin (1990)  Science  249:404-406); (Cwirla et al. (1990)  Proc. Natl. Acad. Sci. U.S.A.  97:6378-6382); (Felici (1991)  J. Mol. Biol.  222:301-310).  
     [0104] Modulators of the activity of a product of a differentially expressed gene identified according to the drug screening assays provided above can be used to improve treatment of a selected condition. These methods of treatment include the steps of administering the modulators of the activity of a product of a differentially-expressed gene in a pharmaceutical composition as described herein, in combination with the selected therapy, to a subject in need of such treatment.  
     EXAMPLES  
     [0105] The following examples are offered by way of illustration and not by way of limitation.  
     Example 1  
     Treatment-Specific Changes in Gene Expression in Primary Leukemia Cells, In Vivo, During Initial Therapy for Acute Lymphoblastic Leukemia (ALL)  
     SUMMARY  
     [0106] To elucidate genomic determinants of leukemia response to chemotherapy, oligonucleotide microarrays (Affymetrix® HG-U95A GeneChip) were used to analyze expression of approximately 9,600 human genes in bone marrow leukemic blasts obtained from children with ALL, at diagnosis and one day post-treatment with mercaptopurine (1 gm/ 2  IV) or methotrexate (MTX) given alone (1 gm/m 2  IV), or mercaptopurine (6-MP) in combination with either low-dose MTX [180 mg/m 2  orally] or high-dose MTX [1.0 mg/m 2  IV]). A stratified (immunophenotype, DNA ploidy) randomization was used to assign treatment, and the fold-change in gene expression (post-treatment to diagnosis) was computed for 60 patients. Using linear discriminate analysis with variance (LDAV) genes that most discriminated among treatments were selected based on expression changes (fold-change from diagnosis to post-treatment) or based on post-treatment expression levels alone. There were distinct expression profiles that discriminated among all treatments, using either the fold-change or the post-treatment expression patterns, although the change in gene expression discriminated significantly better among treatments. Leave-one-out cross-validation using support-vector-machine (SVM), based on the 120 most discriminating genes, correctly classified 60 out of 60 patients (100%) based on fold-change versus 58 out of 60 (96.7%) using only post-treatment expression profiles. The smallest number of genes for discrimination among treatments was 120 using fold-change, which included genes involved in cellular processes such as apoptosis, cell cycle control and stress response. Together, these in vivo data reveal unique, treatment-specific changes in gene expression in primary leukemia cells, establishing that changes in expression differ according to the specific medication, dosage and combination given. These findings provide new insights to cancer cell responses to chemotherapy and can be used to illuminate mechanisms of leukemia resistance and identify novel targets to augment existing treatment modalities.  
     Methods  
     [0107] Primary leukemia cells. This study included 60 patients with ALL enrolled on St. Jude Children&#39;s Research Hospital Total Therapy Studies XIIIB and XV. Bone marrow samples were obtained at diagnosis (pre-treatment) and one day post-treatment with mercaptopurine (6-MP) or methotrexate (MTX) given alone, or mercaptopurine in combination with either low-dose MTX (LDMTX/6-MP) or high-dose MTX (HDMTX/6-MP). A stratified (immunophenotype, DNA ploidy) randomization was used to assign treatment. Total RNA was extracted from cryopreserved mononuclear cell suspensions with TriReagent (MRC, Cincinnati, Ohio).  
     [0108] Mircoarray analysis. High quality RNA was hybridized to Affymetrix HG-U95A GeneChipe (12,600 probe sets, ˜9,600 human genes) according to the manufacturers protocol (Affymetrix, Santa Clara, Calif.). Scaled gene expression values for pre-treatment, post-treatment and fold-change (post-treatment vs. pre-treatment ratio) were calculated using Affymetrix Microarray Suite® (MAS) 5.0.  
     [0109] Gene expression data analysis. Analysis was done on fold-change and on post-treatment expression. The data were log-transformed and probe sets were filtered out if “absent” in all 120 arrays or if “no change” in all 60 fold-change ratios. Principal component analysis (PCA) and 2D-hierarchical clustering was performed using GeneMath 1.5 (AppliedMaths, Belgium). We applied supervised methods to find the most discriminating genes, including Linear Discriminant Analysis with Variance (LDAV) (GeneMaths) and ANOVA. Probe sets were ranked according to their discriminating power. To establish that these genes could classify treatments and to find significant genes, leave-one-out cross-validation was performed by support vector machine (SVM) with the top ranked probe sets. To demonstrate the above selected genes were not obtained by chance, a permutation test was performed in which each patient was assigned randomly to one of the four treatments groups, and the same procedure was followed to select genes and perform cross-validation. The p-value is defined as the probability of observing a misclassification rate less or equal to that in the experimental data. 250 permutations were performed. To distinguish one treatment from the other treatments, distinction calculation (Spotfire 6.3, Somerville, Mass.) was performed for each probe set. Permutations (n=1000) were performed to obtain the p-values. Among probe sets with p-values &lt;0.01, those with the largest distinction values were selected.  
