CA2531091A1 - Genes regulated in ovarian cancer as prognostic and therapeutic targets - Google Patents

Abstract

This invention relates to the use of genomic analysis to detect the presence of ovarian cancer in a patient from a sample of tissue or blood and to kits for carrying out this determination. In addition this invention relates to methods to treat a patient with ovarian cancer.

Description

GENES REGULATED IN OVARIAN CANCER AS PROGNOSTIC
AND THERAPEUTIC TARGETS
TEGHNIGAL FIELD
[01] The present invention belongs to the fields of medicine and relates to the use of genomic analysis to evaluafie and treat ovarian cancer. In particular, this invention relates to the measurement of patterns of gene expression t~ determine the presence of ovarian cancer in a patients tissues.
BACICGR~UND ART

[02] ~varian cancer is one of the most common types of cancer that affects women in the United States, with a lifetime risk of approximately 1/70. See Whittemore, Gynecol.
~ncol., Vol. 55, No. 3, Part 2, pp. S15-S19 (1994). It is a rapidly fatal disease usually detected late, with still no good method of prevention. The greatest risk factor for ~varian cancer is a family history of the disease, suggesting the strong influence of genetics. See Schildkraut and Thompson, Am. J. E,oiciemi~L, Vol. 128, No. 3, pp. 456-466 (1988). ~ther factors such as demographic, lifestyle and reproductive factors have also been shown to contribute to the risk of ovarian cancer.

[03] Several microarray expression analyses of ovarian biopsies and cell lines have been conducted to identify genes specifically over-expressed in ovarian cancers.
See Schummer et al., Gene, Vol. 238, No. 2, pp. 375-385 (1999). ~ther sfiudies have tried to correlate gene expression levels with specific tumor types. See Bayani et al., Cancer Res., Vol. 62, No. 12, pp. 3466-3476 (2002); Welsh et al., Proc. Natl. Aead. Sci.
.USA, Vol. 98, No. 3, pp. 1176-1181 (2001 ); and ~no et al., Cancer f;es., Vol. 60, No. 18, pp. 5007-5011 (2000).

[04] These kinds of studies, aimed at increasing our understanding of the molecular mechanism of tumor development and in some cases at better classifying tumors, has provided a list of genes with few overlaps between analyses. Some technical differences may explain in part the apparent lack of consistency or low reproducibility between studies:
quality of samples, amplification of the messenger ribonucleic acid (mRNA) and different microarray platforms. However, it is likely that the heterogeneity of the tumors is a key factor that contributes to the differences observed between studies, in particular, those where few tumors are analyzed. Furthermore, comparison of gene expression levels on microarray experiments have historically been done using ratios of signal intensity (fold change), with limited use of statistical methods and a lack of validation with additional samples.

[05] However, genes apparently expressed at high levels, or with the biggest change in expression, may not always be the most relevant; it is conceivable that a small disruption of the very tight regulation of genes may have dramatic consequences, even when the level of expression is low.

[06] Thus there is a need for the identification of genes whose expression rates are consistently and reliably altered in ovarian cancer. Such a list could provide new insight into ovarian tumor development and progression, and suggest potential new drug targets, and biomarkers for diagnosis, monitoring and treatment of the disease.
DISCLOSURE OF INVENTION

[07] In the present invention, the application of a combination of statistical tests and the recently described leave-one-out method [see van't Veer et al., Nature, Vol.
415, No. 6871, pp. 530-536 (2002)], allows the analyse of expression profiles of tumors and normal ovarian tissues and for these patterns to also be determined in gene expression products in various body fluids including, but not limited to, blood and serum. See van't Veer et al., supra.

[08] The study of two independent sets of samples, a test set and a validation set, confirms the involvement of several known genes with ovarian tumor development, but also identify novel genes. These findings provide new insight into ovarian tumor development and progression, and suggest potential new drug targets, and biomarlcers for diagnosis and monitoring of the disease.

[09] In one embodiment, this invention provides a method to determine if a patient is afflicted with ovarian cancer comprising:
a) obtaining a sample from the said patient;
b) determining the levels of gene expression of two or more of the genes listed in Table 9 in the sample from the patient;
c) comparing the levels of gene expression of the two or more genes determined in (b) to the levels of the same genes listed in Table 1;
d) determining the degree of similarity (DOS) between the levels of gene expression of the two or more genes determined in (c); and e) determining from the DOS between the level of gene expression of the two or more genes the probability that the sample shows evidence of the presence of ovarian cancer in the patienfi.

[10] In a preferred embodiment, this invention provides a method wherein the levels of gene expression are determined for a subset of the genes listed in Table 9 comprising genes Nos. 1-28 in Table 9.

[11] In another embodiment, the invention employs a sample comprising cells obtained from the patient. These may be cells removed from a solid tumor in the said patient or, in a preferred embodiment, the sample comprises blood cells and serum drawn from the said patient. In a most preferred embodiment, the sample comprises a body fluid drawn from the patient.

[12] In a preferred embodiment, this invention employs a method of determining the level of gene expression comprising measuring the levels of protein expression product in the sample from the patient. This may be done in a variety of ways including, but not limited to, detecting the presence and level of fihe protein expression products using a reagent which specifically binds with the proteins, wherein the reagent may be selected from the group consisting of an antibody, an antibody derivative and an antibody fragment.

[13] In another embodiment, this invention provides a method wherein the levels of expression in the sample are assessed by measuring the levels in the sample of the transcribed polynucleotides of the two or more gene in Table g. These transcribed polynucleotide may be mF2N~, or complementary ~NA (c~NA).
(14~] In a preferred embodiment, this method would furfiher include the step of amplifying the transcribed polynucleotide.
[15] In another embodiment, this invention includes a method of treating a subject afflicted with ovarian cancer, the method comprising providing to cells of the subject an antisense oligonuceotide complimentary to one or more of the genes whose expression is up-regulated in ovarian cancer as shown in Table 8.
[16] In addition, this invention provides a method of inhibiting ovarian cancer in a subject at risk for developing ovarian cancer, the method comprising inhibiting expression of one or more of the genes shown in Table 8 to be up-regulated in ovarian cancer.

