WO2006026074A2 - Genes determinant le phenotype atherosclerotique et methodes d'utilisation - Google Patents

Genes determinant le phenotype atherosclerotique et methodes d'utilisation Download PDF

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WO2006026074A2
WO2006026074A2 PCT/US2005/027989 US2005027989W WO2006026074A2 WO 2006026074 A2 WO2006026074 A2 WO 2006026074A2 US 2005027989 W US2005027989 W US 2005027989W WO 2006026074 A2 WO2006026074 A2 WO 2006026074A2
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cluster incl
mrna
homo sapiens
genes
human
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WO2006026074A3 (fr
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Mike West
Joseph R. Nevins
Pascal Goldschmidt
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Duke University
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Duke University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • Atherosclerosis is the leading cause of morbidity and mortality in the industrialized world While applicants have made substantial progiess in the treatment and prevention of atherosclerosis and the related thromboembolic complications, there remains an urgent need to develop individualized prognostic tools and therapeutic plans Progress m this direction will come with an improved understanding of the genetic foundations of the disease
  • Atherosclerosis is a complex trait manifested by chronic inflammation that selectively affects arterial vessels and progressively destroys the structure of the vessel wall, leading to thromboembolic complications.
  • the thromboembolic consequences of atherosclerosis, sudden cardiac death, myocardial infarction, and other ischemic organ damage such as stroke and ischemic renovascular disease represent the major causes of death, morbidity and disability for developed countries and are spreading rapidly worldwide
  • improved predictive tools are needed to allow for early prevention in a fashion that is cost-effective
  • Atherosclerosis results from the combined interaction of a genetic component and environmental factors
  • the genetic component is not attributable to single causative genes making it difficult to study by standard genetic and molecular biological approaches
  • combinations of gene variants determine an individual's susceptibility to atherosclerosis by enhancing the impact of environmental factors
  • the gene variants are often in the form of single nucleotide polymorphisms (SNPs) SNPs represent subtle variations in a gene's coding sequence or the associated regulatory regions resulting in a mild to moderate impact on the function or concentration of the encoded protein
  • SNPs single nucleotide polymorphisms
  • the invention relates in part, to methods of diagnosing, or aiding m the diagnosis, of atherosclerosis
  • the invention also relates to determining the susceptibility, or aiding in determining the susceptibility, of developing atherosclerosis, such as in a mammal Applicants have used a unique collection of human aorta samples, which exhibit a progression of atherosclerotic disease, coupled with novel strategies for analyzing gene expression data, to identify genes and metagenes whose expression closely relates to, and indeed predicts, the extent of fatty streaks and more advanced atherosclerotic lesions Applicants believe this represents a novel approach to the identification of genes that contribute to atherosclerosis Applicants have also analyzed gene expression data from different sections of aorta to identify genes and metagenes indicative of the susceptibility of vascular tissue to becoming atherosclerotic, or of the mammal from which the vascular sample was derived of developing atherosclerosis
  • the invention also provides methods of using the subject atherosclerotic determinant genes and metagenes in diagnosis and treatment methods, as well as in drug screening methods.
  • reagents and kits thereof that find use in practicing the subject methods are provided Also provided are methods of determining whether a gene is correlated with a disease phenotype, e g , atherosclerosis, where correlation is determined using at least one parameter that is not expression level and is preferably determined using binary prediction tree analysis and metagene construction
  • the invention also provides metagenes for atherosclerosis identified by the use of a binary prediction tree model
  • One aspect of the invention provides a method of estimating whether a sample is from tissue having an atherosclerotic phenotype, said method comprising (a) obtaining an expression profile for said sample from at least two of said genes listed m Table I, (b) providing one or more predictive statistical tree models, each model including one or more nodes, each node representing a metagene, each node including a statistical predictive probability of the having an atherosclerotic phenotype, each metagene representing a dominant factor from a group of genes associated with having an atherosclerotic phenotype, wherein at least two genes m the group of genes are selected from those listed in Table I, and (c) determining an estimate of the sample having the atherosclerotic phenotype by averaging the predictions of one or more of the tree models applied to the expression profile of the sample Steps (a) and (b) may be performed in any order
  • At least two of the genes are selected from those having Genbank accession numbers selected fromY09445, AF053233, U43185, AL050008, AB022718, L10333, M80634, AF044896, X78565, ABOl 1143, X69819, J02947, U78095, D67029, AF013249, AB014574, L13939, L06797, D89077, Y08374, X02317, AB002365, AF084481, D34625, ABOl 1103, AF041259, J05037, AF056087, U81800, AL050262, AB018271, J03011, D12485, U88629, U75308, J03600, AF004709, AB002361, X90858, Z29067, U00952, M80254, AF030339, AJ007395, AF013570, Z22555, L
  • genes from table I are genes having Genbank accession numbers selected from Y09445, AF053233, U43185, AL050008, AB022718, L10333, M80634, AF044896, X78565, ABOl 1143, X69819, J02947, U78095, D67029, AF013249, AB014574, L13939, L06797, D89077, Y08374, X02317, AB002365, AF084481, D34625, ABOl 1103, AF041259, J05037, AF056087, U81800, AL050262, AB018271, J03011, D12485, U88629, U75308, J03600, AF004709, AB002361, X90858, Z29067, U00952, M80254, AF030339, AJ007395,
  • the tissue is a vascular tissue, such as aortic tissue.
  • the tissue is preferably mammalian tissue, such as human, primate or rodent tissue.
  • the sample is from a mammal suspected of having tissue having an atherosclerotic phenotype or from a mammal is at risk of being afflicted with atherosclerosis
  • Mammals at risk for being afflicted with atherosclerosis include those having traditional cardiovascular risk factors
  • Cardiovascular risk factors include but are not limited to cholesterol, HDL cholesterol, systolic blood pressure, cigarette smoking, exercise, alcohol, race, family history of premature coronary artery disease, and medication use, including aspirin, statins, B-blockers and hormone replacement therapy m women
  • the methods for estimating whether a sample is from tissue having an atherosclerotic phenotype are carried out in the context of determining if an agent has anti- atherosclerosis properties
  • a mammal may be treated with a compound, and a sample is obtained from the mammal to determine if the compound can decrease an atherosclerosis phenotype
  • the mammal is a rodent model of atherosclerosis, such as apolipoprotem E (apoE)-deficient C57BL/6 mice
  • other mouse disease models are used, such as KK/ Ay mice, an animal model of type II diabetes
  • at least one metagene in at least one of the predictive statistical tree models is one of the 509 metagenes provided herein
  • at least one of the metagenes (i) is one of the 509 metagenes and (n) represents a dominant factor
  • one or more predictive statistical tree models correctly classify samples with greater than 85%, 90%, 95%, 98% or 99% accuracy
  • the one or more predictive statistical tree models correctly classify samples with an accuracy of up to 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%
  • the invention also provides a method of predicting the susceptibility of a mammal for developing atherosclerosis, the method comprising (a) obtaining an expression profile of at least two of said genes listed m Table II from a sample from the mammal, (b) providing one or more predictive statistical tree models, each model including one or more nodes, each node representing a metagene, each node including a statistical predictive probability of being susceptible to developing atherosclerosis, each metagene representing a dominant factor from a group of genes associated with susceptible to developing atherosclerosis, wherein at least two genes in the group of genes are selected from those listed m Table II, and (c) determining an estimate of the sample being susceptible to developing atherosclerosis by averaging the predictions of one or more of the tree models applied to the expression profile of the sample Steps (a) and (b) may be performed in any order
  • the at least 2, 3, 4, 5, 6 or 7 of the genes are selected from genes having Genbank accession numbers selected from M68891, X51757, D83004, X06256, Z22865, X75918 and M55153
  • the tissue is a vascular tissue, such as aortic tissue
  • the tissue is preferably mammalian tissue, such as human, primate or rodent tissue
  • the sample is from a mammal suspected of having tissue susceptible to developing atherosclerosis Susceptible mammals include those having traditional cardiovascular risk factors
  • the methods for predicting the susceptibility of a mammal for developing atherosclerosis are carried out m the context of determining if an agent can modify the susceptibility of a mammal for developing atherosclerosis
  • a mammal may be treated with a compound, and a sample is obtained from the mammal to determine if the compound can decrease the susceptibility of a mammal for developing atherosclerosis
  • the mammal is a rodent, such as a rodent model of atherosclerosis
  • Mice atherosclerosis models include as apolipoprotem E (apoE)-deficient C57BL/6 mice
  • other mouse disease models are used, such as KK/ Ay mice, an animal model of type II diabetes
  • At least one metagene in at least one of the predictive statistical tree models is one of the 509 metagenes provided herein
  • at least one of the metagenes (i) is one of the 509 metagenes and (ii) represents a dominant factor from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 genes associated with having an atherosclerotic phenotype
  • at least one of the statistical tree models has two or more metagenes
  • one or more predictive statistical tree models correctly classify samples with greater than 85%, 90%, 95%, 98% or 99% accuracy
  • the one or more predictive statistical tree models conectly classify samples with an accuracy of up to 90%, 91%, 92%, 93%, 94% or 95%
  • Figure 1 shows an example prediction tree for cookie fat outcomes
  • the root node splits on predictor/factor 92, followed by two subsequent splits on additional predictors 330 and 305
  • the II values are point estimates of the predictive probabilities of high fat versus low fat at each of the nodes, with suffixes simply indexing nodes
  • the F# symbols indicate the thresholds that define the predictor based splits within each node
  • Figure 2 shows two predictive factors in cookie dough analysis All samples are represented by index numbers 1 through 78 Training data are denoted by blue (low fat) and red (high fat), and validation data by cyan (low fat) and magenta (high fat) The two full lines (black)demark the thresholds on the two predictors m this example tree
  • Figure 3 shows a scatter plot of cookie data on three factors in example tree. Samples are denoted by blue (low fat) and red (high fat), with training data represented by filled circles and validation data by open circles.
  • Figure 4 shows three ER related metagenes in 49 primary breast tumors. Samples are denoted by blue (ER negative) and red (ER positive), with training data represented by filled circles and validation data by open circles.
  • Figure 5 shows three ER related metagenes in 49 primary breast tumors. All samples are represented by index number in 1-78. Training data are denoted by blue (ER negative) and red (ER positive), and validation data by cyan (ER negative) and magenta (ER positive).
  • Figure 6 shows honest predictions of ER status of breast tumors. Predictive probabilities are indicated, for each tumor, by the index number on the vertical probability scale, together with an approximate 90% uncertainty interval about the estimated probability. All probabilities are referenced to a notional initial probability (incidence rate) of 0.5 for comparison. Training data are denoted by blue (ER negative) and red (ER positive), and validation data by cyan (ER negative) and magenta (ER positive).
  • Figure 7 shows cross-validation probability predictions of lymph node status. Samples (tumors) are plotted by index number, and the plotted numbers are marked on the vertical scale at the estimated predictive probabilities of high risk (red) versus low risk (blue). Approximate 90% uncertainty(confidence) intervals about these estimated probabilities are indicated by vertical dashed lines.
  • Figure 8 shows gene expression patterns from the major metagene that predicts lymph node status. Samples are plotted by sample index number and by color (color coding as in Figure 7).
  • Figure 9 shows cross-validation probability predictions of 3-year recurrence. Samples (tumors) are plotted by index number, and the plotted numbers are marked on the vertical scale at the estimated predictive probabilities of 3 year recurrence (red) versus 3 year recurrence free survival (blue). Approximate 90% uncertainty intervals about these estimated probabilities are indicated by vertical dashed lines.
  • Figure 10 shows a diagram of aorta processing.
  • the thoracic aorta is harvested, divided along the ventral aspect and furthered sectioned as shown.
  • the A and B strips are used for RNA extraction.
  • the C strip is evaluated for Sudanophilia and raised lesion mapping.
  • Figure 11 shows a summary of results of cross validation analysis of disease burden analysis. Samples are plotted by the probability that they are severely diseased (95% CI) Severely diseased samples are red and minimally diseased, blue
  • Figure 12 shows a summary of results of cross validation analysis of aorta location Samples are plotted by the probability that they are from the distal location in the aorta (95% CI) Distal sections are red and proximal sections, blue
  • Figure 13 shows a graphical display of gene expression in the key metagene for aortic location The image indicates the discrimination between aorta samples of distal sections and proximal sections by difference of shades
  • Genes whose expression is correlated with and determinant of an atheiosclerotic phenotype or determinant of susceptibility to developing atherosclerosis are provided.
  • the metagenes provided by the invention as useful in binary prediction tree statistical models to classify genes according to atherosclerotic phenotype or susceptibility to atherosclerosis
  • methods of using the subject atherosclerotic determinant genes m diagnosis and treatment methods, as well as drug screening methods
  • reagents and kits thereof that find use in practicing the subject methods are provided Also provided are methods of determining whether a gene is correlated with a disease phenotype, where correlation is determined using at least one parameter that is not expression level and is preferably determined using a binary prediction tree analysis
  • the subject invention is directed to a collection of genes whose expression is correlated with atherosclerosis, i e , that are atherosclerotic phenotype determinative genes, as well as methods for using the collection or subparts thereof in various applications
  • atherosclerosis i e
  • atherosclerotic phenotype determinative genes are described first in greater detail, followed by a review of the various different applications in which the collection finds use, including diagnostic, therapeutic and screening applications
  • reagents and kits for use in practicing the subject methods is provided.