     [0110] Treatment Regimen and bone marrow sampling time. Bone marrow samples were obtained at diagnosis (pre-treatment) and one day post-treatment with mercaptopurine (6-MP) or methotrexate (MTX) given alone, or mercaptopurine in combination with either low-dose MTX (LDMTX/6-MP) or high-dose MTX (HDMTX/6-MP). After total RNA extraction, samples were processed according to Affymetrix protocol. Fold-change as well as expression values for each gene in each patient were computed. A schematic of this process is shown in FIG. 1A and FIG. 1B.  
     [0111] Patient characteristics. A total of 60 patients were analyzed. No difference was found in terms of gene expression in this study between HDMTX (infusion for 24 h) treatment and HDMTX (infusion for 4 h) treatment. Therefore data from these patients was pooled together.  
     [0112] Unsupervised clustering of 60 ALL samples and PCA using all genes. About 8222 genes (fold-change) and 8002 (post-treatment only) were used for hierarchical clustering. Post-treatment samples clustered by lineage, ploidy and molecular subtypes.  
     [0113] Leave-one-out cross-validation results. SVMs were constructed using top ranked genes. Leave-one-out cross-validation showed that classification error rate decreased as the number of genes used to make the classification increased. Using the 120 genes showing the greatest fold-change in gene expression, all patients were correctly assigned to their corresponding treatment group by this analysis. Selected top 160 genes for post-treatment only, correctly assigned 58 out of 60, the latter indicating that in some cases the changes in gene expression is more informative than just the post-treatment expression profile.  
     [0114] 2D-Hierarchical clustering. Using the 120 most discriminating genes based on fold-change observed between pre-treatment and post-treatment expression, a 2-dimensional hierarchical cluster of genes whose change in expression was associated with a particular treatment was created. This exercise identified genes whose change in expression (either up or down) was characteristic of a particular treatment and could be used to determine which treatment had been administered to a particular patient.  
     [0115] Clustering of 60 ALL samples with most discriminating genes only. Three dimensional hierarchical clustering was performed using expression data from A) 120 genes (fold-change) and 160 genes (post-treatment only). Both analyses resulted in clustering of patients according to the treatment they were given, with only one sample being misclassified by this process. Differences between the four treatment groups was more evident from the comparison of fold-change in pre and post treatment gene expression than for post-treatment gene expression alone.  
     [0116] Distinction calculation results. To distinguish one treatment from the other treatments, distinction calculation values were computed. The ten genes with the highest distinction values for both directions (five up-regulated and five down-regulated) for each treatment are shown in the table below. These genes and their expression products represent screening targets that may be used to synergize or enhance the effects of the therapy they are associated with.  
               TABLE 1                          Top 40 Discriminating Genes                         Treatment   GenBank   Name               HDMTX   W72424   S100 calcium binding protein A9           AF004230   Leukocyte immunoglobulin-like receptor           AL036554   Defensin, alpha 3, neutrophil-specific           AI126134   S100 calcium binding protein A8           AA151971   CDNA clone = IMAGE-588365           AB007939   KIAA0470 gene product           AB024327   unr-interacting protein           L42542   ralA binding protein 1           D64109   Transducer of ERBB2, 2           X89750   TGFB-induced factor       HDMTX/   D88532   Phosphoinositide-3-kinase       6-MP   L20826   Plastin 1 (I isoform)           AF003001   Telomeric repeat binding factor           U93867   Polymerase (RNA) III (DNA directed) (62kD)           U46116   Protein tyrosine phosphatase, receptor type.               G           U66469   cell growth regulatory with ring finger domain           U51698   Apoptosis antagonizing transcription factor           Z99716   Septin 3           AI701164   Ubiquitin-conjugating enzyme E2G 1           AJ006068   DTDP-D-glucose 4,6-dehydratase       LDMTX/   Y15801   protein kinase, Y-linked       6-MP   AB014582   KIAA0682 gene product           L13689   murine leukemia viral (bmi-1) oncogene           U65416   MHC class I polypeptide-related sequence B           M77698   YY1 transcription factor           X66358   cyclin-dependent kinase-like 1           X12451   cathepsin L           W28760   cDNA/gb = W28760           AF054177   chromodomain helicase DNA binding protein           M34379   elastase 2, neutrophil       6MP   L36720   bystin-like           AF051941   nucleoside diphosphate kinase type 6           X59303   valyl-tRNA synthetase 2           AA149307   hypothetical protein FLJ21174           L27071   TXK tyrosine kinase           AA005018   CGI-49 protein           X54486   serine (or cysteine) proteinase inhibitor           M28393   perforin 1 (pore forming protein)           U34683   glutathione synthetase           U72066   retinoblastoma binding protein 8                                          
 
     [0117] Conclusion. Together, these in vivo data reveal unique, treatment-specific changes in gene expression in primary leukemia cells, establishing that changes in expression differ according to the specific medication, dosage and combination given. These findings provide new insights to cancer cell responses to chemotherapy, illuminate mechanisms of leukemia resistance and identify novel targets to augment existing treatment modalities.  
     Example 2  
     Treatment-Specific Changes in Gene Expression in Primary Leukemia Cells, In Vivo, During Initial Therapy for Acute Lymphoblastic Leukemia (ALL) Associated With Relapse  
     [0118] Gene Expression data from Example 1 was further analyzed according to which patients responded favorably to therapy and which patients suffered from a relapse following therapy. Based on this analyses, genes were identified whose expression was down regulated after therapy administration in patients which subsequently suffered a relapse relative to patients which responded favorably to therapy. These genes are identified in Table 2A below. In accordance with the teachings of the present invention, these genes are identified as targets to screen for drugs that can increase their expression or increase the activity of their expression products. Such drugs could be used to improve the subject ALL therapy.  