[17] This invention also provides kits for use in determining treatment strategy for a patient with suspected ovarian cancer comprising:
a) a number (for example, two or more) of antibodies able to recognize and bind to the polypeptide expression product of the two or more of the genes in Table 9;
b) a container suitable for containing the said antibodies and a sample of body fluid from the said individual wherein the antibody can contact the polypeptide expressed by the two or more genes shown in Table 9 if they are present;
c) means to detect the combination of the said antibodies with the polypepfiides expressed by the two or more genes shown in Table 9; and d) instructions for use and interpretation of the kit results.
[18] In another embodiment, this invention provides a kit for use in determining the presence or absence of ovarian cancer in a patient comprising:
a) a number (for example, two or more) of polynucleotides able to recognize and bind to the mRNA expression product of the two or more genes shown in Table 9;
b) a container suitable for containing the said polynucleotides and a sample of body fluid from the said individual wherein the said polynucleotide can contact the mRNA, if ifi is present;
c) means fio detect the levels of combination ofi the said polynucleotide with the mRNA from the two or more genes shown in Table 9; and d) instructions for use and interpretation of the Icit results.
BRIEF DESCRIPTION OF ~RAylIINGS
[19] FIG. 1 (A). Re-Classification of Samples Using Increasing Number of Probe Sets.
[20] FIG. 1 (B). Plot of Errors as a Function of Number of Probe Sets for Determination of Optimum Number of Classification Genes. Calculated values by increasing number of individual probe sets from the top 6 to the top 55 (A) or to all 900 (B).
Arrow indicates the minimum number of probe sets (N=28) that minimizes misclassification.
[21] FIG. 2. Determination of a Threshold CC Value for Classification of Ovarian Status.
[22] FIG. 3. Correlation of Test and Validation Biopsy Profiles with Mean Normal Profile for Different Size Probe Sets. N or T represent N~rmal or Tumor status, respectively. "r" is the PCC value of the probe set profile of the corresponding biopsy sample with the mean Normal profile (Group 1 ). Samples are ordered from highest CC to lowest.

[23] FIG. 4. Correlation of Biopsy Profiles with Mean of All Normal Profiles for Different Size Probe Sets. N or T represent N~rmal or Tumor status, respectively. "r" is the PCC
value of the probe set profile of the corresponding biopsy sample with the mean profile of all Normal samples. Samples are ordered from highest CC to lowest.
M~DES F~R CARRYING ~UT THE INVENTI~N
[24] The present invention provides methods to determine whether or not a sample from a patient including, but not limited to, biopsy tissue or blood, serum or some other body fluid from a patient, contains evidence of the presence of ovarian cancer in the patient.
(25] This invention is based, in part, on the discovery of approximately 900 genes which are differentially expressed in tissue from ovarian cancer as compared to normal tissue. This methods of this invention comprise measuring the activities of the approximately 900 or fewer genes that are shown to be differently-expressed in ovarian cancer as compared to normal tissue.
[26] In a preferred embodiment, only a small fraction of the 900 genes would be measured. These measurements, could, in various embodiments, be in the tissue itself from biopsies, etc., or in prefierred embodiments could be perfiormed as more indirect measurement of gene expression including, but not limited to, cRNA or polypeptide expression products in various tissues including blood or other body fluids.
[2~] The measurements, direct or indirect, of the rates of expression of two or more of these 900 genes from an individual whose fiissues status was unknown could then be compared to the expression values for the same two or more genes measured in ovarian cancer tissue or normal tissue.
[28] The "degree of similarity" (D~S) of the unknown two or more gene expression values to the cancer tissue versus normal tissue would then be determined.
[29] This DOS could be determined by any procedure that produces a result whose value is a known function of the D~S between the two groups of numbers, i.e., the measured gene expression values of the two or more genes in tissue from an individual whose ovarian cancer status is unknown and to be determined and the measured gene expression values for the same two or more genes from individuals whose tissue is known to contain ovarian cancer and from individuals whose tissue is known not to contain ovarian cancer.

[30] As used herein the term "DOS" shall mean the extent to which the pattern of gene expression values are alike or numerically similar, as measured by a comparison of the values of gene expression determined by direct or indirect methods.
[31] In a preferred embodiment, the DOS would be determined by a mathematical calculation resulting in a correlation coefficient (CC). In a particularly preferred embodiment, the Pearson Correlation Coefiacient (PCC) would be determined but any other mathematical procedure that produces a result whose value is a known function of the DOS
between the two groups of numbers could be used.
[32] The value of the DOS (PCC), so calculated, can then be directly related t~ the probability that the tissue sample is from a patient who does or does not have ovarian cancer. That is to say, the higher the patients' DOS (CC or PCC) as compared to the gene expression values from a patient who does not have ovarian cancer or the higher the DOS
(CC or PCC) as compared to the gene expression values from a patient who does have ovarian cancer then the greater the probability that the patient does not or does have ovarian cancer, respectively.
[33] Thus, in a given case, the value of the DOS can be used to determine probabilities for the presence of ovarian cancer. Those of skill in the art will understand that the clinical circumstance for each patient will dictate the value of the DOS (PCC) to be used as a cutoff or to help make clinical decisions with regard to a specific patient. For example, in one embodiment, it is desirable to determine with optimal accuracy the number of a group of patients who have ovarian cancer. This means to minimise both false positives (hlo Ovarian Cancer misclassified as Ovarian Cancer) and at the same time to minimise false negatives (Ovarian Cancer misclassified as No Ovarian Cancer ).
[34] In one preferred embodiment of the present invention, this would work as shown in FIG. 3, using the 23 predictor probe set (as described below) if the gene expression profile correlafies with the mean normal (No Ovarian Cancer) profile with a CC <_0.920 the tissue sample is 63 times more likely to contain ovarian cancer then if the CC >0.920 [odds ratio (OR) = 63 with 95% confidence interval (CI): 3.3-1194.7].
[35] To use this threshold in one embodiment of this invention, a patient whose gene expression profile when compared with the mean No Ovarian Cancer expression profile y achieves a PCC of >0.92b would be classified in the-No Ovarian Caricer group and would be presumed not to have ovarian cancer, while a patient whose expression profile was had a PCC of s0.920 would be classified in the Ovarian Cancer group and would be assumed to have ovarian cancer with a high probability.