  • Atherosclerotic phenotype determinative genes genes whose expression or lack thereof correlates with an atherosclerotic phenotype
  • atherosclerotic determinative genes include genes (a) whose expression is correlated with an atherosclerotic phenotype, i e , are expressed in cells and tissues thereof that have an atherosclerotic phenotype, and (b) whose lack of expression is correlated with an atherosclerotic phenotype, i e , are not expressed m cells and tissues thereof that have an atherosclerotic phenotype
  • a cell is a cell with an atherosclerotic phenotype if it is obtained from vascular tissue that is determined to be atherosclerotic, e g , by Sudan staining according to the method reported in the experimental section, below Likewise
  • Atherosclerotic susceptibility determinative genes genes whose expression or lack thereof correlates with a susceptibility to developing an atherosclerotic phenotype
  • the invention claims all collections and subsets thereof of atherosclerotic phenotype determinative genes as well as metagenes disclosed herewith
  • the subject collections of atherosclerotic phenotype determinative genes may be physical or virtual Physical collections are those collections that include a population of different nucleic acid molecules, where the atherosclerotic phenotype determinative genes are represented in the population, i e , there are nucleic acid molecules m the population that correspond in sequence to the genomic, or more typically, coding sequence of the atherosclerotic phenotype determinative genes in the collection
  • the nucleic acid molecules are either substantially identical or identical in sequence to the sense strand of the gene to which they correspond, or are complementary to the sense strand to which they correspond, typically to an extent that allows them to hybridize to their corresponding sense strand under stringent conditions
  • An example of stringent hybridization conditions is hybridization at 50°C or higher and 0 IxSSC (15 mM sodium chlo ⁇ de/1 5 mM sodium citrate)
  • Another example of stringent hybridization conditions is overnight incubation at 42 0 C in a solution 50 % formamide, 5 x SSC (150 mM NaCl, 15 mM t ⁇ sodium citrate), 50 mM sodium phosphate (pH 7 6), 5 x Denhardt's solution, 10% dextran sulfate, and 20 ⁇ g/ml denatured, sheared salmon sperm DNA, followed by washing the filters in 0 1 x SSC at about 65 0 C
  • Stringent hybridization conditions are hybridization conditions that are at least as
  • the nucleic acids that make up the subject physical collections may be smgle-stranded or double-stranded
  • the nucleic acids that make up the physical collections may be linear or circular
  • the individual nucleic acid molecules may include, in addition to an atherosclerotic phenotype determinative gene coding sequence, other sequences, e g , vector sequences
  • a variety of different nucleic acids may make up the physical collections, e g , libraries, such as vector libraries, of the subject invention, where examples of different types of nucleic acids include, but are not limited to, DNA, e g , cDNA, etc , RNA, e g , mRNA, cRNA, etc and the like
  • the nucleic acids of the physical collections may be piesent in solution or affixed, i e , attached to, a solid support, such as a substrate as is found in array embodiments, where further description of such diverse embodiments is provided below
  • virtual collections of the subject atherosclerotic phenotype determinative genes By virtual collection is meant one or more data files or other computer readable data organizational elements that include the sequence information of the genes of the collection, where the sequence information may be the genomic sequence information but is typically the coding sequence information
  • the virtual collection may be recorded on any convenient computer or processor readable storage medium
  • the computer or processor readable storage medium on which the collection data is stored may be any convenient medium, including CD, DAT, floppy disk, RAM, ROM, etc, which medium is capable of being read by a hardware component of the device
  • databases of expression profiles of atherosclerotic phenotype determinative genes Such databases will typically comprise expression profiles of various cells/tissues having atherosclerotic phenotypes, such as various stages of atherosclerosis, negative expression profiles, prognostic profiles, etc , where such profiles are further described below
  • Media refers to a manufacture that contains the expression profile information of the present invention
  • the databases of the present invention can be recorded on computer ieadable media, e g any medium that can be read and accessed directly by a computer
  • Such media include, but are not limited to magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape, optical storage media such as CD-ROM, electrical storage media such as RAM and ROM, and hybrids of these categories such as magnetic/optical storage media
  • magnetic storage media such as floppy discs, hard disc storage medium, and magnetic tape
  • optical storage media such as CD-ROM
  • electrical storage media such as RAM and ROM
  • hybrids of these categories such as magnetic/optical storage
  • a computer-based system refers to the hardware means, software means, and data storage means used to analyze the information of the present invention
  • the minimum hardware of the computer-based systems of the present invention comprises a central processing unit (CPU), input means, output means, and data storage means
  • CPU central processing unit
  • input means input means
  • output means output means
  • data storage means may comprise any manufacture comprising a recording of the present information as described above, or a memory access means that can access such a manufacture
  • a variety of structural formats for the input and output means can be used to input and output the information in the computer-based systems of the present invention
  • One format for an output means ranks expression profiles possessing varying degrees of similarity to a reference expression profile Such presentation provides a skilled artisan with a ranking of similarities and identifies the degree of similarity contained in the test expression profile
  • Atherosclerotic phenotype determinative genes of the subject invention are those listed in Table I
  • Specific atherosclerotic susceptibility determinative genes of the subject invention are those listed in Table II
  • the invention also provides metagenes indicative of atherosclerotic burden or susceptibility
  • the subject collections and subsets thereof, as well as applications directed to the use of the aforementioned subject collections only serve as an example to illustrate the invention
  • the subject collections of atherosclerotic-determinative genes include at least 2 of the genes listed in Table I Table I contains the following genes, designated by Genbank Accession Number J04765, AF052124, X15525, M94345, AB020687, U51240, Y09445, AF053233, U43185,
  • Preferred Table I genes consist of the following genes, designated by Genbank Accession Number: Y09445, AF053233, U43185, AL050008, AB022718, Ll 0333, M80634, AF044896, X78565, ABOl 1143, X69819, J02947, U78095, D67029, AF013249, AB014574, L13939, L06797, D89077, Y08374, X02317, AB002365, AF084481, D34625, ABOIl 103, AF041259, J05037, AF056087, U81800, AL050262, ABO 18271, J03011, D12485, U88629, U75308, J03600, AF004709, AB002361, X90858, Z29067, U00952, M80254, AF030339, AJ007395, AF013570, Z22555, L
  • Table II contains the following genes, designated by Genbank Accession Number: M26679, S82986, AF051323, J02947, M16937, K03000, M36711, D76435, M74297, M68891, U43328, X17360, X51757, U59831, D83004, L49169, L35545, U16799, M20560, X06256, Z22865, X75918, X16665, M97676, M55153.
  • Preferred Table II genes consist of the following genes, designated by Genbank Accession Number: M68891, X51757, D83004, X06256, Z22865, X75918, M55153.
  • the number of genes in the collection that are from Table I or Table II is at least 5, at least 10, at least 25, at least 50, at least 75 or more, including all of the genes listed in Table I or are preferred Table I genes.
  • the subject collections may include only those genes that are listed in Tables I and/or Table II, or they may include additional genes that are not listed in the tables. Where the subject collections include such additional genes, in certain embodiments the % number of additional genes that are present in the subject collections does not exceed about 50%, usually does not exceed about 25 %.
  • a great majority of genes in the collection are atherosclerotic phenotype determinative genes, where by great majority is meant at least about 75%, usually at least about 80 % and sometimes at least about 85, 90, 95 % or higher, including embodiments where 100% of the genes in the collection are atherosclerotic phenotype determinative genes.
  • At least one of the genes in the collection is a gene whose function does not readily implicate it in the production of an atherosclerotic phenotype where such genes include those genes that are listed in Table I but which have not been assigned a biological process
  • the subject collections include two or more genes from this group, where the number of genes that are included from this group may be 5, 10, 20 or more, up to and including all of the genes in this group.
  • the set comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 40 or 50 preferred genes from Table I.
  • At least one of the genes in the collection is a gene whose function does not readily implicate it in susceptibility to atherosclerosis, where such genes include those genes that are listed in Table I but which have not been assigned a biological process (see section 3 of the experimental section V for a listing of atherosclerotic susceptibility genes which have been assigned a biological functions; those not listed are the ones without a biological function assigned).
  • the subject collections include 2 or more genes from this group, where the number of genes that are included from this group may be 5, 10, 20 or more, up to and including all of the genes in this group.
  • the set comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 40 or 50 preferred genes from Table II.
  • the subject collections find use in a number of different applications.
  • Applications of interest include, but are not limited to: (a) diagnostic applications, in which the collections of the genes are employed to either predict the presence of, or the probability for occurrence of, an atherosclerotic phenotype; (b) pharmacogenomic applications, in which the collections of genes are employed to determine an appropriate therapeutic treatment regimen, which is then implemented; and (c) therapeutic agent screening applications, where the collection of genes is employed to identify atherosclerotic phenotype modulatory agent or to identify atherosclerotic susceptibility modulatory agents.
  • diagnostic applications include methods of determining the presence of an atherosclerotic phenotype. In certain embodiments, not only the presence but also the severity or stage of an atherosclerotic phenotype is determined. In addition, diagnostic methods also include methods of determining the propensity to develop an atherosclerotic phenotype, such that a determination is made that an atherosclerotic phenotype is not present but is likely to occur.
  • a nucleic acid sample obtained or derived from a cell, tissue or subject that includes the same that is to be diagnosed is first assayed to generate an expression profile, where the expression profile includes expression data for at least two of the genes of Table I or Table II, or preferred genes within those tables, where the expression profile may include expression data for 5, 10, 20, 50, 75 or more of, including all of, the genes listed in Table I or Table II, or preferred genes within those tables.
  • the expression profile also includes expression data for at least 1 of the genes listed in Table I or Table II, or preferred genes within those tables, wherein the expression profile may include expression data for 2, 5, 10, 20 or more, including all of the genes listed in Table I or Table II, or preferred genes within those tables.
  • the number of different genes whose expression data, i.e., presence or absence of expression, as well as expression level, that are included in the expression profile that is generated may vary, but is typically at least 2, and in many embodiments ranges from 2 to about 100 or more, sometimes from 3 to about 75 or more, including from about 4 to about 70 or more.
  • the sample that is assayed to generate the expression profile employed in the diagnostic methods is one that is a nucleic acid sample.
  • the nucleic acid sample includes a plurality or population of distinct nucleic acids that includes the expression information of the atherosclerotic phenotype detenninative genes of interest of the cell or tissue being diagnosed.
  • the nucleic acid may include RNA or DNA nucleic acids, e.g., mRNA, cRNA, cDNA etc., so long as the sample retains the expression information of the host cell or tissue from which it is obtained.
  • the sample may be prepared in a number of different ways, as is known in the art, e.g., by mRNA isolation from a cell, where the isolated mRNA is used as is, amplified, employed to prepare cDNA, cRNA, etc., as is known in the differential expression art.
  • the sample is typically prepared from a cell or tissue harvested from a subject to be diagnosed, e.g., via biopsy of tissue, using standard protocols, where cell types or tissues from which such nucleic acids may be generated include any tissue in which the expression pattern of the to be determined atherosclerotic phenotype exists, including, but not limited, to, monocytes, endothelium, and/or smooth muscle.
  • the expression profile may be generated from the initial nucleic acid sample using any convenient protocol While a variety of different manners of generating expression profiles are known, such as those employed in the field of differential gene expression analysis, one representative and convenient type of protocol for generating expression profiles is array based gene expression profile generation protocols
  • Such applications are hybridization assays in which a nucleic acid that displays "probe" nucleic acids for each of the genes to be assayed/profiled in the profile to be generated is employed
  • a sample of target nucleic acids is first prepared from the initial nucleic acid sample being assayed, where preparation may include labeling of the target nucleic acids with a label, e g , a member of signal producing system
  • the sample is contacted with the array under hybridization conditions, whereby complexes are formed between target nucleic acids that are complementary to probe sequences attached to the array surface
  • the presence of hybridized complexes is then detected, either qualitatively or quantitatively Specific hybridization technology which may be practiced to generate
  • the obtained expression profile may be compared to a series of positive control/reference profiles each representing a different stage/level of atherosclerosis, so as to obtain more in depth information regarding the particular atherosclerotic phenotype of the assayed cell/tissue.
  • the obtained expression profile may be compared to a prognostic control/reference profile, so as to obtain information about the propensity of the cell/tissue to develop an atherosclerotic phenotype.
  • the comparison of the obtained expression profile and the one or more reference/control profiles may be performed using any convenient methodology, where a variety of methodologies are known to those of skill in the array art, e.g., by comparing digital images of the expression profiles, by comparing databases of expression data, etc.
  • Patents describing ways of comparing expression profiles include, but are not limited to, U.S. Patent Nos. 6,308,170 and 6,228,575, the disclosures of which are herein incorporated by reference. Methods of comparing expression profiles are also described above.
  • the comparison step results in information regarding how similar or dissimilar the obtained expression profile is to the control/reference profiles, which similarity/dissimilarity information is employed to determine the atherosclerotic phenotype of the cell/tissue being assayed.
  • similarity with a positive control indicates that the assayed cell/tissue has an atherosclerotic phenotype.
  • similarity with a negative control indicates that the assayed cell/tissue does not have an atherosclerotic phenotype.
  • the above comparison step yields a variety of different types of information regarding the cell/tissue that is assayed.
  • the above comparison step can yield a positive/negative determination of an atherosclerotic phenotype of an assayed cell/tissue.
  • the above comparison step can yield information about the particular stage of an atherosclerotic phenotype of an assayed cell/tissue.
  • the above comparison step can be used to obtain information regarding the propensity of the cell or tissue to develop an atherosclerotic phenotype.
  • the above obtained information about the cell/tissue being assayed is employed to diagnose a host, subject or patient with respect to the presence of, state of or propensity to develop, atherosclerosis.
  • the information may be employed to diagnose a subject from which the cell/tissue was obtained as having atherosclerosis.
  • Atherosclerotic phenotype determinative genes finds use in is pharmacogenomic and/or surgicogenomic applications.