               TABLE 2A                          Genes down-regulated in relapse patients                     GenBank           Accession#   Gene Name               AF081287   CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) phos.       U33203   Mdm2, transformed 3T3 cell double minute 2, p53 binding protein       (mouse)       D28532   solute carrier family 17 (sodium phosphate), member 1       AF052182   DHHC1 protein       M35878   insulin-like growth factor binding protein 3       AF023466   glycine-N-acyltransferase       W72239       W28255   gamma tubulin ring complex protein (76p gene)       U44755   small nuclear RNA activating complex, polypeptide 2,45kD       M14502   arginase, liver       D87436   lipin 2       S80267   spleen tyrosine kinase       M15169   adrenergic, beta-2, receptor, surface       X66397   translocated promoter region (to activated MET oncogene)       AB012293   lymphocyte antigen 6 complex, locus H       W37606   HCF-binding transcription factor Zhangfei       AF056490   phosphodiesterase 8A       AF070617       D30655   eukaryotic translation initiation factor 4A, isoform 2       AJ001019   ring finger protein 3       X83300   SMA4       AJ007292   ephrin-A2       D87012   topoisomerase (DNA) III beta       X52151   arylsulfatase A       S66427   retinoblastoma binding protein 1       D83407   Down syndrome critical region gene 1-like 1       M90360   A kinase (PRKA) anchor protein 13       AL050372       X95152   breast cancer 2, early onset       Y13492   smoothelin       AA151922   APG12 autophagy 12-like ( S.cerevisiae )       AI560890       U29656   non-metastatic cells 3, protein expressed in       AF026029   poly(A) binding protein, nuclear 1       U69883   potassium intermediate/small conductance calcium-activated channel.su.       AF006513   chromodomain helicase DNA binding protein 1       AB029032   KIAA1109 protein       U12779   mitogen-activated protein kinase-activated protein kinase 2       N29665   KIAA0618 gene product       Z29630   spleen tyrosine kinase       W22296   protein kinase C binding protein 1       U34994   protein kinase, DNA-activated, catalytic polypeptide       AB003791   carbohydrate (keratan sulfate Gal-6) sulfotransferase 1       AF094521   Cdc42 effector protein 3       AB026190   Kelch motif containing protein       AF000430   dynamin 1-like       L12723   heat shock 70kD protein 4       X60592   tumor necrosis factor receptor superfamily, member 5       AL049787   hypothetical gene CG018       AF052169                  
 
     [0119] This analysis also revealed genes whose expression was up-regulated after therapy administration in patients which subsequently suffered a relapse relative to patients which responded favorably to therapy. These genes are identified in Table 2B below. In accordance with the teachings of the present invention, these genes are identified as targets to screen for drugs which can decrease their expression or decrease the activity of their expression products. Such drugs could be used to improve the subject ALL therapy.  
               TABLE 2B                          Genes up-regulated in relapse patients                     GenBank           Accession #   Gene Name               M34276           AL050162   testis derived transcript (3 LIM domains)       M31516   decay accelerating factor for complement (CD55, Cromer blood group       sys.       AI004207   hypothetical protein FLJ00002       AF108145   MYLE protein       AF070554       X78710   metal-regulatory transcription factor 1       W25984   Hypothetical protein TCBAP0758       AL050064   hypothetical protein FLJ11220       U67615   Chediak-Higashi syndrome 1       AB007864   KIAA0404 protein       X00734   tubulin, beta, 5       AJ222801   sphingomyelin phosphodiesterase 2, neutral membrane (neutral sphingo.       AF038179   hypothetical protein FLJ11191       U77664   ribonuclease P (38kD)       X94630   CD97 antigen       U20982   insulin-like growth factor binding protein 4       AI535828   jumping translocation breakpoint       U40992   DnaJ (Hsp40) homolog, subfamily B, member 4       Y15908   diaphanous homolog 2 (Drosophila)       AF023456   protein phosphatase, EF hand calcium-binding domain 2       D50915   KIAA0125 gene product       AF032862   hyaluronan-mediated motility receptor (RHAMM)       AJ243274   Kruppel-like factor 12       L13698   growth arrest-specific 1       L40401   peroxisomal long-chain acyl-coA thioesterase       AL049929   ATPase, H+ transporting, lysosomal (vacuolar proton pump)           membranes.       U35146   cyclin-dependent kinase-like 2 (CDC2-related kinase)       AL046940       U43842   bone morphogenetic protein 4       AF070524       AJ010841   thioredoxin-like 2       D45132   PR domain containing 2, with ZNF domain       AB028995   KIAA1072 protein       D14889   RAB33A, member RAS oncogene family       M16942   major histocompatibility complex, class II, DR beta 4       W27944   Wnt inhibitory factor-1       M18728   carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific.       X82209   meningioma (disrupted in balanced translocation)1       AF023462   phytanoyl-CoA hydroxylase (Refsum disease)       U11821   tumor necrosis factor (ligand) superfamily, member 6       Y00816   complement component (3b/4b) receptor 1 including Knops blood group.       X53004   glycophorin E                  
 
     Example 3  
     Changes in Gene Expression After Combination Therapy Were Not the Composite of Each Agent Given Alone.  