[36] In a further preferred embodiment, the PCC can be set to produce optional sensitivity. That is, to make the smallest possible number of false negatives (~varian Cancer misclassified as No ~varian Cancer). Such an optimal sensitivity setting would be indicated in situations where the occurrence of ovarian cancer must be ruled out with the greatest certainty obtainable. In this embodiment, the threshold is determined by setting the PCC to >0.955. In this case, in the example given, using the 28 predictor probes shown in Table 9 (probe sets 1-28 shown in Table 9), 100% of patients with a CC of >0.955 as compared to the No ~varian Cancer group did not have ovarian cancer and 100% of the patients whose CC were <0.870, as compared to the No ~varian Cancer group, did have ovarian cancer.
[37] As is shown in the example, one of skill in the art can choose a PCC that will either maximize sensitivity or maximize specificity or produce any desired ratio of false p~sitives or false negatives. ~ne of skill in the art can easily adjust their choice of PCC
to the clinical situation to provide maximum benefit and safety to the patient.
[38] Another aspect of the of the invention are methods to treat ovarian cancer. These methods consist of various efforts to suppress the excess gene expression of the genes that have been found to be up-regulated in ovarian cancer. These genes are shown in Table 8.
Methods to decrease the excess expression of these gene would include, but not be limited to, use of antisense ~NA, sil~NA and methods to complex and deactivate the protein expression products of these over-expressed genes.
~Tefih~ds of l~Ieasurerr~enfi [39] In some embodiments of this invention, the gene expression of a selected group of the 900 genes is determined by measuring mRNA levels from tissue samples as described below.
[40] In some embodiments, the gene expression can be measured more indirectly by measuring polypeptide gene expression products in tissues including, but not limited to, tumor and blood tissue.
[41] In some embodiments, gene expression is measured by identifying the presence or amount of one or more proteins encoded by one of the genes listed in Table 9.
[42] The present invention also provides systems for detecting two or more markers of interest, e.g., two or more markers from Table 2. For example, where it is determined that a finite~set of particular markers provides relevant information, a defection system is provided that detects the finite set of markers. For example, as opposed to detecting all genes expressed in a tissue with a generic microarray, a defined microarray or other detection technology is employed to detect the plurality, e.g., 28, 42, etc., of markers that define a biological condition, e.g., the presence or absence of ovarian cancer, etc.
[43] The present invention is not limited by the method in which gene expression biomarkers are detected or measured. In some embodiments, mRNA, cDNA or protein is detected in tissue samples, e.g., biopsy samples. In other embodiments, mRNA, cDNA or protein is detected in bodily fluids, e.g., serum, plasma, urine or saliva. A
preferred embodiment of the invention provides that fihe method of the invenfiion is performed ex vivo.
The present invention furfiher provides kits for the detection of these relevant gene expression biomarkers.
[44] In some preferred embodiments, protein or the polypeptide expression product is deflected. Protein expression may be detected by any suitable method. In some embodiments, proteins are deflected by binding of an antibody specific for the protein. For example, in some embodiments, antibody binding is detected using a suitable technique including, but not limited to, radioimmunoassay, enzyme-linked immunosorbant assay (ELISA), "sandwich" immunoassays, immunoradiometric assays, gel diffusion precipitation reactions, immunodiffusion assays, in sitcc immunoassays, e.g., using colloidal gold, enzyme or radioisotope labels, e.g., Western blofis, precipitation reactions, agglutination assays, e.g., gel agglutination assays, hemagglutination assays, etc., complement fixation assays, immunofluorescence assays, protein A assays, immunoelectrophoresis assays and proteomic assays, such as the use of gel electrophoresis coupled to mass spectroscopy to identify multiple proteins in a sample.
[45] In one embodiment, antibody binding is detected by detecting a label on the primary antibody. In another embodiment, the primary antibody is detected by detecting binding of a secondary antibody or reagent to the primary antibody. In a further embodiment, the secondary antibody is labeled. Many methods are known in the art for detecting binding in an immunoassay and are within the scope of the present invention.
[46] In some embodiments, an automated detection assay is utilized. Methods for the automation of immunoassays include, but are not limited to, those described in U.S. Patent Nos. 5,885,530; 4,981,785; 6,159,750; and 5,358,691, each of which is herein incorporated by reference. In some embodiments, the analysis and presentation of results is also automated. For example, in some embodiments, software that generates-a diagnosis and/or --~--prognosis based on the presence or absence of a series of proteins corresponding to markers is utilized.