  • a subject/host/patient is first diagnosed for an atherosclerotic phenotype, e.g., presence or absence of atherosclerosis, propensity to develop atherosclerosis, etc., using a protocol such as the diagnostic protocol described in the preceding section
  • pharmacological and/or surgical treatment protocol where the suitability of the protocol for a particular subject/patient is determined using the results of the diagnosis step
  • pharmacological and surgical treatment protocols include, but are not limited to surgical treatment protocols, including bypass grafting, endarterectomy, and percutaneous translumenal angioplasty (PCTA)
  • Pharmacological protocols of interest include treatment with a variety of different types of agents, including but not limited to thrombolytic agents, growth factors, cytokines, nucleic acids (e g gene therapy agents), etc
  • a cell/tissue sample of a patient undergoing treatment for an atherosclerosis disease condition is monitored using the procedures described above in the diagnostic section, where the obtained expression profile is compared to one or more reference profiles to determine whether a given treatment protocol is having a desired impact on the disease being treated
  • periodic expression profiles are obtained from a patient during treatment and compared to a series of reference/controls that includes expression profiles of various atherosclerotic stages and normal expression profiles
  • An observed change m the monitored expression profile towards a normal profile indicates that a given treatment protocol is working in a desired manner
  • the present invention also encompasses methods for identification of agents having the ability to modulate an atherosclerotic phenotype, e g , enhance or dimmish an atherosclerotic phenotype, which finds use in identifying therapeutic agents for atherosclerosis
  • Identification of compounds that modulate an atherosclerotic phenotype can be accomplished using any of a variety of drug screening techniques
  • the screening assays of the invention are generally based upon the ability of the agent to modulate an expression profile of atherosclerotic phenotype determinative genes
  • agent as used herein describes any molecule, e g , protein or pharmaceutical, with the capability of modulating a biological activity of a gene product of a differentially expressed gene Generally a plurality of assay mixtures are run in parallel with different agent concentrations to obtain a differential response to the various concentrations Typically, one of these concentrations serves as a negative control, i e , at zero concentration or below the level of detection
  • Candidate agents encompass numerous chemical classes, though typically they are organic molecules, preferably small organic compounds having a molecular weight of more than 50 and less than about 2,500 Daltons
  • Candidate agents comprise functional groups necessary for structural interaction with proteins, particularly hydrogen bonding, and typically include at least an amine, carbonyl, hydroxyl or carboxyl group, preferably at least two of the functional chemical groups
  • the candidate agents often comprise cyclical carbon or heterocyclic structures and/or aromatic or polyaromatic structures substituted with one or more of the above functional groups
  • Candidate agents are also found among biomolecules including, but not limited to peptides, saccharides, fatty acids, steroids, purines, pyrimidmes, derivatives, structural analogs or combinations thereof
  • Candidate agents are obtained from a wide variety of sources including libraries of synthetic or natural compounds For example, numerous means are available for random and directed synthesis of a wide variety of organic compounds and biomolecules, including expression of randomized oligonucleotides and oligopeptides Alternatively, libraries of natural compounds in the form of bacterial, fungal, plant and animal extracts (including extracts from human tissue to identify endogenous factors affecting differentially expressed gene products) are available or readily produced Additionally, natural or synthetically produced libraries and compounds are readily modified through conventional chemical, physical and biochemical means, and may be used to produce combinatorial libraries Known pharmacological agents may be subjected to directed or random chemical modifications, such as acylation, alkylation, este ⁇ fication, amidif ⁇ cation, etc to produce structural analogs
  • Exemplary candidate agents of particular interest include, but are not limited to, antisense polynucleotides, and antibodies, soluble receptors, and the like Antibodies and soluble receptors are of particular interest as candidate agents where the target differentially expressed gene product is secreted or accessible at the cell-surface (e g , receptors and other molecule stably-associated with the outer cell membrane)
  • Screening assays can be based upon any of a variety of techniques readily available and known to one of ordinary skill in the art
  • the screening assays involve contacting a cell or tissue known to have an atherosclerotic phenotype with a candidate agent, and assessing the effect upon a gene expression profile made up of atherosclerotic phenotype determinative genes
  • the effect can be detected using any convenient protocol, where in many embodiments the diagnostic protocols described above are employed Generally such assays are conducted in vitro, but many assays can be adapted for in vivo analyses, e g , m an animal model of the diabetes
  • the invention contemplates identification of genes and gene products from the subject collections of atherosclerotic determinative genes or of atherosclerosis susceptibility genes as therapeutic targets
  • this is the converse of the assays described above for identification of agents having activity m modulating (e g , decreasing or increasing) an atherosclerotic phenotype, and is directed towards identifying genes that are atherosclerotic phenotype determinative genes, e g , the genes appearing in Table I, as therapeutic targets or directed towards identifying genes that are atherosclerotic susceptibility determinative genes, e g , the genes appearing in Table II, as therapeutic targets
  • therapeutic targets are identified by examining the effect(s) of an agent that can be demonstrated or has been demonstrated to modulate an atherosclerotic phenotype (e g , inhibit or suppress an atherosclerotic phenotype)
  • the agent can be an antisense oligonucleotide that is specific for a selected
  • Assays for identification of therapeutic targets can be conducted in a variety of ways using methods that are well known to one of ordinary skill in the art
  • a test cell that expresses or overexpresses a candidate gene, e g , a gene found in Table I is contacted with the known atherosclerotic agent, the effect upon a atherosclerotic phenotype and a biological activity of the candidate gene product assessed
  • the biological activity of the candidate gene product can be assayed be examining, for example, modulation of expression of a gene encoding the candidate gene product (e g , as detected by, for example, an increase or decrease in transcript levels or polypeptide levels), or modulation of an enzymatic or other activity of the gene product
  • Inhibition or suppression of the atherosclerotic phenotype indicates that the candidate gene product is a suitable target for atherosclerotic therapy
  • Assays described herein and/or known in the art can be readily adapted in for assays for identification of therapeutic targets
  • reagents and kits thereof for practicing one or more of the above described methods
  • the subject reagents and kits thereof may vary greatly
  • Reagents of interest include reagents specifically designed for use in production of the above described expression profiles of atherosclerotic phenotype determinative genes
  • One type of such reagent is an array probe nucleic acids in which the atherosclerotic phenotype determinative genes of interest are represented
  • array probe nucleic acids in which the atherosclerotic phenotype determinative genes of interest are represented
  • Representative array structures of interest include those described in U S Patent Nos 5,143,854, 5,288,644, 5,324,633, 5,432,049, 5,470,710, 5,492,806, 5,503,980, 5,510,270, 5,525,464, 5,547,839, 5,580,732, 5,661,028, 5,800,992, the disclosures of which are herein incorporated by reference, as well as WO 95/21265, WO 96/31622,
  • the subject arrays may include only those genes that are listed in Table I and/or Table II, or they may include additional genes that are not listed m Table I and Table II Where the subject arrays include probes for such additional genes, in certain embodiments the number % of additional genes that are represented does not exceed about 50%, usually does not exceed about 25 % In many embodiments where additional "non-Table I or Table II" genes are included, a great majority of genes in the collection are atherosclerotic phenotype determinative genes, where by great majority is meant at least about 75%, usually at least about 80 % and sometimes at least about 85, 90, 95 % or higher, including embodiments where 100% of the genes in the collection are atherosclerotic phenotype determinative genes In many embodiments, at least one of the genes represented on the array is a gene whose function does not readily implicate it m the production of an atherosclerotic phenotype, where such genes include those genes listed in Table I and/or Table II In many embodiments, the subject arrays include 2
  • Another type of reagent that is specifically tailored for generating expression profiles of atherosclerotic phenotype determinative genes is a collection of gene specific primers that is designed to selectively amplify such genes
  • Gene specific primers and methods for using the same are described in U S Patent No 5,994,076, the disclosure of which is herein incorporated by reference
  • the number of genes that are from Table I and/or Table II that have primers in the collection is at least 5, at least 10, at least 25, at least 50, at least 75 or more, including all of the genes listed in Table I and/or Table II
  • the subject gene specific primer collections may include only those genes that are listed m Table I and/or Table II, or they may include primers for additional genes that are not listed m Table I and/or Table II Where the subject gene specific primer collections include primers for such additional genes, in certain embodiments the number % of additional genes that are represented does not exceed about
  • the subject kits will further include instructions for practicing the subject methods These instructions may be present in the subject kits in a variety of forms, one or more of which may be present m the kit One form m which these instructions may be present is as printed information on a suitable medium or substrate, e g , a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc Yet another means would be a computer readable medium, e g , diskette, CD, etc , on which the information has been recorded Yet another means that may be present is a website address which may be used via the internet to access the information at a removed site Any convenient means may be present in the kits
  • the subject invention provides methods of ameliorating, e g , treating, an atherosclerotic disease conditions, by modulating the expression of one or more target genes or the activity of one or more products thereof, where the target genes are one or more of the atherosclerotic phenotype determinative genes of Table I, the preferred genes of Table I, or the genes in Table I to which no function has been assigned
  • the subject invention provides methods of decreasing the susceptibility to atherosclerotic disease by modulating the expression of one or more target genes or the activity of one or more products thereof, where the target genes are one or more of the atherosclerotic susceptibility determinative genes of Table II, the preferred genes of Table II, or the genes m Table II to which no function has been assigned
  • cardiovascular diseases are brought about, at least in part, by an excessive level of gene product, or by the presence of a gene product exhibiting an abnormal or excessive activity As such, the reduction in the level and/or activity of such gene products would bring about the amelioration of cardiovascular disease symptoms Techniques for the reduction of target gene expression levels or target gene product activity levels are discussed below
  • cardiovascular diseases are brought about, at least in part, by the absence or reduction of the level of gene expression, or a reduction m the level of a gene product's activity
  • an increase in the level of gene expression and/or the activity of such gene products would bring about the amelioration of cardiovascular disease symptoms
  • target genes involved in cardiovascular disease disorders can cause such disorders via an increased level of target gene activity
  • a variety of techniques may be utilized to inhibit the expression, synthesis, or activity of such target genes and/or proteins
  • compounds such as those identified through assays described which exhibit inhibitory activity, may be used in accordance with the invention to ameliorate cardiovascular disease symptoms
  • such molecules may include, but are not limited to small organic molecules, peptides, antibodies, and the like Inhibitory antibody techniques are described, below
  • compounds can be administered that compete with an endogenous ligand for the target gene product, where the target gene product binds to an endogenous ligand
  • the resulting reduction m the amount of hgand-bound gene target will modulate endothelial cell physiology
  • Compounds that can be particularly useful for this purpose include, for example, soluble proteins or peptides, such as peptides comprising one or more of the extracellular domains, or portions and/or analogs thereof, of the target gene product, including, for example, soluble fusion proteins such as Ig- tailed fusion proteins (For a discussion of the production of Ig-tailed fusion proteins, see, for example, U S Pat No 5,116,964 )
  • compounds, such as ligand analogs or antibodies, that bind to the target gene product receptor site, but do not activate the protein, e g , receptor-hgand antagonists
  • antisense and ⁇ bozyme molecules which inhibit expression of
  • RNA interference molecules RNA interference molecules
  • ⁇ bozyme triple helix molecules
  • triple helix molecules Such molecules may be designed to reduce or inhibit mutant target gene activity Techniques for the production and use of such molecules are well known to those of skill in the art
  • Anti-sense RNA and DNA molecules act to directly block the translation of mRNA by hybridizing to targeted mRNA and preventing protein translation
  • ohgodeoxyribonucleotides derived from the translation initiation site, e g between the -10 and +10 regions of the target gene nucleotide sequence of interest, are preferred
  • RNA interference or "RNAi” is a term initially applied to a phenomenon observed in plants and worms where double-stranded RNA (dsRNA) blocks gene expression in a specific and post-transc ⁇ ptional manner
  • dsRNA double-stranded RNA
  • RNAi construct is a generic term used herein to include small interfering RNAs (siRNAs, around 19-30 nucleotides in length), hairpin RNAs, and other RNA species which can be cleaved in vivo to form siRNAs.
  • RNAi constructs also include expression vectors (also referred to as RNAi expression vectors) capable of giving rise to transcripts which form dsRNAs or hairpin RNAs in cells, and/or transcripts which can produce siRNAs in vivo.
  • RNAi constructs contain a nucleotide sequence that hybridizes under physiologic conditions of the cell to the nucleotide sequence of at least a portion of the mRNA transcript for the gene to be inhibited ⁇ i.e., the "target" gene).
  • the double-stranded RNA need only be sufficiently similar to natural RNA that it has the ability to mediate RNAi.
  • the invention has the advantage of being able to tolerate sequence variations that might be expected due to genetic mutation, strain polymorphism or evolutionary divergence.
  • the number of tolerated nucleotide mismatches between the target sequence and the RNAi construct sequence is no more than 1 in 5 base pairs, or 1 in 10 base pairs, or 1 in 20 base pairs, or 1 in 50 base pairs. Mismatches in the center of the siRNA duplex are most critical and may essentially abolish cleavage of the target RNA.
  • nucleotides at the 3' end of the siRNA strand that is complementary to the target RNA do not significantly contribute to specificity of the target recognition.
  • RNAi constructs can be synthesized using methods well known in the art to synthesize or recombinantly produce RNA molecules.
  • Ribozymes are enzymatic RNA molecules capable of catalyzing the specific cleavage of RNA.
  • the mechanism of ribozyme action involves sequence specific hybridization of the ribozyme molecule to complementary target RNA, followed by an endonucleolytic cleavage.
  • the composition of ribozyme molecules must include one or more sequences complementary to the target gene mRNA, and must include the well known catalytic sequence responsible for mRNA cleavage. For this sequence, see U.S. Pat. No. 5,093,246, which is incorporated by reference herein in its entirety.
  • RNA sequences encoding target gene proteins are engineered hammerhead motif ribozyme molecules that specifically and efficiently catalyze endonucleolytic cleavage of RNA sequences encoding target gene proteins.
  • Specific ribozyme cleavage sites within any potential RNA target are initially identified by scanning the molecule of interest for ribozyme cleavage sites which include the following sequences, GUA, GUU and GUC. Once identified, short RNA sequences of between 15 and 20 ribonucleotides corresponding to the region of the target gene containing the cleavage site may be evaluated for predicted structural features, such as secondary structure, that may render the oligonucleotide sequence unsuitable. The suitability of candidate sequences may also be evaluated by testing their accessibility to hybridization with complementary oligonucleotides, using ribonuclease protection assays.