     [0120] To determine whether changes in gene expression differed when HDMTX or MP were given alone versus in combination, we compared genes that changed expression (by &gt;50%) in over 70% of patients after single agent and combination treatment. In over 70% of patients treated with HDMTX, MP, or HDMTX+MP, 97, 197 and 173 genes changed expression by at least 50%. However, only seven (11.9%) of 59 genes that were down-regulated after HDMTX alone were also down-regulated when HDMTX was given with MP, and only eight (21.1%) of 38 genes that were up-regulated after HDMTX alone also increased after HDMTX+MP. Similarly, only 18 (11.4%) of 158 genes that increased after MP alone also increased after MP+HDMTX, and only seven (17.5%) of 40 genes that were down-regulated after MP alone were also down-regulated after MP+HDMTX. Overall, only 40 of 295 genes (13.6%) that changed after HDMTX alone or MP alone (sum of the two groups) also changed after the combination of HDMTX+MP (Tables 3A and 3B). Among the 295 genes that changed significantly after 6 MP alone or HDMTX alone, the overall magnitude of change in-expression was significantly less after the combination of MP+HDMTX (P&lt;0.001, paired t-test).  
               TABLE 3A                          Genes That Concordantly Change after treatment with HDMTX       Alone and After Treatment with HDMTX-MP*                                             Median   Median                   FC   FC                   HDMT   HDMTX/       Probe set ID   Identifier   Gene name   X**   MP**                                         36161_at   M34175   adaptor-related protein complex 2, beta 1 subunit   5.7   3.6       37277_at   U80017   baculoviral IAP repeat-containing 1   2.6   2.4       38819_at   U33635   PTK7 protein tyrosine kinase 7   2.5   2.4       36651_at   X15525   acid phosphatase 2, lysosomal   2.5   1.9       40123_at   D87435   golgi-specific brefeldin A resistance factor 1   2.2   2.0       34279_at   AL050141   hypothetical protein FLJ20719   2.0   2.5       32125_at   AA928996   Tho2   1.9   3.0       38464_at   X87237   glucosidase I   1.9   3.2       36432_at   AL079298   methylcrotonoyl-Coenzyme A carboxylase 2 (beta)   −1.8   −2.0       35074_at   AF004715   jerky homolog-like (mouse)   −1.9   −2.5       36246_at   Z35309   adenylate cyclase 8 (brain)   −2.2   −2.8       32413_at   M13934       −2.8   −2.4       32583_at   J04111   v-jun sarcoma virus 17 oncogene homolog (avian)   −3.5   −2.3       725_i_at   J03071       −4.0   −5.5       1915_s_at   V01512       −5.7   −2.4                                  
 
     [0121]               TABLE 3B                          Genes that Concordantly Change After MP Alone And After       HDMTX + MP*                                                 Median                   Median   FC                   FC   HDMTX/       Probe set ID   Identifier   Gene Name   MP**   MP**                                         36161_at   M34175   adaptor-related protein complex 2, beta 1 subunit   4.0   3.6       32125_at   AA928996   Tho2   2.5   3.0       35436_at   L06147   golgi autoantigen, golgin subfamily a, 2   2.1   2.5       34836_at   U18420   RAB5C, member RAS oncogene family   2.0   1.9       36822_at   U51334   TAF15 RNA polymerase II, TATA box binding protein   2.0   1.9       37277_at   U80017   baculoviral IAP repeat-containing 1   2.0   2.4       38915_at   AB011135   KIAA0563 gene product   2.0   1.9       31652_at   AB023217   KIAA1000 protein   −1.9   −2.2       41117_s_at   AB016243   solute carrier family 9 (sodium/hydrogen exchanger)   −2.0   −1.7       38146_at   AB011107   zinc finger protein 387   −2.1   −3.5       940_g_at   D12625   neurofibromin 1   −2.1   −1.7       31785_f_at   U92817   unnamed HERV-H protein   −2.3   −2.5       34702_f_at   M27826   chorionic somatomammotropin hormone 2   −2.3   −2.9       41303_r_at   AI378632   Homo sapiens mRNA; cDNA DKFZp564P233   −2.5   −3.6       450_g_at   U66469   cell growth regulatory with ring finger domain   −2.5   −1.9       40590_at   AA166687   cell division cycle 27   −2.8   −7.7       31529_at   X99141   keratin, hair, basic, 3   −3.0   −2.1       39407_at   M22488   bone morphogenetic protein 1   −3.0   −4.3       33047_at   AI971169   ESTs, Highly similar to BCL2-like 11   −3.2   −2.1       34704_r_at   AA151971   chorionic somatomammotropin hormone 2   −3.5   −3.0       40387_at   U80811   endothelial differentiation,G-protein-coupled receptor   −3.5   −3.7       32583_at   J04111   v-jun sarcoma virus 17 oncogene homolog (avian)   −3.7   −2.3       39586_at   AF097935   desmoglein 1   −4.0   −3.0       1915_s_at   V01512       −4.9   −2.4       725_i_at   J03071       −9.2   −5.5                                    
     Example 3  
     Human Leukemia Cell Lines Differ From Primary Leukemia Cells in Response to Therapy  
     [0122] When the treatments with HDMTX alone (12 nM×24 hr plus 18 hr drug-free media) or MP alone (10 μM×24 hr) were recapitulated with two human ALL cell lines in vitro (i.e., B-lineage Nalm6 [N.MTX] and T-lineage CEM [C.MTX}), very little overlap was found in the genes that changed by &gt;50% after treatment in the cell lines compared to the primary leukemia cells in patients. Specifically, only seven out of the 97 genes (7.2%) that changed by &gt;50% in at least 70% of patients after HDMTX also changed in the cell lines. Similarly, only 27 of the 197 genes (13.7%) changed in a consistent manner after MP treatment of cell lines and primary cells in vivo (see Supplemental Table 4A for list of genes).  