[47] In other embodiments, the immunoassay described in U.S. Patent Nos.
5,599,677 and 5,672,480, each of which is herein incorporated by reference, is utilized.
In other embodiments, proteins are detected by immunohistochemistry. In still other embodiments, markers are detected at the level of cDNA or RNA.
[48] As used herein, the term "gene expression biomarkers" shall mean any biol~gic marker which can indicate the rate or degree of gene expression of a specific gene including, but not limited to, mRNA, cDNA or the polypeptide expression product of the specific gene.
[49] In some embodiments of the present invention, gene expression biomarkers are detected using a PCR-based assay. In yet other embodiments, reverse-transcriptase PCR
(RT-PCR) is used to detect the expression of RNA. In RT-PCR, RNA is enzymatically converted to cDNA using a reverse-transcriptase enzyme. The cDNA is then used as a template for a PCR reaction. PCR products can be detected by any suitable method including, but not limited to, gel electrophoresis and staining with a DNA-specific stain or hybridization to a labeled probe.
[50] In some embodiments, the quantitative RT-PCR with standardized mixtures of competitive templates method described in U.S. Patent Nos. 5,639,606; 5,643, 765; and 5,876,978, each of which is herein incorporated by reference, is utilized.
[51] In preferred embodiments of the present invention, gene expression biomarkers are detected using a hybridization assay. In a hybridization assay, the presence or absence of a marker is determined based on the ability of the nucleic acid from the sample to hybridize to a complementary nucleic acid molecule, e.g., an oligonucleotide probe. A
variety of hybridization assays are available.
[52] In some embodiments, hybridization of a probe to fihe sequence of interest is detected directly by visualizing a bound probe, e.g., a Northern or Southern assay. See, e.g., Ausabel et al., eds., Current Protocols in Mfolecular biology, John Wiley & Sons, N1P
(1991 ). In these assays, DNA (Southern) or RNA (Northern) is isolated. The DNA or RNA is then cleaved with a series of restriction enzymes that cleave infrequently in the genome and not near any of the markers being assayed. The DNA or RNA is then separated, e.g., on an agarose gel, and transferred to a membrane. A labeled probe or probes, e.g., by incorporating a radionucleotide, is allowed to contact the membrane under low-, medium- or high-stringency conditions. Unbound probe is removed and the presence of binding is detected by visualizing the labeled probe.
[53] In some embodiments, the DNA chip assay is a GeneChip (Affymetrix, Santa Clara, CA). See, e.g., U.S. Patent Nos. 6,045,996; 5,925,525; and 5,858,659, each of which is herein incorporated by reference. The GeneChip technology uses miniaturized, high-density arrays of oligonucleotide probes affixed to a "chip". Probe arrays are manufactured by Affymetrix's light-directed chemical synthesis process, which combines solid-phase chemical synthesis with photolithographic fabrication techniques employed in the semiconductor industry. Using a series of photolithographic masks to define chip exposure sites, followed by specific chemical synthesis steps, the process constructs high-density arrays of oligonucleotides, with each probe in a predefined position in the array.
Multiple probe arrays are synthesized simultaneously on a large glass wafer. The wafers are then diced, and individual probe arrays are packaged in injection-molded plastic cartridges, which protect them from the environment and serve as chambers for hybridizafiion.
[54] The nucleic acid to be analyzed is isolated, amplified by PCR and labeled with a fluorescent reporter group. The labeled DNA is then incubated with the array using a fluidics station. The array is then inserted into the scanner, where patterns of hybridization are detecfed. The hybridization data are collected as light emitted from the fluorescent reporfier groups already incorporated into the target, which is bound to the probe array. Pr~bes that perfectly match fihe target generally produce stronger signals than those fihat have mismatches. Since the sequence and position of each probe on the array are known, by complementary, the identity of the target nucleic acid applied to the probe array can be determined.
[55] In other embodiments, a DNA microchip containing electronically captured probes (Nanogen, San Diego, CA) is utilized. See, e.g., U.S. Patent illos. 6,017,696;
6,063,513; and 6,051,360, each of which are herein incorporated by reference. Through the use ~fi microelectronics, Nanogen's technology enables the active movement and concentration ofi charged molecules to and from designated test sites on its semiconductor microchip. DNA
capture probes unique to a given gene expression biomarkers are electronically placed at, or "addressed" to, specific sites on the microchip. Since nucleic acid molecules have a strong negative charge, they can be electronically moved to an area of positive charge.
[56] In still further embodiments, an array technology based upon the segregation of fluids on a flat surface (chip) by differences in surface tension (ProtoGene, Palo Alto, CA) is utilized. See, e.g., U.S. Patent Nos. 6,001,311; 5,965,551; and 5,474,796, each of which is herein incorporated by reference. Protogene's technology is based on the fact that fluids can be segregated on a flat surface by differences in surface tension that have been imparted by chemical coatings. Once so segregated, oligonucleotide probes are synthesized directly on the chip by ink jet printing of reagents.

[57] In yet other embodiments, a "bead array" is used for the detection of gene expression biomarkers (Illumina, San Diego, CA). See, e.g., PCT Publications and WO 00/39587, each of which is herein incorporated by reference. Illumina uses a BEAD
ARRAY technology that combines fiber optic bundles and beads that self-assemble into an array. Each fiber optic bundle contains thousands to millions of individual fibers depending on the diameter of the bundle. The beads are coated with an oligonucleotide specific for the detection of a given marker. Batches of beads are combined to form a pool specific to the array. To perform an assay, the BEAD ARRAY is confiacted with a prepared sample.
Hybridization is detected using any suitable method.
[58] In some preferred embodiments of the present invention, hybridization is detected by enzymatic cleavage of specific structures, e.g., INVADER'T" assay, Third Wave Technologies. See, e.g., U.S. Patent Nos. 5,846,717, 6,090, 543; 6,001,567;
5,985,557; and 5,994,069, each of which is herein incorporated by reference. In some embodiments, hybridization of a bound probe is detected using a TaqMan assay (PE
Biosystems, Foster City, CA). See, e.g., U.S. Patent Nos. 5,962,233' and 5,538,848, each of which is herein incorporated by reference. The assay is performed during a PCR reaction. The TaqMan assay exploits the 5'-3' exonuclease activity of DNA polymerises, such as AMPLITAQ DNA
polymerise. A probe, specific for a given marker, is included in the PCR
reaction. The probe consists of an oligonucleotide with a 5'-reporter dye, e.g., a fluorescent dye and a 3'-quencher dye. During PCR, if the probe is bound to its target, the 5'-3' nucleolytic activity of the ~4MPLITAQ polymerise cleaves the probe between the reporter and the quencher dye.
The separation of the reporfier dye firom the quencher dye results in an increase of fluorescence. The signal accumulates wifih each cycle of PCR and can be monitored with a fluorimeter.
[59] Additional detection assays that are produced and utilized using the systems and methods of the present invention include, but are not limited to, enzyme mismatch cleavage methods, e.g., Variagenics (see U.S. Patent Nos. 6,110,684; 5,958,692; and 5,851,770, herein incorporated by reference in their entireties); branched hybridization methods, e.g., Chiron (see U.S. Patent Nos. 5,849,481; 5,710,264; 5,124,246; and 5,624,802, herein incorporated by reference in their entireties); rolling circle replication (see, e.g., U.S. Patent Nos. 6,210,884 and 6,183,960, herein incorporated by reference in their entireties); NASBA
(see, e.g., U.S. Patent No. 5,409,818, herein incorporated by reference in its entirety);
molecular beacon technology (see, e.g., U.S. Patent No. 6,150,097, herein incorporated by reference in its entirety); E-sensor technology (see Motorola, U.S. Patent Nos. 6,248,229;