  • Nucleic acid molecules to be used in triple helix formation for the inhibition of transcription should be single stranded and composed of deoxyribonucleotides.
  • the base composition of these oligonucleotides must be designed to promote triple helix formation via Hoogsteen base pairing rules, which generally require sizeable stretches of either purines or pyrimidines to be present on one strand of a duplex.
  • Nucleotide sequences may be pyrimidine-based, which will result in TAT and CGC+ triplets across the three associated strands of the resulting triple helix.
  • the pyrimidine-rich molecules provide base complementarity to a pu ⁇ ne- ⁇ ch region of a single strand of the duplex in a parallel orientation to that strand
  • nucleic acid molecules may be chosen that are pu ⁇ ne- ⁇ ch, for example, containing a stretch of G residues
  • the potential sequences that can be targeted for triple helix formation may be increased by creating a so called "switchback" nucleic acid molecule Switchback molecules are synthesized in an alternating 5'-3', 3'-5' manner, such that they base pair with first one strand of a duplex and then the other, eliminating the necessity for a sizeable stretch of either purines or pyrimidines to be present on one strand of a duplex
  • the antisense, RNAi, ⁇ bozyme, and/or triple helix molecules described herein may reduce or inhibit the transcription (triple helix) and/or translation (antisense, ⁇ bozyme) of mRNA produced by both normal and mutant target gene alleles
  • nucleic acid molecules that encode and express target gene polypeptides exhibiting normal activity may be introduced into cells via gene therapy methods such as those described, below, that do not contain sequences susceptible to whatever antisense, RNAI, ⁇ bozyme, or triple helix treatments are being utilized
  • Anti-sense RNA and DNA, RNAi constructs, ribozyme, and triple helix molecules of the invention may be prepared by any method known in the art for the synthesis of DNA and RNA molecules These include techniques for chemically synthesizing o
  • Antibodies that are both specific for target gene protein and interfere with its activity may be used to inhibit target gene function.
  • Such antibodies may be generated using standard techniques known in the art against the proteins themselves or against peptides corresponding to portions of the proteins Such antibodies include but are not limited to polyclonal, monoclonal, Fab fragments, single chain antibodies, chimeric antibodies, etc
  • hpofectm liposomes may be used to deliver the antibody or a fragment of the Fab region which binds to the target gene epitope into cells Where fragments of the antibody are used, the smallest inhibitory fragment which binds to the target protein's binding domain is preferred
  • peptides having an ammo acid sequence corresponding to the domain of the variable region of the antibody that binds to the target gene protein may be used Such peptides may be synthesized chemically or produced via recombinant DNA technology using methods well known in the art (e g , see Creighton, 1983, supra, and Sambrook et al , 1989, supra)
  • single chain neutralizing antibodies which bind to intracellular target gene epitopes may also be administered Such single chain antibodies may be administered, for example, by expressing nucleotide sequences encoding single-cham antibodies withm the target cell population by utilizing, for example, techniques such as
  • Antibodies that are specific for one or moie extiacellular domains of the gene product, for example, and that interfere with its activity, are particularly useful m treating cardiovascular disease Such antibodies are especially efficient because they can access the target domains directly from the bloodstream Any of the administration techniques described, below which are appropriate for peptide administration may be utilized to effectively administer inhibitory target gene antibodies to their site of action
  • Target genes that cause cardiovascular disease may be underexpressed withm cardiovascular disease situations Alternatively, the activity of target gene products may be diminished, leading to the development of cardiovascular disease symptoms Described m this Section are methods whereby the level of target gene activity may be increased to levels wherein cardiovascular disease symptoms are ameliorated
  • the level of gene activity may be increased, for example, by either increasing the level of target gene product present or by increasing the level of active target gene product which is present
  • a target gene protein at a level sufficient to ameliorate cardiovascular disease symptoms may be administered to a patient exhibiting such symptoms Any of the techniques discussed, below, may be utilized for such administration
  • One of skill in the art will readily know how to determine the concentration of effective, non-toxic doses of the normal target gene protem, utilizing techniques known to those of ordinary skill in the art
  • RNA sequences encoding target gene protein may be directly administered to a patient exhibiting cardiovascular disease symptoms, at a concentration sufficient to produce a level of target gene protein such that cardiovascular disease symptoms are ameliorated Any of the techniques discussed, below, which achieve intracellular administration of compounds, such as, for example, liposome administration, may be utilized for the administration of such RNA molecules.
  • the RNA molecules may be produced, for example, by recombinant techniques as is known in the art.
  • patients may be treated by gene replacement therapy.
  • One or more copies of a normal target gene, or a portion of the gene that directs the production of a normal target gene protein with target gene function may be inserted into cells using vectors which include, but are not limited to adenovirus, adeno-associated virus, and retrovirus vectors, in addition to other particles that introduce DNA into cells, such as liposomes.
  • vectors which include, but are not limited to adenovirus, adeno-associated virus, and retrovirus vectors, in addition to other particles that introduce DNA into cells, such as liposomes.
  • techniques such as those described above may be utilized for the introduction of normal target gene sequences into human cells.
  • Cells, preferably, autologous cells, containing normal target gene expressing gene sequences may then be introduced or reintroduced into the patient at positions which allow for the amelioration of cardiovascular disease symptoms.
  • Such cell replacement techniques may be preferred, for example, when the target gene product is a secreted, extracellular gene product.
  • the identified compounds that inhibit target gene expression, synthesis and/or activity can be administered to a patient at therapeutically effective doses to treat or ameliorate cardiovascular disease.
  • a therapeutically effective dose refers to that amount of the compound sufficient to result in amelioration of symptoms of cardiovascular disease.
  • Toxicity and therapeutic efficacy of such compounds can be determined by standard pharmaceutical procedures in cell cultures or experimental animals, e.g., for determining the LD 50 (the dose lethal to 50% of the population) and the ED 50 (the dose therapeutically effective in 50% of the population).
  • the dose ratio between toxic and therapeutic effects is the therapeutic index and it can be expressed as the ratio LD 50 /ED 50 .
  • Compounds which exhibit large therapeutic indices are preferred. While compounds that exhibit toxic side effects may be used, care should be taken to design a delivery system that targets such compounds to the site of affected tissue to minimize potential damage to uninfected cells and, thereby, reduce side effects.
  • the data obtained from the cell culture assays and animal studies can be used in formulating a range of dosage for use in humans.
  • the dosage of such compounds lies preferably within a range of circulating concentrations that include the ED 50 with little or no toxicity.
  • the dosage may vary within this range depending upon the dosage form employed and the route of administration utilized.
  • the therapeutically effective dose can be estimated initially from cell culture assays.
  • a dose may be formulated in animal models to achieve a circulating plasma concentration range that includes the IC 50 (i.e., the concentration of the test compound which achieves a half-maximal inhibition of symptoms) as determined in cell culture.
  • IC 50 i.e., the concentration of the test compound which achieves a half-maximal inhibition of symptoms
  • levels in plasma may be measured, for example, by high performance liquid chromatography. FORMULATIONS AND USE
  • compositions for use in accordance with the present invention may be formulated in conventional manner using one or more physiologically acceptable carriers or excipients Suitable vehicles and their formulation inclusive of various proteins are described, for example, in the book Remington's Ph ⁇ maceutical Sciences (Mack Publishing Company, Easton, Pa , USA 1985) or Handbook of Pharmaceutical Excipients, 4 th ed (Ed Rowe et al , Pharmaceutical Press, Grayslake, IL, USA 2003), the contents of which are incorporated herein by reference Thus, the compounds and their physiologically acceptable salts and solvates may be formulated for admmistiation by inhalation or insufflation (either through the mouth or the nose) or oral, buccal, parenteral or rectal administration
  • the pharmaceutical compositions may take the form of, for example, tablets or capsules prepared by conventional means with pharmaceutically acceptable excipients such as binding agents (e g , pregelatimzed maize starch, polyvinylpyrrolidone or hydroxypropyl methylcellulose), fillers (e g , lactose, microcrystallme cellulose or calcium hydrogen phosphate), lubricants (e g , magnesium stearate, talc or silica), dismtegrants (e g , potato starch or sodium starch glycolate), or wetting agents (e g , sodium lauryl sulphate)
  • binding agents e g , pregelatimzed maize starch, polyvinylpyrrolidone or hydroxypropyl methylcellulose
  • fillers e g , lactose, microcrystallme cellulose or calcium hydrogen phosphate
  • lubricants e g , magnesium stearate, talc or silica
  • the compounds may also be formulated in rectal compositions such as suppositories or retention enemas, e g , containing conventional suppository bases such as cocoa butter or other glyce ⁇ des
  • the compounds may also be formulated as a depot preparation Such long acting formulations may be administered by implantation (for example subcutaneously or intramuscularly) or by intramuscular injection
  • the compounds may be formulated with suitable polymeric or hydrophobic materials (for example as an emulsion in an acceptable oil) or ion exchange resms, or as sparingly soluble derivatives, for example, as a sparingly soluble salt
  • compositions may, if des ⁇ ed, be presented in a pack or dispenser device which may contain one or more unit dosage forms containing the active ingredient
  • the pack may for example comprise metal or plastic foil, such as a blister pack
  • the pack or dispenser device may be accompanied by instructions for administration
  • an expression profile for a nucleic acid sample obtained from a source having the atherosclerotic phenotype, or a sample to be tested for susceptibility is prepared using the gene expression profile generation techniques described above, with the only difference being that the genes that are assayed are candidate genes and not genes necessarily known to be atherosclerotic phenotype/susceptibihty determinative genes
  • the obtained expression profile is compared to a control profile, e g , obtained from a source that does not have an atherosclerotic phenotype
  • a feature of the subject invention is that the correlation is based on at least one parameter that is other than expression level As such, a parameter other than whether a gene is up or down regulated is employed to find a correlation of the gene with the atherosclerotic phenotype
  • One expression analysis approach may include a Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes as illustrated in the following three exemplary analyses
  • Bayesian analysis is an approach to statistical analysis that is based on the Bayes law, which states that the posterior probability of a parameter p is proportional to the prior probability of parameter p multiplied by the likelihood of p derived from the data collected
  • This increasingly popular methodology represents an alternative to the traditional (or frequentist probability) approach whereas the latter attempts to establish confidence intervals around parameters, and/or falsify a-p ⁇ o ⁇ null-hypotheses
  • the Bayesian approach attempts to keep track of how a-p ⁇ o ⁇ expectations about some phenomenon of interest can be refined, and how observed data can be integrated with such a- prio ⁇ beliefs, to arrive at updated posterior expectations about the phenomenon
  • Bayesian analysis have been applied to numerous statistical models to predict outcomes of events based on available data
  • These include standard regression models, e g binary regression models, as well as to more complex models that are applicable to multi-
  • Another such model is commonly known as the tree model which is essentially based on a decision tree Decision trees can be used in clarification, prediction and regression
  • a decision tree model is built starting with a root mode, and training data partitioned to what are essentially the
  • splitting rule For instance, for clarification, training data contains sample vectors that have one or more measurement variables and one variable that determines that class of the sample
  • Various splitting rules have been used, however, the success of the predictive ability varies considerably as data sets become larger
  • past attempts at determining the best splitting for each mode is often based on a "purity" function calculated from the data, where the data is considered pure when it contains data samples only from one clan
  • used purity functions are entropy, gmi-mdex, and towmg rule
  • Bayes' factor B ⁇ may be evaluated for all predictors and, for each predictor, for any specified range of thresholds.
  • the Bayes' factor maps out a function of T and high values identify ranges of interest for thresholding that predictor.
  • T the only relevant threshold to consider
  • each probability ⁇ ⁇ is a non-decreasing function of T, a constraint that must be formally represented in the model.
  • the key point is that the beta prior specification must formally reflect this.
  • the sequence of beta priors, Be(a n b ⁇ ) as T varies, represents a set of marginal prior distributions for the corresponding set of values of the cdfs.
  • the threshold-specific beta priors are consistent, and the resulting sets of Bayes' factors comparable as T varies, under a Dirichlet process prior with the betas as margins.
  • the required constraint is that the prior mean values m r are themselves values of a cumulative distribution function on the range of ⁇ , one that defines the prior mean of each ⁇ ⁇ as a function.
  • Bayes' factor measure of association that may be used in the generation of trees in a forward-selection process as implemented in traditional classification tree approaches
  • Given the data in this node construct a binary split based on a chosen (predictor, threshold) pair ( ⁇ , r) by (a) finding the (predictor, threshold) combination that maximizes the Bayes' factor for a split, and (b) splitting if the resulting Bayes' factor is sufficiently large
  • Bayes' factors of 2 2,2 9,3 7 and 5 3 correspond, approximately, to probabilities of 9, 95, 99 and 995, respectively
  • This guides the choice of threshold which may be specified as a single value for each level of the tree
  • Bayes' factor thresholds of around 3 in a range of analyses as exemplified below Higher thresholds limit the growth of trees by ensuring
  • Inference and prediction involves computations for branch probabilities and the predictive probabilities for new cases that these underlie. We detail this for a specific path down the tree, i.e., a sequence of nodes from the root node to a specified terminal node.
  • the predictor profile of this new case is such that the implied path traverses nodes 0, 1, 4, 9, terminating at node 9.
  • This path is based on a (predictor, threshold) pair ( ⁇ >, T 0 ) that defines the split of the root node, ( ⁇ 1; r ⁇ that defines the split of node 1, ⁇ d ( ⁇ 4 , Tn) that defines the split of node 4.