               TABLE 4A                          Genes that Concordantly Change After HDMTX in Cell lines and in       Patients, in vivo *                                     (1)           Median   Median   Median       Probe set           FC   FC   FC       ID   Identifier   Gene name   N.MTX**   C.MTX**   HDMTX**                                             32264_at   L23134   granzyme M (lymphocyte met-ase 1)   1.9   1.6   2.9       36591_at   X06956   tubulin, alpha 1 (testis specific)   1.9   1.2   1.9       33143_s_at   U81800   solute carrier family 16 (monocarboxylic   1.5   1.7   3.6               acid transporters),       2067_f_at   L22475   BCL2-associated X protein   7.0   1.0   3.7       2001_g_at   U26455   ataxia telangiectasia mutated   2.1   1.6   2.5       35692_at   AL0802   Ras-induced senescence 1   −2.6   −1.0   −2.5           35       1916_s_at   V01512   v-fos FBJ murine osteosarcoma viral   −1.4   −1.9   −11.3               oncogene homolog                                  
 
     [0123] When the treatment with MP alone (10 μM×24 hr.) were recapitulated with two human ALL cell lines in vitro (i.e., B-lineage Nalm6 [N.MP] and T-lineage CEM [C.MP}), very little overlap was found in the genes that changed after treatment in the cell lines compared to the primary leukemia cells in patients. The genes that concordantly change after HDMTX in cell lines and in patients are listed in Table 4B.  
               TABLE 4B                          Genes that Concordantly Change after HDMTX in cell lines and in       patients*                                                 Median   Median   Median       Probe set           FC   FC   FC       ID   Identifier   Gene name   N.MP**   C.MP**   MPII**                                             37881_at   AF1009   growth differentiation factor 11   2.6   3.7   2.1           07       38547_at   Y00796   integrin, alpha L (antigen CD11A (p180),   4.9   −1.4   1.7               lymphocyte function       39286_at   D64109   transducer of ERBB2, 2   1.9   3.0   3.0       40329_at   AL0312   ring finger protein 1   1.3   3.2   1.7           28       41743_i_at   AF0610   tumor necrosis factor alpha-inducible cellular   1.7   1.5   2.3           34   protein       34335_at   AI76553   ephrin-B2   1.6   1.5   1.7           3       34818_at   X96381   ets variant gene 5 (ets-related molecule)   2.5   1.5   2.0       40951_at   AL0492       1.7   1.7   1.9           50       292_s_at   L29219   CDC-like kinase 1   1.1   2.5   1.7       31777_at   AF0064   muscle, skeletal, receptor tyrosine kinase   1.2   −5.7   −2.6           64       33069_f_at   U06641   UDP glycosyltransferase 2 family,   −2.5   1.1   −1.7               polypeptide B15       34068_f_at   X86174   synovial sarcoma, X breakpoint 1   −1.1   −2.5   −2.8       35081_at   D14838   fibroblast growth factor 9 (glia-activating   −1.0   −4.9   −2.3               factor)       35109_at   AB0182   KIAA0756 protein   −5.3   −1.0   −2.6           99       37871_at   X68830   islet amyloid polypeptide   −3.0   −0.9   −2.0       40322_at   D12763   interleukin 1 receptor-like 1   −1.1   −4.6   −2.1       40387_at   U80811   endothelial differentiation, lysophosphatidic   −4.3   1.4   −3.5               acid       32083_at   AF0278   transmembrane 7 superfamily member 1   −1.0   −10.6   −3.2           26   (upregulated in kidney)       35178_at   W27944   Wnt inhibitory factor-1   −6.5   1.1   −5.3       39407_at   M22488   bone morphogenetic protein 1   −1.2   −3.7   −3.0       32834_r_at   AF0135   sudD (suppressor of bimD6, Aspergillus   −3.7   −1.1   −3.0           91   nidulans) homolog       39448_r_at   W27095   B7 protein   −1.9   −1.3   −1.6       41244_f_at   X80910   protein phosphatase 1, catalytic subunit, beta   −2.6   −1.7   −2.1               isoform       32531_at   X52947   gap junction protein, alpha 1, 43kD (connexin   −1.2   −5.7   −3.2               43)       32583_at   J04111   v-jun sarcoma virus 17 oncogene homolog   2.0   −16.0   −3.7               (avian)       1152_i_at   J00117   chorionic gonadotropin, beta polypeptide   −2.8   −2.3   −6.1       618_at   M26167   platelet factor 4 variant 1   −2.1   −4.3   −4.0                                  
 
     Example 5  
     Genes That Discriminated Treatment Response  
     [0124] The relation between changes in gene expression after treatment and clinical outcome was assessed in patients treated with LDMTX plus MP, because this was the largest group with sufficiently long clinical follow-up (median: 3.7 years, range: 2.9-6.4 years for those who remained in remission). Using a Cox proportional hazard regression model, with lineage as a covariate, 146 gene probe sets that were related to relapse (Table 5; P&lt;0.05) were identified. Permutation analysis indicated that the smallest P-value was achieved with 87 probe sets (P=0.028), although statistical significance for discriminating outcome was achieved using 75 to 146 probe sets. Hierarchical clustering using the six genes with the highest discriminating power (the first six genes shown in the table) clearly separated the five patients who relapsed from the 11 patients who remain in complete remission.  