6,221,583; 6,013,170; and 6,063,573, herein incorporated by reference in their entireties);
cycling probe technology (see, e.g., U.S. Patent Nos. 5,403,711; 5,011,769;
and 5,660,988, herein incorporated by reference in their entireties); ligase chain reaction [see Sarnay, Proc. Nafl. Acad. Sei. USA, Vol. 88, pp. 189-93 (1991 )]; and sandwich hybridization methods (see, e.g., U.S. Patent No. 5,288,609, herein incorporated by reference in its entirety).
[60] In some embodiments, mass spectroscopy is used to detect gene expression biomarkers. For example, in some embodiments, a MASSARRAY"y" system (Sequenom, San ~iego, CA) is used to detect gene expression biomarkers. See, e.g., U.S.
Patent Nos. 6,043,031; 5,777,324; and 5,605,798, each of which is herein incorporated by reference.
[61] In some embodiments, the present invention provides kits for the identification, characterization and quantitation of gene expression biomarkers. In some embodiments, the kits contain antibodies specific for gene expression biomarkers, in addition to detection reagents and buffers. In other embodiments, the kits contain reagents specific for the detection of nucleic acid, e.g., oligonucleotide probes or primers. In preferred embodiments, the kits contain all of the components necessary to perform a detection assay, including all controls, directions for perForming assays and any necessary software for analysis and presentation of results. In some embodimenfis, the kits contain instructions including a statement of intended use as required by the Environmental Protection Agency or U.S. Food and ~rug~Administration (F~A) for the labeling of irr vitro diagnostic assays and/or of pharmaceutical or food products.
[62] comparison of the organism's gene e~cpression patfiern, with the result expressed in Table 9, would indicate whether the organism has a gene expression profile which may indicate that the organism does or does not contain ovarian cancer.
[63] In another embodiment, the present invention is a method of screening a test compound for the ability to inhibit, retard, reverse or mimic the gene expression changes characteristic of ovarian cancer. In a typical example of this embodiment, one would first treat a test mammal known to have ovarian cancer with a test compound and then analyze a representative tissue of the mammal for the level of expression of the genes or sequences which change in expression in response to ovarian cancer. Preferably, the tissue is biopsy material from the tumor or, in a preferred embodiment, an easily obtainable tissue, such as blood or serum.
[64] ~ne then compares the analysis of the tissue with a control mammal known to have ovarian cancer but not given the test compound and thereby identifies test compounds that are capable of modifying the expression of the gene expression biomarkers sequences in the mammalian samples such that the expression is altered toward the No ~varian Cancer pattern.
[65] In another embodiment of the present invention, one would use the sequences of the genes disclosed in Table 2 for a therapy for mimicking the No ~varian Cancer state. In general, one would try to amplify gene expression for the genes identified herein as under-expressed in ovarian cancer and decrease the expression of genes identified herein as over-expressed in ovarian cancer. For example, one might try to decrease the expression of genes or sequences identified in Table 2 as increased or increase the expression ~f genes found to be decreased in ovarian cancer.
[66] N9ethods of increasing and decreasing expression would be known to one of skill in the art. Examples for supplementation of expression would include supplying the ~rganism with additional copies of the gene. A preferred example for decreasing expression would include RNA antisense technologies or pharmaceutical intervention. The genes disclosed in Table 2 would be appropriate drug development targets. ~ne would use the information presented in the present application for drug development by using currently exisfiing, or by developing, pharmaceutical compounds that either mimic or inhibit the activity of the genes listed in Table ~, or the proteins encoded by these genes. Therefore, the gene expression biomarkers or genes disclosed herein represent targets for pharmaceutical development and gene therapy or RNA antisense therapy with the goal of suppressing the changes characteristic ~f ovarian cancer at the molecular level. These gene ea~pression alterations may also play a role in understanding the various mechanisms that underlie ovarian cancer.
Additionally, these genes represent biomarkers of ovarian cancer that can be used for diagnostic purposes.
[67] The present invention is not limited by the form of the expression profile. In some embodiments, the expression profile is maintained in computer software. In some embodiments, the expression profile is written material. The present invention is not limited by the number of markers provided or displayed in an expression profile. For example, the expression profile may comprise two or more markers found in Table 2, indicating a biological status of a sample.
[68] The present invention further provides databases comprising expression information, e.g., expression profiles comprising one or more markers from Table 2 from one or more samples. In some embodiments, the databases find use in data analysis including, but not limited to, comparison of markers to one or more public or private information databases, e.g., OMIM, GenBank, BLAST, Molecular Modeling Databases, Medline, genome databases, etc. In some such embodiments, an automated process is carried out to automatically associate information obtained from data obtained using the methods of the present invention to information in one or more of public or private databases. Associations find use, e.g., in making expression correlations to phenotypes, e.g., disease states.
[69] We also understand the present invention to be extended to mammalian homologues of the mouse genes listed in Table 9. One of skill in the art could easily invesfiigate homologues in other mammalian species by identifying particular genes with sufficiently high homology to the genes listed in Table 9. By "high homology"
we mean that the homology is at least 50°/~ overall (wifihin the entire gene or protein) either at the nucleotide or amino acid level.
List of Abbreviati~ns A Absent NCBI National Center for Biotechnology AvgDiffAverage Difference (overall Information intensity of probe set on AffymetrixNeg Negative array) CHTN Cooperative Human Tissue nM Nanometer Network CI Confidence Interval OMIM Online Mendelian Inheritance in Man FIGO Federation of Gynecology OR Odds Ratio and Obstetrics ORF Open Reading Frame GAPDH Glyceraldehyde 3-phosphatep Present dehydrogenase pG Pharmacogenetics GNF Genomics Institute of the Novartis Pos Positive Research Foundation QC Quality Control mg Milligram RNA Ribonucleic Acid E)CAMPLE 1 Preferred Methods [70] To identify genes involved in the development and progression of ovarian tumors, we compared the gene expression profiles of a series of Normal and Tumor ovarian biopsies.
Gene expression data for more than 12,000 genes were generated from each sample. Of the 900 probe sets that we observed to be most differentially-expressed between the Normal and cancerous ovarian biopsies, 98% were down-regulated in the Tumor biopsies.
Using 8 Normal and 10 Tumor samples, we identified a minimum number of probe sets (28) that could be used to classify biopsies as Normal or Tumor. This ending was validated on a second set of biopsies (4 Normal and 14 Tumor) previously profiled by another laboratory. A
mean Normal ovarian profile was established that could be used as a reference to compare other ovarian biopsies. The identification of the most differentially-expressed genes between Normal and Tumor ovarian biopsies may provide new insight into the molecular mechanisms of ovarian tumor development and progression. Some of the genes identified in this study are known to be involved in ovarian cancer, but a large proportion represents novel candidates for drug targets and molecular biomarkers to diagnose or monitor disease and treatment.
I~faterials and I~lethods Sam~ales [71] Flash-frozen ovarian biopsies were obtained from Asterand (Detroit, MI), and consisted of 10 Tumor samples and 10 adjacent Normal tissues. Total RNA was also purchased for 4 additional samples from Ambion (Austin, TX) and Stratagene (La Jolla, CA).
Gene expression proi=tles from samples used in the validation step had been previ~usly generated at GNF and reported. See Welsh et al. (2001 ), supra.
[72] Most of the tumors analyzed were malignant surFace epithelial serous tumors, e.g., papillary cystcarcinoma, papillary cystadenocarcinoma or papillary cystcarcinoma; others included a mucinous cyst carcinoma, an endometrioid adenocarcinoma and a mature teratoma.
[73] A summary of sample information is shown in Table 1 below.
Table 1. ~~arian Samples Dsed f~r Gene E~~aressi~n Ar~alysi~
Sarr~pl~ Tern~r c~de Sta~~~a~~~rr~rner~~l'i'urn~r C~II Stagy S~urc~
1'yp~ (oo) p2437 NormalNormal margin to cystadenoma Stratagene p2709 NormalNo pathology data Ambion p5720 Normal Ambion p5721 Tumor Adenocarcinoma Not Ambion Available p6166 Tumor Serous cyst carcinoma 70 III Asterand p6167 Normal Asterand p6168 Tumor Serous cyst carcinoma 50 II Asterand p6169 Normal Asterand p6170 Tumor Serous cyst carcinoma 80 IC Asterand p6171 Normal Asterand p6172 Tumor Endometrioid adenocarcinoma50 IV Asterand p6173 Normal Asterand p6174 Tumor Mucinous cyst carcinoma 40 I Asterand p6175 Normal Asterand Sample Tumor code Status CommentlTumor Type Cell Stage Source (%) p6176 Tumor Papillary serous cyst 80 III Asterand carcinoma p6177 Normal Asterand p6178 Tumor Serous cyst carcinoma 70 Not Asterand Available p6179 Normal Asterand p6180 Tumor Serous cyst carcinoma 70 Not Asterand Available p6181 Normal Asterand p6182 Tumor Serous cyst carcinoma 70 IIIC Asterand p6183 Normal Asterand p6184 Tumor Mature teratoma ~ 70 III Asterand p6185 Normal Asterand OVR1T Tumor Adenocarcinoma, serous 40 IIIC CHTN
papillary OVR2T Tumor Adenocarcinoma, serous 60 IIIC CHTN
papillary OVRST Tumor Adenocarcinoma, serous 80 IIIB CHTN
papillary OVRBT Tumor Adenocarcinoma, serous 80 1VA CHTN
papillary OVR10T Tumor Adenocarcinoma, serous 40 IVA CHTN
papillary OVR11T Tumor Adenocarcinoma, serous 40 IIIC CHTN
papillary OVR12T Tumor Adenocarcinoma, serous 50 IIIC CHTN
papillary OVR13T Tumor Adenocarcinoma, serous 90 IIIC CHTN
papillary OVR16T Tumor Adenocarcinoma, serous 40 IIIC CHTN
papillary OVR19T Tumor Adenocarcinoma, serous 30 IIIC CHTN
papillary OVR22T Tumor Adenocarcinoma, serous 40 IIIC CHTN
papillary OVR26T Tumor Adenocarcinoma, serous 60 IIIC CHTN
papillary OVR27T Tumor Adenocarcinoma, serous 40 IIIC CHTI~
papillary OVR28T Tumor Adenocarcinoma, serous 80 IIIC CHTN
papillary OVR102N Normal BioChain Institute OVR278ENNormal Enriched for epithelium BioChain Institute OVR278SNNormal Enriched for stroma BioChain Institute HUOVR Normal BioChain Institute Note: Paired samples (Normal and Tumor adjacent tissue) obtained from the same patient are boxed together. Stages of ovarian cancers are indicated using the FIGO staging system.
RNA Expression Profiling [74] Total RNA was extracted from each biopsy and processed as previously described.
RNA extraction techniques are well-known to those of skill in the art. All samples profiled were processed using the Affymetrix GENECHIPT"°' system as recommended by Affymetrix (GeneChip Expression Analysis Technical Manual, rev. 1, July 2001).
Concentration and total amount of RNA and cRNA were estimated by measuring the samples at 260 nM
and 280 nM wavelengths using a Seckman-Coulter DU 650 spectrophotometer after a 1:50 dilution of the samples (see Table 2). The type of array used for this study was the Human Genome U95Av2 (http://www.affymetrix.com/products/arrays/s~ecific/hgu95.affx).
Analvfical Sfirateq~
[75] Analysis of the expression profiles was performed in several steps described below.
Selection of micr~array data ~f highest quality [76] We used for our analysis only microarrays for which the scaling factor was lower than 6, and where more than 30% of the probe sets were called "Present" by the Affymetrix MAS 4.0 algorithm.
Selection of a subset of nr~lae sets [77] Expression data were directly imported into the GENE SPRING~ program (Silicon Genetics, Redwood City, CA) from the database. Genes expressed in only a few samples were eliminated; out of the 12,627 probe sets on the microarray, only those with an AvgDiff of at least 100 in 10°/~ of the samples or more were used for further analysis. A clustering experiment was performed to visualise the different gene expression profiles of Normal and Tumor biopsies.
[78] Further filtering was accomplished by eliminating probe sets of low quality or very low intensity signals in both groups of samples (Group 1: Normal biopsies;
Group 2: Tumor biopsies). Probe sets not called "Present" (P) in at least 75°/~ of the samples in one of the two groups were not used for further analysis. In addition, AvgDiff values lower than 20 were all converted to a value of 20.
Focus on the most differentially expressed Genes [79] Selection of genes differentially-expressed between the two groups of samples was done in 2 steps:
1. The AvgDiff of each probe set was compared between the 2 groups of samples by a non-parametric one-way ANOVA test, using SAS 8.2.
2. The AvgDiff of each probe sets with p<0.05 was then correlated with the group of samples (Normal or Tumor). Probe sets were ranked from highest absolute PCC to lowest (calculated in Microsoft Excel).