  • the new case follows this path as a result of its predictor values, in sequence:
  • Prediction follows by estimating T ⁇ * based on the sequence of conditionally independent posterior distributions for the branch probabilities that define it For example, simply "pluggmg-in" the conditional posterior means of each ⁇ will lead to a plug-m estimate of ⁇ * and hence ⁇ *
  • the full posterior for T ⁇ * IS defined implicitly as it is a function of the ⁇ Since the branch probabilities follow beta posteriors, it is trivial to draw Monte Carlo samples of the ⁇ and then simply compute the corresponding values of ⁇ * and hence it* to generate a posterior sample for summarization This way, we can evaluate simulation-based posterior means and uncertainty intervals for T ⁇ * that represent predictions of the binary outcome for the new case
  • the forward generation process allows easily for the computation of the resulting relative likelihood values for trees, and hence to relevant weighting of trees in prediction.
  • the overall marginal likelihood function for the tree is then the product of component marginal likelihoods, one component from each of these split nodes.
  • the overall marginal likelihood value is the product of these terms over all nodes j that define branches in the tree. This provides the relative likelihood values for all trees within the set of trees generated. As a first reference analysis, we may simply normalize these values to provide relative posterior probabilities over trees based on an assumed uniform prior. This provides a reference weighting that can be used to both assess trees and as posterior probabilities with which to weight and average predictions for future cases.
  • Example 1 Analysis of Biscuit Dough Data A first example concerns the application of biscuit dough data (publicly available at
  • the data set provides 78 samples, of which 39 are taken as training data and the remaining 39 as validation cases to be predicted, precisely as in Brown et al (1999).
  • the binary outcome is 0/1 according to whether the measured fat content exceeds a threshold, where the threshold is the mean of the sample of fat values.
  • the analysis was developed repeatedly, exploring aspects of model fit and prediction of the validation sample as the number of control parameters were varied.
  • the particular parameters of key interest varied were the Bayes' factor thresholds that define splits, and controls on the number of such splits that may be made at any one node It was determined that across ranges of these control parameters, that there was a good degree of robustness
  • the Bayes' factor threshold was fixed at 3 on the log scale, after which and two-level trees were explored allowing at most 10 splits of the root node and then at most 4 splits of each of nodes 1 and 2 This allowed up to 160 trees, with this analysis generating 148 trees
  • Figure 1 represents one of the 148 trees, split at the root node by the spectral predictor labeled factor 92 (corresponding to a wavelength of 1566 nm) Multiple wavelength values appear in the 148 trees, with values close to this appearing commonly, reflecting the underlying continuity of the spectra
  • the key second level predictor is factor 305, one of the principal component predictors
  • the data are scatter plotted on these two predictors in Figure 2 with corresponding levels of the predictor-specific thresholds from this tree marked The data appears also against the three predictors in this tree in Figure 3
  • m terms of posterior predictive probabilities for the 39 validation samples, accuracy is good
  • n 49 samples used in the binary regression analysis described in West et al (2001) is analyzed in this study, using predictors based on metagene summaries of the expression levels of many genes Metagenes are useful aggregate, summary measures of gene expression profiles
  • the evaluation and summarization of large-scale gene expression data in terms of lower dimensional factors of some form is utilized for two mam purposes first, to reduce dimension from typically several thousand, or tens of thousands of genes to a more practical dimension, second, to identify multiple underlying "patterns" of variation across samples that small subsets of genes share, and that characterize the diversity of patterns evidenced in the full sample
  • a cluster- factor approach is used here to define empirical metagenes This defines the predictor variables x utilized in the tree model Metagenes may be obtained by combining clustering with empirical factor methods
  • the metagene summaries used m the ER example were based on the following steps
  • the original data was developed using Affymetrix arrays with 7129 sequences, of which 7070 were used (following removal of Affymetrix controls from the data )
  • the expression estimates used were Iog2 values of the signal intensity measures computed using the dChip software for post-processing Affymetrix output data (See Li, C and Wong, W H Model-based analysis of oligonucleotide arrays Expression index computation and outlier detection Proc Natl Acad Sa , 98, 31-36 (2001)
  • the corresponding p metagenes were then evaluated as the dominant singular factors of each of these cluster, as referenced above
  • the data comprised 40 training samples and 9 validation cases Among the latter, 3 were initial training samples that presented conflicting laboratory tests of the ER protein levels, so casting into question their actual ER status, these were therefore placed in the validation sample to be predicted, along with
  • the current tiee model identifies several metagene patterns that together combine to define an ER profile of tumors, and that when displayed as m Figures 4 and 5 isolate these three cases as quite clearly consistent with their designated ER negative status in some aspects, yet conflicting and much more m agreement with the ER positive patterns on others Metagene 347 is the dominant ER signature, the genes involved in defining this metagene include two representations of the ER gene, and several other genes that are coregulated with, or regulated by, the ER gene Many of these genes appeared in the dominant factor in the regression piediction This metagene strongly discriminates the ER 11 negatives from positives, with several samples in the mid-range Thus, it is no surprise that this metagene shows up as defining root node splits in many high- hkelihood trees This metagene also clearly defines these three cases - 16, 40 and 43 - as appropriately ER negative However, a second ER associated metagene, number 352, also defines a significant discrimination In this dimension, however,
  • the tiee model analysis here identifies multiple interacting patterns and allows easy access to displays such as those shown in Figures 4 to 6 that provide insights into the interactions, and hence to interpretation of individual cases
  • predictions based on averaging multiple trees are in fact dominated by the root level splits on metagene 347, with all trees generated extending to two levels where additional metagenes define subsidiary branches Due to the dominance of metagene 347, the three interesting cases noted above are perfectly in accord with ER negative status, and so are well predicted, even though they exhibit additional, subsidiary patterns of ER associated behavior identified in the figures
  • Figure 6 displays summary predictions
  • the 9 validation cases are predicted based on the analysis of the full set of 40 training cases
  • Predictions are represented m terms of point predictions of ER positive status with accompanying, approximate 90% intervals from the average of multiple tree models
  • the training cases are each predicted in an honest, cross-validation sense each tumor is removed from the data set, the tree model is then refitted completely to the remaining 39 training cases only, and the hold-out case is predicted, i e , treated as a validation sample Excellent predictive performance is observed for both these one-at-a-time honest predictions of training samples and for the out of sample predictions of the 9 validation cases
  • One ER negative, sample 31 is firmly predicted as having metagene expression patterns completely consistent with ER positive status This is m fact one of the three cases for which the two laboratory tests conflicted The other two such cases, however agree with the initial ER negative test result - number 33, for which the predictions firmly agree with the initial ER negative test result, and number 14, for which the predictions agree with the initial ER positive result though
  • Example 3 Prediction of Lymph Node Metastases and Cancer Recurrence This study assesses complex, multivariate patterns in gene expression data from primary breast tumor samples that can accurately predict nodal metastatic states and relapse for the individual patient using the statistical tree model of the invention
  • DNA microarray data on samples of primary breast tumors was generated to which non ⁇ linear statistical analyses embodied by the tree model of the invention was applied to evaluate multiple patterns of interactions of groups of genes that have true predictive value, at the individual patient level, with respect to lymph node metastasis and cancer recurrence
  • patterns of gene expression were identified that associate with outcome Much more importantly, these patterns were capable of honestly predicting outcomes in individual patients with about 90% accuracy, based on a simple threshold of 0 5 probability m each case
  • the metagenes that predict lymph node metastasis and recurrence identify distinct groups of genes, suggesting different biological processes underlying these two characteristics of breast cancer
  • the binary prediction tree model was applied to the analysis of gene expression patterns m primary breast tumors that predict lymph node metastasis, as well as tumor recurrence
  • the first study compares traditional "low-risk” versus "high-risk” patients, primarily based on age, primary tumor size, lymph node status, and Estrogen receptor ("ER") status Among ER positive individuals, the "high-risk” clinical profile is represented by advanced lymph node metastases (10 or more positive nodes), the "low-risk profile” identifies node-negative women of age greater than 40 yeais with tumor size below 2cm
  • Expression data were generated and metagenes identified and used in the Bayesian statistical tree analysis
  • Figure 7 displays summary predictions from the resulting total of 37 cross-validation analyses For each individual tumor, this graph illustrates the predicted probability for "high-risk” versus "low-risk” (red versus blue) together with an approximate 90% confidence interval, based on analysis of the 36
  • the second frame (upper right) shows that low-risk is consistent with low levels of metagene 130 or high levels of metagene 146; hence, cases 1 and 3 are not inconsistent in the overall pattern, though case 11 is consistent.
  • An analysis that selects one set of genes, summarized here as one metagene, as a "predictor" would be potentially misleading, as it ignores the broader picture of multiple interlocked genomic patterns that together characterize a state.
  • these two metagenes play key roles: low levels of metagene 146 coupled with higher levels of metagene 130 are strongly predictive of high-risk cases. Combined use of multiple metagenes, in the context of the tree selection model building process, ultimately yields a pattern that has the capacity to accurately predict the clinical outcome.
  • the second analysis concerns 3 year recurrence following primary surgery among the challenging and varied subset of patients with 1-3 positive lymph nodes. Such patients typically receive adjuvant chemotherapy alone, but more than 20% suffer relapse within five years. Hence, improved prognosis for this heterogeneous group is of critical importance; patients identified with a high probability of relapse could be targeted for more intensive treatment.
  • the dataset provided 52 ER-positive cases in this lymph node category (34 non-recurrent, 18 recurrent).
  • the aggregate predictions from the sets of generated statistical tree models defines a rather accurate picture; once again, there is an approximate 90% overall predictive accuracy in the 52 separate one-at-a-time, cross-validation prediction assessments as shown in Figure 9.
  • the tree model identified subsets of genes related to the metagene predictors of lymph node involvement. These are replete with those involved in cellular immunity, including a high proportion of genes that function in the interferon pathway. They include genes that are induced by interferon such as various chemokines and chemokine receptors (Rantes, CXCLlO, CCR2), other interferon- induced genes (IFI30, IFI35, IFI27, IFITl, IFIT4, IFITM3), as well as interferon effectors (2'-5' oligoA synthetase), and genes encoding proteins mediating the induction of these genes in response to interferon (STATl and IRFl).
  • interferon such as various chemokines and chemokine receptors (Rantes, CXCLlO, CCR2), other interferon- induced genes (IFI30, IFI35, IFI27, IFITl, IFIT4, IFITM3), as well as interferon effectors (2
  • genes implicated in recurrence prediction as identified by the tree model do not exhibit such a striking functional clustering but do include many examples previously associated with breast cancer Moreover, this group of genes is clearly distinct set from those that predict lymph node involvement They include genes associated with cell proliferation control, both cell cycle specific activities (CDKN2D, Cyclm F, E2F4, DNA p ⁇ mase, DNA ligase), more general cell growth and signaling activities (MK2, JAK3, MAPK8IP, and EFl D), and a number of growth factor receptors and G-protem coupled receptors, some of which have been shown to facilitate breast tumor growth (EpoR) Possibly, the poor prognosis with respect to survival reflects a more vigorous proliferative capacity of the tumor
  • the genes implicated in the prediction of lymph node metastasis and overall recurrence of disease although clearly representing interrelated phenomena, nevertheless reflect the participation of distinct biological processes
  • the tree model is thus flexible in that regard as it only selects those metagenes that are most relevant to the prediction in hand
  • traditional statistical testing perspectives that focus on significant differences at a population parameter level may say little of practical significance in terms of an individual patient's prognosis
  • the tree model takes into account the relevant multiple features of the complex patterns of gene expression, especially in a context such as breast cancer where multiple, interacting biological and environmental processes define physiological states, and individual dimensions provide only partial information
  • the tree model of the present invention assesses the complex, multivariate patterns in gene expression data from primary tumor biopsies, exploring the value of such patterns m predicting lymph node metastasis and relapse, two critically important aspects of breast cancer, at the individual patient level
  • the tree model identifies multivariate patterns of gene expression that, m this realistic context of substantial patient heterogeneity, deliver predictive accuracy of about 90%
  • the above gene expression analysis approach to the identification of atherosclerotic phenotype determinative genes may be combined with one or more additional selection protocols m a "multi-prong" gene selection approach for identifying genes associated with an atherosclerotic phenotype
  • Additional selection protocols that can be employed in conjunction with the subject selection protocol include (1) selection protocols that identify all currently known genes that are associated with atherosclerosis (e g , as determined by using existing biological and clinical databases, e g , by performing a thorough review of the published literature concerning biological research on atherosclerosis mechanism and clinical research related to drugs that have shown a beneficial, or detrimental, effect on patients with atherosclerotic clinical manifestations), (2) genes that have been identified as associated with atherosclerosis using human genetic studies, e g , genetic linkage analysis (for example, one analyzes the genome of individuals who have presented with premature coronary heart disease (CAD, hard manifestations of CAD before 45 for men and before 50 for women, such as myocardial infarction or bypass surgery), and their siblings and studies markers
  • Figure 6 provides a flow diagram showing a selection procedure as described above as it would be used to identify atherosclerotic phenotype determinative gene variants, e g , SNPs, which are then used, either singly or in combination, in a variety of different applications, including the applications described above in connection with the specific atherosclerotic phenotype determinative genes identified herein
  • the "minimally diseased” group showed less than 5% Sudan IV staining and contained no raised lesions
  • the size of a particular section used in the analysis was quite small, on average 10mm by 5mm, making Sudanaphiha and raised lesion content homogeneous throughout the section
  • the second phenotype was the location of the section within the thoracic aorta as a surrogate for disease susceptibility This assumption is based on the conclusive evidence from the PDAY study that progression of disease advances from the distal to proximal areas of the aorta suggesting that distal regions are more susceptible to disease development See Cornhill JF, et al Topography of human aortic suda ⁇ ophilic lesions Monogr Atheroscler 1990,15 13-19 As stated above, applicants analyzed sections from identical locations in all the aortas There were 31 proximal (IA) sections and 32 distal (4B) sections in our analysis of aorta location Applicants used the same pool of aorta sections for both analyses IV. Statistical analysis.