               TABLE 5                          Genes significantly correlated to treatment outcome as identified by Cox       proportional hazard regression analysis*                                                 Median FC   Median FC                   Median FC   Relapse   Relapse   Weight       Identifier   Gene Name   CCR**   B-lineage   T-lineage   by LDA                                             AF070554   clone 24582 mRNA   −2.3   2.9   1.7   0.295       X94630   CD97 antigen   −1.1   1.6   1.5   0.207       AB003791   carbohydrate (keratan sulfate Gal-6) sulfotransferase 1   −1.2   −2.9   −4.3   0.193       W72239   clone = IMAGE-345279   1.3   −1.2   1.0   0.186       U20982   insulin-like growth factor binding protein 4   −1.1   1.6   2.8   0.175       M15169   adrenergic, beta-2-, receptor, surface   1.1   −3.1   −4.3   0.167       U77664   ribonuclease P (38kD)   −1.4   1.6   1.5   0.149       AL050064   hypothetical protein FLJ11220   1.1   1.6   1.7   0.144       AF023466   glycine-N-acyltransferase   1.7   −5.5   −2.6   0.144       AB026190   Kelch motif containing protein   1.1   −3.6   −2.1   0.136       X00734   tubulin, beta, 5   −1.2   2.0   1.3   0.134       L35546   glutamate-cysteine ligase, modifier subunit   1.7   −2.6   1.1   0.134       X96586   neutral sphingomyelinase (N-SMase) activation   1.5   −1.1   1.1   0.127           associated factor       X76057   mannose phosphate isomerase   −1.2   2.2   1.2   0.125       AB016194   ELK1, member of ETS oncogene family   −1.6   1.7   −1.1   0.124       W27466   heterogeneous nuclear ribonucleoprotein D-like   −1.2   −3.6   −2.0   0.123       AJ131186   nuclear matrix protein NMP200 related to splicing factor   1.1   −3.0   −1.6   0.123           PRP19       AF038187   CS box-containing WD protein   1.0   2.4   1.6   0.120       AF045229   regulator of G-protein signalling 10   −1.1   1.9   1.5   0.120       M29551   protein phosphatase 3, catalytic subunit, beta isoform   1.1   1.5   1.2   0.114       AL050289   chromosome 6 open reading frame 5   1.1   −1.1   −1.1   0.113       Z46376   hexokinase 2   1.6   −7.2   −1.3   0.111       AF052159   clone 24416 mRNA   −1.1   3.0   1.3   0.111       L36983   dynamin 2   1.2   −1.4   1.2   0.108       AF011468   serine/threonine kinase 15   −1.1   −13.5   −1.4   0.104       U41303   small nuclear ribonucleoprotein polypeptide N   1.1   −1.5   1.1   0.103       M34641   fibroblast growth factor receptor 1 (fms-related tyrosine   −1.1   −1.9   −1.4   0.103           kinase 2)       M60278   diphtheria toxin receptor (epidermal growth factor-like   1.2   −5.3   1.3   0.100           growth factor)       AF010313   etoposide-induced mRNA   1.6   −1.1   1.4   0.100       J02871   cytochrome P450, subfamily IVB, polypeptide 1   −1.6   1.1   −1.1   0.100       AF030227   vav 1 oncogene   1.0   1.7   1.1   0.097       L02547   cleavage stimulation factor, 3′ pre-RNA, subunit 1,   1.3   −2.2   1.1   0.097           50kD       L01042   TATA element modulatory factor 1   −1.2   4.8   1.3   0.097       AF049910   transforming, acidic coiled-coil containing protein 1   1.5   1.2   1.3   0.096       AF054185   proteasome (prosome, macropain) subunit   1.0   −3.5   −1.2   0.095       Y11392   chromosome 21 open reading frame 2   −1.7   1.2   −1.1   0.094       M30894   T cell receptor gamma locus   1.4   −7.0   1.2   0.094       M60974   growth arrest and DNA-damage-inducible, alpha   1.4   −3.6   1.4   0.091       X16901   general transcription factor IIF, polypeptide 2   1.2   3.7   1.3   0.091       L37936   Ts translation elongation factor, mitochondrial   −1.2   1.5   −1.1   0.090       L39211   carnitine palmitoyltransferase I, liver   1.0   3.0   1.9   0.089       AJ010842   XPA binding protein 1; putative ATP(GTP)-binding   −1.1   −2.0   −1.1   0.087           protein       U46461   disheveled, dsh homolog 1 (Drosophila)   −1.5   −5.3   −1.4   0.085       AL080062   DKFZP564I122 protein   −1.2   −4.8   −1.4   0.084       D11466   phosphatidylinositol glycan   −1.2   −1.7   −1.3   0.084       AI767675   chymotrypsin-like   1.4   −2.8   1.1   0.083       U26648   syntaxin 5A   1.0   4.0   