Re-classification of samples [80] We used the "leave-one-out" analytical strategy previously described to determine the optimal number of probe sets that distinguished an ovarian tumor from a normal ovarian tissue. See van't Veer et al, (2002), supra.
[81] For every sample left-out, we defiermined the average AvgDiff of each probe set in each group of samples (Groups 1 and 2). PCCs between the expression profile of the left-out sample and the average profile of each group were calculated for each probe set. The effectiveness of each probe set in distinguishing a tumor from a normal ovarian fiissue was evaluated by re-classifying each sample as Normal or Tumor based on the higher of the two CCs.
[82] We determined the number of misclassified samples when using increasingly larger sets of genes (sfiarting with 5). As used herein, a "false Neg" is defined as a Tumor incorrectly classified as a Normal ovary tissue, and inversely, a "false Pos"
is defined as a Normal tissue incorrectly classified as a Tumor.
[83] The probe sets that most-effectively distinguished tumor from normal ovarian tissue were then tested in their ability to classify gene expression profiles of a different set of ovarian tissues (falormal and Tumor) generated at GNF (see Table 1 ).
Statistical determination of OR
[84] Using the desired threshold correlation value, a ~ x 2 table was constructed indicating the number of biopsies correctly and incorrectly identified as Normal or Tumor:
OF~s, alone with g5~/~ Cls, were calculated using SAS version 8.~. Statistical significance was determined using a Fisher's exact test with p-value cut-off of 0.05.
Genes [85] The link between a probe set name and a GenBank Accession Number was provided by Affymetrix, together with a short gene description. We complemented and updated this description by a search of the NCBI databases, mainly LocusLink (http://www.ncbi.nlm.nih.govlLocusLinkiindex.html), OMIM
(http:l/www.ncbi.nlm.nih.gov/Omim/searchomim.html) and PubMed (http://www.ncbi.nlm.nih.aov/entrez/auery.fcgi).
Results RNA Expression Profiling [86] Eighteen out of the 20 biopsies yielded more than the 5 mg of purified total RNA
necessary to process the samples further (see Table 2). Sample p6175, from which less than 1 mg of purified total RNA was obtained, was the smallest sample (38 mg).
The quality of the RNA was assessed by electrophoresis on a 1 % agarose gel. The absence of both 28S and 18S ribosomal RNA bands was observed for samples p6169 and p6180, indicating some RNA degradation. For microarray hybridization, a maximum of 15 mg of cRNA
was used when available, but no less than 12 mg. Enough cRNA was available for 21 samples (see Table 2).
C,iuality Assessment [87] The data from the 21 arrays hybridized in PG (this study) and from the 18 hybridized previously at GNF were shacked for quality (see Table 3). See Welsh et al.
(2001 ), supra.
All but 3 (p6169, p6180 and p6185) passed our criteria of a scaling factor lower than 6, with more than 30 % of probe sets called "P°' (see Table 3). The 36 remaining expression profiles were separated into 2 sets: a test set of 18 profiles generated in PG
consisting of data from 8 Normal and 10 Tumor biopsies, and a validation set of 18 profiles previously generated at GNF from 4 Normal and 14 Tumor biopsies. See Welsh et al. (2001 ), supra.
~4nalvsis Olusferine~ Analysis [88] Expression data of the 18 samples of the test set were imported into the GENESPRING~ software. Out of the 12,627 probe sets on the Affymetrix 1J95A
microarray, 2,174 had an AvgDiff of at least 100 in 2 or more of these 18 samples and were used for clustering analysis. The resulting clustering tree of samples and probe sets is shovun in Fig. 1. Interestingly, the dendogram of experiments contains two main branches corresponding fio the two groups of samples, Normal (top) and Tumor (bottom) biopsies. The vast majority (>90°/~) of genes examined have an overall higher expression in the Normal ovarian tissues. The dendogram of probe sets shows only a small cluster of genes with higher expression in the Tumor tissues (left part).
Selection of the most differentially expressed,genes [89] Out of the 2,174 probe sets, 217 were excluded from further analysis because they provided large number of "A" or "marginal" calls (>75% in both groups).
[90] Data for the remaining 1,957 probe sets were exported into SAS version 8.2 for non-parametric one-way ANOVA testing between the Normal and the Tumor groups. A
total of 900 probe sets had AvgDiff values significantly different between the two groups (p<0.05).
These genes are listed in Table 9.