  • the second analysis identified gene signatures associated with the proximal and distal locations within the thoracic aorta as a possible metric of atherosclerotic susceptibility There were no significant difference in the characteristics of the two locations in either Sudan IV staining (12 4% ⁇ 3.0% vs. 12.6%+3.1%) or raised lesions (10.1% ⁇ 4.7% vs. 13.2% ⁇ 5.3%). There was also no significant difference in the gender or ages of the donor pools an shown on the following table:
  • Sudan IV Staining percent of the total aorta that is stained.
  • Figure 1 1 displays the results from the analysis of disease severity where the predictive model correctly classifies 93 5% (29 of 31 sections) of the sections as minimally or severely diseased based solely upon their gene expression profiles
  • This figure shows results of the hold-one-out cross validation analysis where applicants construct the model from 30 samples and use it to predict the phenotype of the 31 st sample
  • the plot represents the probability that the unknown sample is severely diseased
  • the red numbers represent the severely diseased section with 95% confidence intervals
  • the blue numbers represent minimally diseased samples
  • the gene p ⁇ o ⁇ tization process identified a set of 208 genes whose expression patterns provide the power to discriminate and predict disease states in our aorta samples as shown on Table I below which includes Genbank Deposit numbers and Unigene designations
  • CAPG capping protein actm filament
  • gelsohn-like Hs 82422 solute carrier family 21 organic anion transporter
  • TAF4 (TBP)-associated factor 135 kD Hs.24644
  • HLA-DMA major histocompatibility complex, class II, DM alpha Hs.77522 leukocyte immunoglobulin-like receptor, subfamily B
  • CD36 antigen (collagen type I receptor, thrombospondin
  • AF079167 OLRl oxidised low density lipoprotein (lectin-like) receptor 1 Hs.77729 alanyl (membrane) aminopeptidase (aminopeptidase N, aminopeptidase M, microsomal aminopeptidase, CD 13,
  • J04621 SDC2 syndecan 2 (heparan sulfate proteoglycan 1, cell surface- Hs.1501 associated, fibroglycan)
  • Y14768 LTA lymphotoxm alpha (TNF superfamily, member 1) Hs 36 ATPase, H+ transporting, lysosomal 13kD, Vl subunit G
  • AF022797 KCNN4 activated channel, subfamily N, member 4 Hs.10082
  • SH3BGR SH3 domain binding glutamic acid-rich protein Hs.47438 fibroblast growth factor receptor 2 bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome,
  • TNFRSFlB tumor necrosis factor receptor superfamily member 1 B Hs.256278 small inducible cytokine subfamily A (Cys-Cys)
  • Table I encode proteins previously suspected to play a role in atherosclerosis including apolipoprotem E (apoE), osteopontm, and the oxidized LDL receptor 1 (olrl) Applicants performed a query against gene ontology databases to determine the important biological processes represented in the analysis Applicants found that the genes reflected processes that applicants would infer from our current understanding of atherosclerosis such as cell cycle regulation and inflammatory response Genes in these categories without direct links to atherosclerosis could be novel candidates for study Such genes include capg, gm2 ganghoside activator protein, matrix metalloproteinase 9 (mmp9) and chemokme (C-C motif) receptor-like 2 (ccrll) Genes from table I were classified according to biological process as follows
  • Apoptosis accessory protein BAP31 dedicator of cyto-kmesis 2, hematopoietic protein 1, secreted f ⁇ zzled-related protein 1, tumor necrosis factor receptor superfamily, member IB, tumor protein p53 binding protein, 2
  • Cell Motility GRB2-associated binding protein 2 KIAA0429 gene product, lymphocyte-specific protein 1, myosin, heavy polypeptide 11, smooth muscle, plasminogen activator, urokinase receptor, profilm 2, talm 2
  • breast cancer metastasis-suppressor 1 follicular lymphoma variant translocation 1 Immune Response accessory protein BAP31, capping protein, gelsolin-like, cathepsm L, CD2 antigen (p50), sheep red blood cell receptor, complement component 1, q subcomponent, complement component 2, dedicator of cyto-kmesis 2, immunoglobulin lambda constant 6, leukocyte lmmunoglobulin-hke receptor, lymphocyte cytosohc protein 2, MHC, class II, DM alpha, MHC, class II, DM beta, MHC, class II, DQ beta 1, MHC, class II, DR alpha, MHC, class II, DR beta 1, osteopontm, TAP binding protein, Wiskott-Aldrich syndrome
  • Inflammatory Response allograft inflammatory factor 1, arachidonate 5-hpoxygenase, ATPase, H+ transporting, lysosomal 13IcD, carboxypeptidase A3, chemokme (C-X-C motif), receptor 4 , granulin, HLA-B associated transcript 1, interleukin 10 receptor, alpha, interleukin 11 receptor, alpha, lymphotoxin alpha (TNF superfamily, member 1), lymphotoxin beta (TNF superfamily, member 3), small inducible cytokine A3, small inducible cytokine A5, small inducible cytokine subfamily A, m 18, thromboxane A synthase 1 , tumor necrosis factor receptor superfamily, member IB.
  • Chemotaxis endothelial cell growth factor 1, lymphocyte-specific protein 1, plasminogen activator, urokinase receptor.
  • Cell Signaling adenylate cyclase 9, ADP- ⁇ bosylation factor-like 7, chemokme (C-X-C motif), receptor 4, cyclin-dependent kinase inhibitor IB, discs, large (Drosophila) homolog 5, fibroblast growth factor receptor 2 , mitogen-activated protein kinase 13, mitogen-activated protein kmase-activated protein kinase 3, osteopontm, regulator of G-protein signaling 1, regulator of G- protem signaling 19, regulator of G-protem signaling 5, Rho GTPase activating protein 4, SH3 domain binding glutamic acid-rich protein, Src-1 ike- adaptor, tumor protein p53 binding protein, 2 Regulation of Transcription, chromobox homolog 1, endothelial cell growth factor 1, Hl histone family, member X, hematopoietic cell-specific Lyn substrate 1, lntegrin, beta 4, KIAA0363 protein, KIAA0363 protein, nuclear receptor
  • Cytoskeleton/Structural Component leiomodm 1, Lysosomal-associated multispanning membrane protein-5, reticulon 1, Rho GTPase activating protein 4, sarcoglycan, epsilon, solute carrier family 16, member 3, vesicle-associated membrane protein 8, Wolfram syndrome lL ⁇ id Metabolism: apolipoprotein E, L-3-hydroxyacyl-Coenzyme A dehydrogenase, short chain.
  • Carbohydrate Metabolism aldo-keto reductase family 1, member Bl, fructose- 1 ,6- bisphosphatase 1, galactosidase, beta 1, hexokmase 3, solute carrier family 2 member 5.
  • FIG. 12 is a plot of the hold-one-out cross validation analysis that shows the probability that an unknown sample is from the distal aorta with 95% confidence intervals.
  • the red numbers represent samples from the distal location; the blue numbers are from the proximal aorta.
  • Figure 13 shows expression levels by color display of the genes in the key predictive metagene and illustrates the differential expression patterns between proximal and distal tissues.
  • HSPA6 heat shock 7OkD protein 6 HSPA6 heat shock 7OkD protein 6 (HSP70B 1 )
  • nuclear receptor subfamily 4 nuclear receptor subfamily 4, group A, member 2, homeo box A4, msh homeo box homolog 1, transcription factor AP-2 alpha, forkhead box Dl, homeo box B7, homeo box D4, homeo box C6.
  • Inflammatory Response protein C receptor, endothelial.
  • X14830 Human mRNA for muscle acetylcholine receptor beta-;
  • Cluster Incl. M2821 l:Homo sapiens GTP-binding protein (RAB4) mRNA, comp; 39280_at Cluster Incl. U80744:Homo sapiens CTG4a mRNA, complete cds /cds (387,81 ; 37604_at Cluster Incl.
  • RAB4 Homo sapiens GTP-binding protein
  • U72511 Human B-cell receptor associated protein (hBAP) mR; 37705_at Cluster Incl.
  • AF020543 Homo sapiens palmitoyl-protein thioesterase-2 (P; 41579_s_at Cluster Incl.
  • AF058925 Homo sapiens Jak2 kinase mRNA, complete cds
  • ABOl 1165 Homo sapiens mRNA for KIAA0593 protein, partial ; 34668_at Cluster Incl.
  • D88152 Homo sapiens mRNA for acetyl-coenzyme A transporte; 35139_at Cluster Incl.
  • AF035119 Homo sapiens deleted in liver cancer-1 (DLC-I) m; 41727_at Cluster Incl.
  • AB023224 Homo sapiens mRNA for KIAAl 007 protein, partial ; 34792_at Cluster Incl.
  • Y09443 H.sapiens mRNA for alkyl-dihydroxyacetonephosphate; 41373_s_at Cluster Incl.
  • AF027516 Homo sapiens trans-golgi network glycoprotein ; 34217_at Cluster Incl.
  • COPA Homo sapiens coatomer protein
  • AF038897 Homo sapiens syntaxin 16 mRNA, complete cds /cds; 3953 l_at Cluster Incl.
  • L06237 Human microtubule- associated protein IB (MAPlB) ge; 40581_at Cluster Incl.
  • U88629 Human RNA polymerase II elongation factor ELL2, co; 41287_s_at Cluster Incl.
  • /cds (; 35571_at Cluster Incl. AF055917:Homo sapiens protease-activated receptor 4 mRNA,; 35884_at Cluster Incl. Y07829:Homo sapiens RFB30 gene for RING finger protein /c; 40042_r_at
  • U06452 Human melanoma antigen recognized by T-cells (MART; 32106_at Cluster Incl.
  • L28101 Homo sapiens kallistatin (PI4) gene, exons 1-4, co; 35993_s_at Cluster Incl.
  • U81504 Homo sapiens beta-3A-adaptin subunit of the AP-3 c; 33260_at Cluster Incl.
  • L13857 Human guanine nucleotide exchange factor mRNA, com; 33710_at Cluster Incl.
  • AF058696 Homo sapiens cell cycle regulatory protein p95 (; 35184_at Cluster Incl.
  • ABOl 1118 Homo sapiens mRNA for KIAA0546 protein, partial ; 35618_at Cluster Incl.
  • ABOl 1141 Homo sapiens mRNA for KIAA0569 protein, comple; 36048_at Cluster Incl.
  • ABOl 1084 Homo sapiens mRNA for KIAA0512 protein, complete; 36080_at Cluster Incl.
  • AB023213 Homo sapiens mRNA for KIAA0996 protein, complete; 36527_at Cluster Incl.
  • U90920 Human PTPLl -associated RhoGAP mRNA, complete cds /; 38252_s_at Cluster Incl.
  • U84007 Human glycogen debranching enzyme isoform 1 (AGL; 38253_at Cluster Incl.
  • U84011 Human glycogen debranching enzyme isoform 6 (AGL) ; 38270_at Cluster Incl.
  • AF005043 Homo sapiens poly(ADP-ribose) glycohydrolase (hP; 38626_at Cluster Incl.
  • AL050282 Homo sapiens mRNA; cDNA DKFZp586H2219 (from cl; 39706_at Cluster Incl.
  • AB014536 Homo sapiens mRNA for KIAA0636 protein, complete; 39776_at Cluster Incl.
  • AB014523 Homo sapiens mRNA for KIAA0623 protein, complete; 39785_at Cluster Incl.
  • AF046024 Homo sapiens UBA3 (UBA3) mRNA, complete cds /cds; 40129_at Cluster Incl.
  • U47077 Human DNA-dependent protein kinase catalytic subun; 40404_s_at Cluster Incl.
  • U66615 Human SWI/SNF complex 155 KDa subunit (BAF155) mRN; 40844_at Cluster Incl.
  • OS-4 protein Homo sapiens OS-4 protein (OS-4) mRNA, complet; 33365_at Cluster Incl.
  • AB023162 Homo sapiens mRNA for KIAA0945 protein, complete; 33870_at Cluster Incl.
  • AB029005 Homo sapiens mRNA for KIAA1082 protein, partial ; 33899_at Cluster Incl.
  • U34252 Human gamma-aminobutyraldehyde dehydrogenase mRNA,; 34312_at Cluster Incl.
  • AF068227 Homo sapiens putative transmembrane protein (CLN; 34327_at Cluster Incl.
  • Z46606 H.sapiens HLTF gene for helicase-like transcriptio; 34825_at Cluster Incl.
  • AL031775:dJ30M3.3 (novel protein similar to C. elegans Y6; 35289_at Cluster Incl.
  • AJ131245 Homo sapiens mRNA for Sec24 protein (Sec24B isof; 36588_at Cluster Incl.
  • AB018353 Homo sapiens mRNA for KIAA0810 protein, partial ; 36596_r_at Cluster Incl.
  • AF002697 Homo sapiens ElB 19K/Bcl-2- binding protein Nip3 ; 38436_at Cluster Incl.
  • D86981 Human mRNA for KIAA0228 gene, partial cds
  • /cds (; 38727_at Cluster Incl.
  • M23161:Human transposon-like element mRNA /cds UNKNOWN /g; 38763_at Cluster Incl.
  • M55265 Human casein kinase II alpha subunit mRNA, complet; 33113_at Cluster Incl.
  • /cds UNKN; 38512_r_at Cluster Incl.
  • Cluster Incl AF027204:Homo sapiens putative tetraspan transmembrane pr; 39242_at Cluster Incl.
  • U36221 Human pancreatic zymogen granule membrane protein ; 40288_r_at Cluster Incl.
  • M35531 Human GDP-L-fucose-beta-D-galactoside 2-alpha-l-fu; 41036_at Cluster Incl.
  • AB016869 Homo sapiens mRNA for p70 ribosomal S6 kinase be; 41694_at Cluster Incl.
  • AB023220 Homo sapiens mRNA for KIAA1003 protein, complete; 37210_at Cluster Incl.
  • L36531 Homo sapiens integrin alpha 8 subunit mRNA, 3 end; 41091_at Cluster Incl.
  • U05237 Human fetal Alz-50-reactive clone 1 (FACl) mRNA, c; 41466_s_at Cluster Incl.