1.1   0.081       D82351   RNA binding motif, single stranded interacting protein 1   1.3   7.0   1.4   0.080       D86966   KIAA0211 gene product   −1.1   −1.4   −1.1   0.079       S76346   AML1 = AML1 {alternatively spliced, exons 5 and b}   −1.1   −3.6   −1.5   0.078       M63256   cerebellar degeneration-related protein (62kD)   −1.3   −5.3   −1.2   0.078       D21211   protein tyrosine phosphatase (APO-1/CD95 associated   −1.1   −6.5   −1.1   0.076           phosphatase)       U08377   splicing factor, arginine/serine-rich 8   1.0   2.5   −1.1   0.076       AL096751   M-phase phosphoprotein 9   1.6   −3.7   2.1   0.075       AB007940   KIAA0471 gene product   1.3   −1.7   1.5   0.075       Z12173   glucosamine (N-acetyl)-6-sulfatase   −1.1   2.5   1.1   0.074       AF070606   clone 24411 mRNA   1.1   −2.9   1.0   0.074       S40369   glutamate receptor, ionotropic, kainate 5   1.5   −3.7   1.5   0.074       D38293   adaptor-related protein complex 3, mu 2 subunit   2.6   −1.6   1.7   0.071       W28191   43d1 Homo sapiens cDNA   −1.4   −4.4   −1.1   0.070       U21936   solute carrier family 15 (oligopeptide transporter)   −1.7   −9.2   −3.0   0.070       U80764   EST clone 122887 mariner transposon Hsmar1   1.0   −1.4   −1.1   0.070           sequence       D13666   osteoblast specific factor 2 (fasciclin I-like)   −1.1   −5.3   −1.6   0.070       M28211   RAB4, member RAS oncogene family   1.1   4.9   1.2   0.069       AB015633   transmembrane protein 5   1.1   −5.3   1.2   0.067       S59184   RYK receptor-like tyrosine kinase   1.0   −2.6   −1.1   0.067       U11863   amiloride binding protein 1 (amine oxidase)   1.1   −3.9   1.2   0.067       AB011151   KIAA0579 protein   1.1   2.1   −2.3   0.064       X56807   desmocollin 2   −1.3   −8.0   −1.1   0.063       U64805   breast cancer 1, early onset   1.5   −1.4   1.0   0.062       X12534   RAP2A, member of RAS oncogene family   −2.8   −1.1   −1.5   0.062       U50535   Human BRCA2 region, mRNA sequence CG006   −1.1   1.5   1.1   0.062       W28518   48a1 Homo sapiens cDNA   −1.7   −3.2   −1.9   0.061       AB022918   alpha2,3-sialyltransferase   1.0   −3.6   1.1   0.061       Z46606   HLTF gene for helicase-like transcription factor   1.1   2.7   −1.1   0.060       U88964   interferon stimulated gene (20kD)   1.1   1.9   −1.1   0.060       AL050002   cDNA DKFZp564O222   1.1   −1.8   −1.1   0.060       AL080149   bromodomain-containing 1   1.2   −1.1   1.2   0.059       X03363   v-erb-b2 erythroblastic leukemia viral oncogene   −1.4   −3.5   −1.1   0.058       M83667   CCAAT/enhancer binding protein (C/EBP), delta   1.1   −2.9   1.1   0.058       M21574   platelet-derived growth factor receptor, alpha   −1.4   1.5   −1.5   0.058           polypeptide       AF030424   histone acetyltransferase 1   1.1   3.7   1.3   0.057       M19507   Myeloperoxidase   −1.3   −2.1   −1.2   0.057       AF020043   chondroitin sulfate proteoglycan 6 (bamacan)   1.1   −1.2   1.2   0.057       AI765533   ephrin-B2   −1.4   −6.1   −1.5   0.056       M31932   Fc fragment of IgG, low affinity IIa, receptor for (CD32)   −1.4   −3.7   −1.5   0.056       AW026535   leptin receptor gene-related protein   −1.1   −1.6   −1.3   0.055       AB011090   Max-interacting protein   1.0   −1.5   1.1   0.055       AB006631   K1AA0293 protein   −1.3   −8.0   2.5   0.054       X95632   abl-interactor 12 (SH3-containing protein)   −2.0   −11.3   −1.3   0.054       D25216   KIAA0014 gene product   −1.1   −4.0   1.0   0.054       L35263   mitogen-activated protein kinase 14   1.0   1.6   1.1   0.054       M36881   lymphocyte-specific protein tyrosine kinase   1.1   −1.5   1.0   0.054       M13194   excision repair cross-complementing rodent repair   1.1   2.1   1.0   0.053           deficiency       AF007150   angiopoietin-like 2   1.0   −4.1   1.1   0.053       U63743   kinesin-like 6 (mitotic centromere-associated kinesin)   1.0   −3.4   1.1   0.052       AL049415   a disintegrin and metalloproteinase domain 19   1.0   −2.3   1.6   0.052       Y00636   CD58 antigen, (lymphocyte function-associated antigen           3)   1.0   −1.4   1.1   0.050       U07809   nuclear factor I/A   −1.3   −3.1   −1.7   0.048       AF031824   cystatin F (leukocystatin)   −1.1   −3.0   1.3   0.048       AB007915   KIAA0446 gene product   1.1   −1.3   1.3   0.048       D26121   ZFM1 protein alternatively spliced product   1.0   −4.9   1.7   0.047       X84908   phosphorylase kinase, beta   1.0   1.5   1.1   0.047       AF054186   eukaryotic translation elongation factor 1 epsilon 1   1.0   1.7   −1.1   0.045       U37547   baculoviral IAP repeat-containing 2   −1.1   1.9   −1.2   0.045       AL120559   cyclic AMP phosphoprotein, 19 kD   −1.3   −2.0   −1.2   0.045       J03626   uridine monophosphate synthetase   −1.1   −2.1   1.2   0.043       D38535   inter-alpha (globulin) inhibitor H4   −1.2   −2.5   1.2   0.042       M55210   laminin, gamma 1 (formerly LAMB2)   1.7   −1.3   1.9   0.042       AA808961   proteasome (prosome, macropain)   1.1   1.4   1.0   0.042       D63789   small inducible cytokine subfamily C, member 2   1.1   −3.2   −1.4   0.042       D26361   KIAA0042 gene product   −1.3   1.7   −1.5   0.041       AF052177   KIAA1719 protein   −1.2   −3.5   1.2   0.038       AB023153   MAK-related kinase   1.1   3.0   1.0   0.038       U77970   neuronal PAS domain protein 2   −1.1   −5.5   1.3   0.038       M96956   teratocarcinoma-derived growth factor 1   −1.3   −10.9   1.1   0.036       L17075   activin A receptor type II-like 1   1.3   −5.5   −1.3   0.036       U69127   far upstream element (FUSE) binding protein 3   −1.1   1.4   −1.4   0.032       D87457   engulfment and cell motility 1 (ced-12 homolog, C.   −1.1   1.7   −1.3   0.029           elegans)       AL050367   cDNA DKFZp564A026   1.3   −1.3   1.5   0.024       H23429   wingless-type MMTV integration site family, member 4   1.3   2.8   −1.4   0.023       X99141   keratin, hair, basic, 3   1.0   −4.3   1.2   0.022       AL021707   KIAA0063 gene product   1.1   −1.2   1.3   0.022       L42621   lymphocyte antigen 9   1.0   −1.4   1.6   0.022       Y09008   uracil-DNA glycosylase   1.2   2.5   −1.2   0.021       Z69030   protein phosphatase 2, regulatory subunit B (B56)   −1.1   1.4   −1.1   0.020       AF070623   clone 24468 mRNA   −1.4   −5.1   2.0   0.020       AB007923   phosphodiesterase 4D interacting protein   1.6   −4.3   2.1   0.020       M27878   zinc finger protein 84 (HPF2)   1.1   5.3   −1.3   0.018       U42360   Putative prostate cancer tumor suppressor   −2.0   −19.0   1.9   0.018       M68891   GATA binding protein 2   −1.6   −4.1   1.3   0.018       AA883868   ring finger protein 5   −1.1   −2.2   1.2   0.016       L12535   Ras suppressor protein 1   −1.5   −2.3   1.3   0.016       D83664   S100 calcium binding protein A12 (calgranulin C)   −1.7   −7.2   1.1   0.016       J04162   Fc fragment of IgG, low affinity IIIb, receptor for (CD16)   1.1   −5.1   3.2   0.015       AL080209   hypothetical protein DKFZp586F2423   1.1   −1.6   1.4   0.014       M20137   interleukin 3 (colony-stimulating factor, multiple)   −1.4   −4.0   −1.1   0.011       W26981   solute carrier family 17   −2.0   −2.7   1.1   0.010       AF071771   Zinc finger protein 143 (clone pHZ-1)   −1.3   −4.0   −0.5   0.009       AB014562   KIAA0662 gene product   1.1   1.6   −1.1   0.005       L41162   collagen, type IX, alpha 3   −2.0   −5.9   2.5   0.004       X66436   H. sapiens hsr1 mRNA (partial)   1.1   1.6   −1.4   0.004       U17032   Rho GTPase activating protein 5   −1.3   3.2   −4.9   0.003       M60094   H1 histone family, member T (testis-specific)   −2.3   −7.5   1.9   0.002       Z50115   thimet oligopeptidase 1   1.2   2.2   −1.2   0.001       L08237   Omithine Aminotransferase-Like 3   1.1   4.4   −1.5   0.001                       # continuous complete remission (Median FC CCR) and the median fold-change among patients who relapsed (Median FC Relapse) are shown for each gene, with minus (−) indicating genes that exhibited a decrease in expression, whereas a positive number indicates those genes that exhibited an increase in expression after treatment with LDMTX/MP.                   
 
     [0125] Various publications, patent applications and patents are cited herein, the disclosures of which are incorporated by reference in their entireties.