[91] The AvgDiff of these 900 probe sets was then correlated with the two groups of samples (Group 1: Normal; Group 2: Tumor). The absolute PCC (R)-values ranged from 0.042-0.877, with 694 probe sets (77%) with a R-value higher than 0.5. The AvgDiff data of the 900 probe sets ranked from highest absolute PCC to lowest are available in Appendix 1.
Leave-one-out method and re-classification of samples [92] The "leave-one-out" analytical strategy previously described was applied to the 18 ovarian samples for the expression of the 900 selected probe sets. See van't ~/eer et al.
(2002), supra.
[93] The number of misclassified samples when using the first 5 probe sets was 6 (2 false Pos and 4 false Neg). Increasingly large sets of genes were used. The number of misclassifications varied between 2 and 7, with the minimum achieved when using the first 28 probe sets (Fig. 1 ). These first 28 probe sets displayed only one false Pos and one false Neg. Interestingly, perfect classification of Normal biopsies (0 false Pos) was achieved with the first 32 probe sets (which also detected 2 false Neg), while perfect classification of Tumor biopsies (0 false Neg) was never seen.
~ofimal classificati~n set and correlation thresh~Id ~ralues [94] IIVe determined the mean Normal (No Tumor) biopsy profile for the classification probe sets, to be used as a reference for analysis of biopsies of unknown or questionable status; we expected that tumor heterogeneity may not allow the determination of a reference Tumor profile. ~Ne examined the classification value of the first 28 probe sets, by comparing their expression for each of fihe 18 samples to the mean Normal profile calculated using all 8 Normal biopsy profiles. Samples were then ranked by correlation values from highest to lowest and error rates were determined as a function of where the threshold correlation was drawn. The results are displayed in Fig. 2. The minimum number of incorrectly assigned samples was 2 [1 false Pos (p6177) and 1 false Neg (p6168)]. The corresponding CC value was between 0.920 and 0.921. The ~R and Fisher's exact test were performed based on the number of samples correctly and incorrectly predicted to be Normal or Tumor. The difference between the observed and expected biopsy status was significant: OR
= 63; 95%
CI: 3.3-1194.7, p=0.0029. The OR indicates that an ovarian biopsy of the test set is nearly 63 times more likely to be from an ovarian tumor if its expression profile of the 28 predictor probe sets correlates with the mean Normal profile with a CC s0.920 (see Table 4). In our test set, 100% of profiles with a CC >0.955 correspond to Normal biopsies and 100% of profiles with a CC <0.870 correspond to Tumor biopsies (see Fig. 3).