  • L04282 Human CACCC box-binding protein mRNA, complete c; 32129_at Cluster Incl.
  • AL079314 Homo sapiens mRNA full length insert cDNA clone ; 32734_at Cluster Incl.
  • L76703 Horno sapiens protein phosphatase 2A B56-epsilon (P; 35725_at Cluster Incl.
  • D89618 Homo sapiens mRNA for karyopherin alhph 3, complet; 35985_at Cluster Incl.
  • AB023137 Homo sapiens mRNA for KIAA0920 protein, complete; 38639_at Cluster Incl.
  • AF040963 Homo sapiens Mad4 homolog (Mad4) mRNA, complete ; 39419_at Cluster Incl.
  • ABOl 1088 Homo sapiens mRNA for KIAA0516 protein, partial ; 40463_at Cluster Incl.
  • AL021396 Human DNA sequence from clone 971N18 on chromoso; 32159_at Cluster Incl.
  • L00049 Human cellular c-Ki-ras2 proto-oncogene, 5 flank a; 32815_at Cluster Incl.
  • Homo sapiens cDNA, 3 end /clone IMAG; 33381_at Cluster Incl.
  • AF012108 Homo sapiens Amplified in Breast Cancer (AIBl) m; 35295_g_at Cluster Incl.
  • Z36715:H.sapiens mRNA for Net transcription factor /cds (; 31833_at Cluster Incl.
  • J03191:Human profilin mRNA, complete cds /cds (127,549); 36977_at Cluster Incl.
  • U41745 Human PDGF associated protein mRNA, complete cds /; 39182_at Cluster Incl.
  • HNMP- Human hematopoietic neural membrane protein
  • X66363 H.sapiens mRNA PCTAIRE-I for serine/threonine prot; 39835_at Cluster Incl.
  • AL109700 Homo sapiens mRNA full length insert cDNA clone ; 39939_at Cluster Incl.
  • AL033538 Human DNA sequence from clone 477H23 on chromoso; 32674_at Cluster Incl.
  • D83032 Homo sapiens mRNA for nuclear protein, NP220, comp; 33255_at Cluster Incl.
  • M97856 Homo sapiens histone-binding protein mRNA, complet; 35142_at Cluster Incl.
  • AF001691 Homo sapiens 195 ItDa cornified envelope precurso; 38681_at Cluster Incl.
  • AF051850 Homo sapiens supervillin mRNA, complete cds /cds; 40828_at Cluster Incl.
  • AF084260 Homo sapiens signalosome subunit 2 (SGN2) mRNA, ; 35778_at Cluster Incl.
  • TACC2 protein TACC2 protein
  • L31881 Human nuclear factor I-X mRNA, complete cds /cds; 32509_at Cluster Incl.
  • M99578 Human lymphocyte surface protein exons 1-5, comp; 32274_r_at Cluster Incl.
  • M65214 Human (HeLa) helix-loop-helix protein HE47 (E2A) m; 32907_at Cluster Incl.
  • L41147 Homo sapiens 5-HT6 serotonin receptor mRNA, comple; 3591 l_r_at Cluster Incl.
  • AJ003147 Homo sapiens complete genomic sequence between; 37095_r_at Cluster Incl.
  • M84562 Human formyl peptide receptor-like receptor (FPR; 37414_at Cluster Incl.
  • AL050378 Homo sapiens mRNA; cDNA DKFZp586I1420 (from clon; 39960_at Cluster Incl.
  • AF091086 Homo sapiens clone 640 unknown mRNA, complete se; 40650_r_at Cluster Incl.
  • X72304 H.sapiens mRNA for corticotrophin releasing fact; 41383_at Cluster Incl.
  • AJ001403 Homo sapiens mNRA for MUC5AC protein (placental); 31804_f_at Cluster Incl.
  • X78283 H.sapiens mRNA for aryl sulfotransferase (ST1A3); 34221_at Cluster Incl.
  • AF040708 Homo sapiens candidate tumor suppressor gene 2; 32756_at
  • AF002163 Homo sapiens delta-adaptin mRNA, complete cds ; 38056_at Cluster Incl.
  • U40998 Human retinal protein (HRG4) mRNA, complete cds /c; 38414_at Cluster Incl.
  • U08377 Human homolog of Drosophila splicing regulator sup; 38741_at Cluster Incl.
  • AC004410:Homo sapiens chromosome 19, fosmid 39554 /cds (O; 35434_at Cluster Incl.
  • AF055033 Homo sapiens clone 24645 insulin-like growth fac; 33806_at Cluster Incl.
  • X66362 H.sapiens mRNA PCTAIRE-3 for serine/threonine prot; 38722_at Cluster Incl.
  • X15880 Human mRNA for collagen VI alpha- 1 C-terminal glob; 40873_at Cluster Incl.
  • L13329 Homo sapiens iduronate-2-sulfatase (IDS) gene /cds; 40972_at Cluster Incl.
  • IDS iduronate-2-sulfatase
  • AB021288 Homo sapiens mRNA for beta 2-microglobulin, comp; 37497_at Cluster Incl. L16499:Human orphan homeobox protein (PRH) mRNA, complete; 41405_at Cluster Incl.
  • AF026692 Homo sapiens frizzled related protein frpHE mRNA; 32675_at Cluster Incl.
  • AF060228 Homo sapiens retinoic acid receptor responder 3 ; 36569_at Cluster Incl.
  • ABOOOl 15 Homo sapiens mRNA expressed in osteoblast, compl; 3764 l_at Cluster Incl.
  • D28915 Human gene for hepatitis C-associated microtubular; 37975_at Cluster Incl.
  • X0401 l Human mRNA of X-CGD gene involved in chronic granu; 39409_at Cluster Incl.
  • Cluster Incl. AB002445:Homo sapiens mRNA from chromosome 5q21-22, clone; 35199_at Cluster Incl. AB023199:Homo sapiens mRNA for KIAA0982 protein, complete; 35252_at Cluster Incl.
  • ABOl 1100 Homo sapiens mRNA for KIAA0528 protein, complete; 35695_at Cluster Incl.
  • U67615 Human beige protein homolog (chs) mRNA, complete c; 35722_at Cluster Incl.
  • AF006010 Human progestin induced protein (DD5) mRNA, comp; 39354_at Cluster Incl.
  • AF054284 Homo sapiens spliceosomal protein SAP 155 mRNA, ; 39699_at Cluster Incl.
  • U22897 Homo sapiens nuclear domain 10 protein (ndp52) mRN; 40102_at Cluster Incl.
  • AB018315 Homo sapiens mRNA for KIAA0772 protein, complete; 40832_s_at Cluster Incl.
  • AL050126 Homo sapiens mRNA; cDNA DKFZp586G011 (from clo; 40839_at Cluster Incl.
  • AL080177 Homo sapiens mRNA; cDNA DKFZp434K151 (from clone; 40868_at Cluster Incl.
  • AF038186:Homo sapiens clone 23914 mRNA sequence /cds UNKN; 32798_at Cluster Incl.
  • AF043105 Homo sapiens glutathione S-transferase mu 3 (GST; 32835_at Cluster Incl.
  • AB018327 Homo sapiens mRNA for KIAA0784 protein, partial ; 34397_at Cluster Incl.
  • AF069250 Homo sapiens okadaic acid-inducible phosphoprote; 34785_at Cluster Incl.
  • AB028948 Homo sapiens mRNA for KIAA1025 protein, partial ; 34786_at Cluster Incl.
  • AF014402 Homo sapiens type-2 phosphatidic acid phosphatas; 35317_at Cluster Incl.
  • AB014579 Homo sapiens mRNA for KIAA0679 protein, partial ; 35802_at Cluster Incl.
  • AB023231 Homo sapiens mRNA for KIAA1014 protein, partial ; 37007_at Cluster Incl.
  • AL080234 Homo sapiens mRNA; cDNA DKFZp586L081 (from clone; 38405_at Cluster Incl.
  • N36997:yy39g07.sl Homo sapiens cDNA, 3 end /clone IMAGE-; 41333_at Cluster Incl.
  • D26069:Human mRNA for KIAA0041 gene, partial cds /cds (0,; 41488_at Cluster Incl.
  • AC002394 Human Chromosome 16 BAC clone CIT987SK-A-211C6 /; 32597_at Cluster Incl.
  • AF058718 Homo sapiens putative 13 S Golgi transport compl; 35709_at Cluster Incl.
  • AF084513 Homo sapiens DNA repair exonuclease (RECl) mRNA,; 36926_at Cluster Incl.
  • AL080212 Homo sapiens mRNA; cDNA DKFZp586H0723 (from clon; 41174_at Cluster Incl.
  • AF012086 Homo sapiens Ran binding protein 2 (RanBP2alpha); 32217_at Cluster Incl.
  • AF039029 Homo sapiens snurportinl mRNA, complete cds /cds; 33830_at Cluster Incl.
  • Y10387:H.sapiens mRNA for PAPS synthetase /cds (36,1910) ; 35303_at Cluster Incl.
  • AB028980 Homo sapiens mRNA for KIAA1057 protein, partial ; 37306_at Cluster Incl.
  • U31383 Human G protein gamma-10 subunit mRNA, complete cd; 37737_at Cluster Incl.
  • D25547 Homo sapiens mRNA for PIMT isozyme I, complete cds; 38395_at Cluster Incl.
  • X61100 Human mRNA for mitochondrial 75 IcDa iron sulphur p; 39923_at Cluster Incl.
  • D86960:Human mRNA for KIAA0205 gene, complete cds /cds (2; 36099_at Cluster Incl.
  • M63180 Human threonyl-tRNA synthetase mRNA, complete cds ; 32563_at Cluster Incl.
  • AF095448 Homo sapiens putative G protein-coupled receptor; 36536_at Cluster Incl.
  • AF070614 Homo sapiens clone 24732 unknown mRNA, partial c; 38704_at Cluster Incl.
  • AB007934 Homo sapiens mRNA for KIAA0465 protein, partial ; 39329_at Cluster Incl.
  • AL050021 Homo sapiens mRNA; cDNA DKFZp564D016 (from clone; 40785_g_at Cluster Incl.
  • Z69030 H.sapiens mRNA for gamma 1 isoform of 61IdDa regu; 41137_at Cluster Incl.
  • AB007972 Homo sapiens mRNA, chromosome 1 specific transcr; 34800_at Cluster Incl.
  • Cluster Incl. AF087036:Homo sapiens musculin mRNA, partial cds /cds (O,; 38618_at Cluster Incl.
  • X16302 Human mRNA for insulin-like growth factor binding ; 33925_at Cluster Incl.
  • D42123 Homo sapiens mRNA for ESP1/CRP2, complete cds /cds; 36950_at Cluster Incl.
  • X90872:H.sapiens mRNA for g ⁇ 25L2 protein /cds (91,735) /g; 40560_at Cluster Incl.
  • AL050259 Homo sapiens mRNA; cDNA DKFZp547D0710 (from clon; 39542_at Cluster Incl.
  • AF05961 l Homo sapiens nuclear matrix protein NRP/B (NRPB); 40614_at Cluster Incl.
  • Cluster Incl AL036554:DKFZp564J2262__rl Homo sapiens cDNA, 5 end /clon; 37204_at Cluster Incl. X67055:H. sapiens mRNA for inter-alpha-trypsin inhibitor h; 37950_at Cluster Incl.
  • AB006630:Homo sapiens mRNA for KIAA0292 gene, partial cds; 34703_f_at Cluster Incl. AA151971:zo30b03.rl Homo sapiens cDNA, 5 end /clone IM; 39412_at Cluster Incl.
  • AF072810 Homo sapiens transcription factor WSTF mRNA, com; 33371_s_at Cluster Incl.
  • U59877 Human low-Mr GTP-binding protein (RAB31) mRNA, c; 34839_at Cluster Incl.
  • AB029027 Homo sapiens mRNA for KIAAl 104 protein, complete; 3484 l_at Cluster Incl.
  • AC002544 Homo sapiens Chromosome 16 BAC clone CIT987SK-A-; 34849_at Cluster Incl.
  • L37368 Human (clone E5.1) RNA-binding protein mRNA, compl; 32588_s_at Cluster Incl.
  • AF040704 Homo sapiens putative tumor suppressor protein (; 40124_at Cluster Incl.
  • X82260 H.sapiens mRNA for RanGTPase activating protein 1 ; 41850_s_at Cluster Incl.
  • U63825 Human hepatitis delta antigen interacting protei.
  • D30612 Homo sapiens mRNA for repressor protein, partial c; 38251_at Cluster Incl.
  • AB023152 Homo sapiens mRNA for KIAA0935 protein, partial ; 32202_at Cluster Incl.
  • U67322 Human HBV associated factor (XAP4) mRNA, complete ; 37700_at Cluster Incl.
  • X92106:H.sa ⁇ iens mRNA for bleomycin hydrolase /cds (78,14; 38812_at Cluster Incl.
  • X79683:H.sapiens LAMB2 mRNA for beta2 laminin /cds (165,5; 39133_at Cluster Incl.
  • AF071748 Homo sapiens cathepsin F (CATSF) mRNA, complete ; 39861_at Cluster Incl.
  • CATSF cathepsin F
  • M98343 Homo sapiens amplaxin (EMSl) mRNA, complete cds /c; 40253_at Cluster Incl.
  • AJOl 1123 Homo sapiens mRNA for phosphatidylinositol 4-kin; 41251_at Cluster Incl.
  • L40410 Homo sapiens thyroid receptor interactor (TRIP3) m; 41530_at Cluster Incl.
  • D16294 Human mRNA for mitochondrial 3-oxoacyl-CoA thiolas; 32527_at Cluster Incl. AI381790:te41hl0.xl
  • Homo sapiens cDNA, 3 end /clone IMAG; 1746_s_at Tumor Necrosis Factor Receptor 2 Associated
  • 35651_at Cluster Incl AF002715 Homo sapiens MAP kinase kinase kinase (MTKl) mRN; 35683_at Cluster Incl AB020659 Homo sapiens mRNA for KIAA0852 protein, complete; 35994_at Cluster Incl AC002398 Human DNA from chromosome 19-specific cosmid F25; 38273_at Cluster Incl AJ006268 Homo sapiens mRNA for putative ATPase, partial /; 38679_g_at Cluster Incl AA733050 zg79bO5 si Homo sapiens cDNA, 3 end /clone 39; 39381_at Cluster Incl
  • /cds (l ; 35054_at Cluster Incl.
  • AF035278:Homo sapiens clone 23676 mRNA sequence /cds UNKN; 33471_g_at Cluster Incl.
  • /cds UN; 38880_at Cluster Incl.
  • Cluster Incl. L13972:Homo sapiens beta-galactoside alpha-2,3-sialyltr; 41113_at Cluster Incl. AI871396:wl81f07.xl Homo sapiens cDNA, 3 end /clone IMAG; 31858_at Cluster Incl.
  • N21470:yx57el 1.si Homo sapiens cDNA, 3 end /clone IMAGE-; 33329_at Cluster Incl.
  • AL049924 Homo sapiens mRNA; cDNA DKFZp547Gl 110 (from clon; 3281 l_at Cluster Incl.
  • X98507:H.sapiens mRNA for myosin-I beta /cds (65,3151) /g; 35812_at Cluster Incl.
  • AJ133769 Homo sapiens mRNA for nuclear transport receptor; 36210_g_at Cluster Incl.
  • Cluster Incl. Z35227:H.sapiens TTF mRNA for small G protein /cds (579,l; 37479_at Cluster Incl. M54992:Human B cell differentiation antigen mRNA, complet; 40364_at Cluster Incl.
  • U83460 Human high-affinity copper uptake protein (hCTRl) ; 40699_at Cluster Incl.
  • M12824 Human T-cell differentiation antigen Leu-2/T8 mRNA; 40738_at Cluster Incl.
  • M16336 Human T-cell surface antigen CD2 (TIl) mRNA, compl; 35698_at Cluster Incl.
  • Y00318 Human mRNA for complement control protein factor I; 36878_f_at Cluster Incl.
  • M60028 Human MHC class II HLA-DQ-beta (DQBl,DQw9), comp; 41764_at Cluster Incl.
  • AF052124 Homo sapiens clone 23810 osteopontin mRNA, com; 34362_at Cluster Incl.
  • M55531 Human glucose transport-like 5 (GLUT5) mRNA, compl; 35260_at Cluster Incl.
  • Y11731:H.sapiens mRNA for DNA glycosylase /cds (338,1375); 38996_at Cluster Incl.
  • /cds (286,114; 38590_r_at Cluster Incl.
  • M55543 Human guanylate binding protein isoform II (GBP-2); 34720_at Cluster Incl.
  • U85193 Human nuclear factor I-B2 (NFIB2) mRNA, complete c; 36918_at Cluster Incl.
  • Y15723 Homo sapiens mRNA for soluble guanylyl cyclase /cd; 39000_at Cluster Incl.
  • AF043324 Homo sapiens N-myristoyltransferase 1 mRNA, comp; 33890_at Cluster Incl.
  • U09813 Human mitochondrial ATP synthase subunit 9, P3 gen; 32544_s_at Cluster Incl.
  • ABOl 1539:Homo sapiens mRNA for MEGF6, partial cds /cds (O; 35347_at Cluster Incl.
  • M34455 Human interferon-gamma-inducible indoleamine 2,3- d; 37467_at Cluster Incl.
  • K02882 Human germline IgD chain gene, C-region, C-delta- 1 ; 34799_at
  • Cluster Incl. AF070643:Homo sapiens clone 24636 mRNA sequence /cds UNKN; 36205_at Cluster
  • Metagene 81; 31991_at Cluster Incl. AL049430:Homo sapiens mRNA; cDNA DKFZp586H201 (from clone; 3301 l_at Cluster Incl. Y10148:H.sapiens mRNA for NTR2 receptor /cds (36,1268) /g;
  • Cluster Incl AB026891:Homo sapiens mRNA for cystine/glutamate transpor; 34933_at Cluster Incl.
  • AJ238381 Homo sapiens pax9 gene, exons 1-2 and joined CDS; 36237_at Cluster Incl.
  • D78586 Human CAD mRNA for multifunctional protein CAD, co; 33224_at Cluster Incl.
  • AB007965 Homo sapiens mRNA, chromosome 1 specific transcr; 33281_at Cluster Incl.
  • AL096714 Homo sapiens mRNA; cDNA DKFZp564E242 (from clone; 39010_at Cluster Incl.
  • AB018335 Homo sapiens mRNA for KIAA0792 protein, complete; 40414_at Cluster Incl.
  • AB023221 Homo sapiens mRNA for KIAA1004 protein, partial ; 33842_at Cluster Incl.
  • AF074015 Homo sapiens integrin subunit alpha 10 precursor
  • L32961 Human 4-aminobutyrate aminotransferase (GABAT) mRN; 33464_at Cluster Incl.
  • AL 109703 Homo sapiens mRNA full length insert cDNA clone ; 41406_at Cluster Incl.
  • AL080172 Homo sapiens mRNA; cDNA DKFZp434G231 (from clone.
  • Metagene 83; 31347_at Cluster Incl. AF058075:Homo sapiens clone ASPBLL54 immunoglobulin lambd; 31512_at Cluster Incl. Z00010:H.sapiens germ line pseudogene for immunoglobulin ; 32904_at Cluster Incl. M28393:Human perforin mRNA, complete cds /cds (0, 1667) /g; 32967_at
  • Cluster Incl AF057557:Homo sapiens anti-Fas-induced apoptosis (TOSO) m; 35228_at Cluster Incl.
  • TOSO anti-Fas-induced apoptosis
  • Y08682 H.sapiens mRNA for carnitine palmitoyltransferase ; 32793_at Cluster Incl.
  • M83664 Human MHC class
  • HLA-DP lymphocyte antigen
  • U78521 Homo sapiens immunophilin homolog ARA9 mRNA, compl; 38078_at Cluster Incl.
  • AF042166 Homo sapiens beta-filamin mRNA, complete cds /cd; 39552_at Cluster Incl.
  • U92436 Human mutated in multiple advanced cancers protein; 41339_at Cluster Incl.
  • AF043117 Homo sapiens ubiquitin-fusion degradation protei; 41524_at Cluster Incl.
  • Cluster Incl. X52486:Human mRNA for uracil-DNA glycosylase /cds (79,105; 39945_at Cluster
  • AB020653 Homo sapiens mRNA for KIAA0846 protein, complete; 32859_at Cluster Incl.
  • M97935 Homo sapiens transcription factor ISGF-3 mRNA, com; 35362_at Cluster Incl.
  • AB018342 Homo sapiens mRNA for KIAA0799 protein, partial ; 35766_at Cluster Incl.

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Abstract

Cette invention concerne des gènes dont l'expression est corrélée à un phénotype athérosclérotique et qui sont un déterminant de ce phénotype. Sont également concernés des gènes dont l'expression est corrélée à une vulnérabilité athérosclérotique et qui sont un déterminant de cette vulnérabilité. L'invention concerne également des méthodes d'utilisation desdits gènes déterminants susmentionnés - phénotype athérosclérotique et vulnérabilité à l'athérosclérose à des fins de diagnostic et de traitement ainsi que pour le criblage de médicaments. L'invention porte également sur des réactifs et des kits convenant pour les méthodes susmentionnées. Sont enfin décrites des méthodes permettant de déterminer si un gène est corrélé à un phénotype pathologique, la corrélation étant déterminée au moyen d'au moins un paramètre qui ne dépend pas du niveau d'expression et qui est déterminé de préférence par une analyse d'arbre prévisionnel binaire.
PCT/US2005/027989 2004-08-04 2005-08-04 Genes determinant le phenotype atherosclerotique et methodes d'utilisation Ceased WO2006026074A2 (fr)

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006102497A3 (fr) * 2005-03-22 2007-03-15 Univ Leland Stanford Junior Procedes et compositions de diagnostic, surveillance et developpement de medicaments destines au traitement de l'atherosclerose
WO2009036976A1 (fr) * 2007-09-18 2009-03-26 Helmholtz Zentrum München-Deutsches Forschungszentrum Für Gesundheit Und Umwelt (Gmbh) Utilisation du gène de photomédine-2/analogue à l'olfactomédine 2b (olfml2b), de ses variants et de sa protéine dans des approches diagnostiques et thérapeutiques des maladies cardiaques
US7888137B2 (en) 2003-10-09 2011-02-15 Universiteit Maastricht Method for identifying a subject at risk of developing heart failure by determining the level of galectin-3 or thrombospondin-2
US8672857B2 (en) 2009-08-25 2014-03-18 Bg Medicine, Inc. Galectin-3 and cardiac resynchronization therapy
AU2011242613B2 (en) * 2010-04-22 2015-08-27 Case Western Reserve University Systems and methods of selecting combinatorial coordinately dysregulated biomarker subnetworks
WO2018202931A3 (fr) * 2017-05-04 2019-03-14 Universidad Del País Vasco/Euskal Herriko Unibertsitatea Procédé pour diagnostiquer une plaque athérosclérotique instable
EP3626832A3 (fr) * 2014-11-25 2020-05-13 The Brigham and Women's Hospital, Inc. Procédé d'identification et de traitement d'une personne présentant une prédisposition à ou souffrant d'une maladie cardio-métabolique
WO2022261705A1 (fr) * 2021-06-16 2022-12-22 Esn Cleer Biomarqueurs et combinaisons de médicaments pour la prédiction de l'insuffisance cardiaque
US11613786B2 (en) 2014-11-25 2023-03-28 President And Fellows Of Harvard College Clonal haematopoiesis
CN116246701A (zh) * 2023-02-13 2023-06-09 广州金域医学检验中心有限公司 基于表型术语和变异基因的数据分析装置、介质和设备
US12140598B2 (en) 2020-06-03 2024-11-12 Endothelium Scanning Nanotechnology Limited Biomarker identification for imminent and/or impending heart failure
CN119290542A (zh) * 2024-12-13 2025-01-10 天津医科大学总医院空港医院 一种胸腺肿瘤微环境抗原检测试剂盒及在肌无力评价中的应用

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003091391A2 (fr) * 2002-04-23 2003-11-06 Duke University Genes determinants pour phenotype atheroscclerotique et methodes d'utilisation
AU2003290537A1 (en) * 2002-10-24 2004-05-13 Duke University Binary prediction tree modeling with many predictors and its uses in clinical and genomic applications

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7888137B2 (en) 2003-10-09 2011-02-15 Universiteit Maastricht Method for identifying a subject at risk of developing heart failure by determining the level of galectin-3 or thrombospondin-2
US8084276B2 (en) 2003-10-09 2011-12-27 Universiteit Maastricht Method for identifying a subject at risk of developing heart failure by determining the level of galectin-3 or thrombospondin-2
WO2006102497A3 (fr) * 2005-03-22 2007-03-15 Univ Leland Stanford Junior Procedes et compositions de diagnostic, surveillance et developpement de medicaments destines au traitement de l'atherosclerose
WO2009036976A1 (fr) * 2007-09-18 2009-03-26 Helmholtz Zentrum München-Deutsches Forschungszentrum Für Gesundheit Und Umwelt (Gmbh) Utilisation du gène de photomédine-2/analogue à l'olfactomédine 2b (olfml2b), de ses variants et de sa protéine dans des approches diagnostiques et thérapeutiques des maladies cardiaques
US8672857B2 (en) 2009-08-25 2014-03-18 Bg Medicine, Inc. Galectin-3 and cardiac resynchronization therapy
AU2011242613B2 (en) * 2010-04-22 2015-08-27 Case Western Reserve University Systems and methods of selecting combinatorial coordinately dysregulated biomarker subnetworks
EP3626832A3 (fr) * 2014-11-25 2020-05-13 The Brigham and Women's Hospital, Inc. Procédé d'identification et de traitement d'une personne présentant une prédisposition à ou souffrant d'une maladie cardio-métabolique
US11613786B2 (en) 2014-11-25 2023-03-28 President And Fellows Of Harvard College Clonal haematopoiesis
US12503733B2 (en) 2014-11-25 2025-12-23 The Brigham And Women's Hospital, Inc. Methods of identifying and treating a person having a predisposition to or afflicted with a cardiometabolic disease
US11788144B2 (en) 2014-11-25 2023-10-17 The Brigham And Women's Hospital, Inc. Methods of identifying and treating a person having a predisposition to or afflicted with a cardiometabolic disease
WO2018202931A3 (fr) * 2017-05-04 2019-03-14 Universidad Del País Vasco/Euskal Herriko Unibertsitatea Procédé pour diagnostiquer une plaque athérosclérotique instable
US12140598B2 (en) 2020-06-03 2024-11-12 Endothelium Scanning Nanotechnology Limited Biomarker identification for imminent and/or impending heart failure
WO2022261705A1 (fr) * 2021-06-16 2022-12-22 Esn Cleer Biomarqueurs et combinaisons de médicaments pour la prédiction de l'insuffisance cardiaque
US12521402B2 (en) 2021-06-16 2026-01-13 Endothelium Scanning Nanotechnology Limited Biomarker and drug combinations for heart failure prediction
CN116246701B (zh) * 2023-02-13 2024-03-22 广州金域医学检验中心有限公司 基于表型术语和变异基因的数据分析装置、介质和设备
CN116246701A (zh) * 2023-02-13 2023-06-09 广州金域医学检验中心有限公司 基于表型术语和变异基因的数据分析装置、介质和设备
CN119290542A (zh) * 2024-12-13 2025-01-10 天津医科大学总医院空港医院 一种胸腺肿瘤微环境抗原检测试剂盒及在肌无力评价中的应用

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