Validation of the mean Normal profile [95] The 28 probe sets selected by the leave-one-out method allowed us to distinguish Normal from Tumor ovarian biopsies in our series of 18 ovarian samples. We then tested if independent ovarian biopsies could be correctly classified by comparing their expression profile to the same mean Normal profile of the 28 classification probe sets.
[96] Fig. 3 summarizes the classification of all ovarian biopsies based on the correlation of 28 probe sets. Remarkably, the profiles of the Normal and Tumor samples of the validation set were clearly separated from each other (see Fig. 3). As in the test set, 100°/~ of profiles with a CC <0.870 correspond to Tumor biopsies. Interestingly, Normal profiles had lower CCs than in the test set, and the threshold correlation value that best separate Normal and Tumor biopsies in the validation set lies between 0.762 and 0.876.
[97] We performed a non-parametric t-test between the average Normal profile of the test set and the average Norma! profile of the validation set. Similarly, we compared the average Tumor profiles of both sets. Since no sfiatistical difference was observed (p=0.373 and p=0.110, respectively), we combined both sets to increase the classification value ofi the 28 probe sets. We compared the expression for all the samples to the mean Normal profile calculated using all 12 Normal biopsy profiiles (8 from the test set and 4 from the validation test). Results confirm that correlation values provide highly-significant separation ~f the Normal biopsy from the Tumor biopsy profiles (see Fig. 4 and Table 5). As seen previously, the profile of Tumor sample (p6168) had a high correlation with the average Normal profile.
Correlafion Paef~veen individual gene e~r~aression and bio~as~ sfafus [98] The selection of probe sets for the classification of ovarian biopsies was originally done based on the profile of the 18 test samples. The good separation of all 36 Normal and Tumor samples (see Fig. 4) with the same probe sets suggested that the genes selected by our method are differentially-expressed in many other ovarian tumors. However, because of tumor heterogeneity, the difference in individual gene expression is likely to vary with the samples analyzed. We evaluated to what extent the 900 probe sets differentially-expressed in the 18 test samples, were also differentially-expressed when all 36 biopsies were analyzed.
[99] Probe sets were ranked from highest absolute PCC to lowest, first using the 18 samples from the test set, and then with all 36 samples from both the test set and the validation set. From the 900 probe sets selected, 694 and 473 had an absolute CC higher than 0.5 with the 18 and 36 samples, respectively; 412 probe sets had a coefficient higher than 0.5 in both cases. Interestingly, from the 28 probe sets originally selected for the biopsy classification, 19 ranked in the top 100; the other 9 probe sets had correlation values ranging from 0.359-0.703.
Genes differentiaU~e expressed between Normal and Tumor ovarian biopsies Genes up-regulated in ovarian tumors [100] Among the genes differentially expressed between Normal and Tumor ovarian biopsies, we detected a few genes already known to be up-regulated in ovarian tumors, such as the genes coding for Claudin 4, topoisomerase II alpha, Kallikrein 8, osteopontin, as well as potential new markers of ovarian cancers (see Table 6).
[101] Claudin 4, a component of tight junctions, has been shown to be up-regulated in ovarian tumors together with another member of this family of transmembrane receptors, Claudin 3. See Hough et al., Cancer Res., Vol. 60, No. 22, pp. 6281-6287 (2000). Costa and colleagues have reported that levels of topoisomerase II alpha correlate with poor prognosis of ovarian surface epithelial neoplasms. Kallikrein 8 has been detected by immunohistochemistry in carcinoma but not Normal ovarian tissue and was suggested as a prognostic marker of ovarian cancer. See Underwood et al., Cancer Res., Vol.
59, No. 17, pp. 4435-4439 (1999); and Magklara et al., Clin. Cancer Res., !/ol. 7, No. 4, pp. 806-811 (2001 ). ~steopontin has also been previously proposed as a diagnostic biomarker f~r ovarian cancer. See tCim et al., J~,f~~, Vol. 287, No. 13, pp. 1671-1679 (2002). Another gene, C20QRF1, has been shown to be expressed in lung carcinoma cell lines but not in normal lung tissues. See i~anda et al., Genomics, Vol. 61, No. 1, pp. 5-14 (1999). Other genes that may have be over-expressed in only some of the biopsies due to the tumor type, the disease stage or other tumor specificity, were not detected by our analytical method.
Genes down-recrulated in ovarian tumors [102] We further examined a large number of genes down-regulated in fihe ovarian tumor biopsies profiled. For analysis purpose, we classified the 28 probe sets and the top 100 down-regulated genes in 8 categories based on the known or suspected function of their product (see Tables 7 and 8). Interestingly, the function of nearly 30% of these 100 genes is still unknown. Most of the other genes play a role in, or are already suspected to be involved in transcription regulation (16 genes), in cell cycle regulation, growth differentiation, cell death or tumor suppression (12 genes) and signal transduction (6 genes). This list includes several potential tumor suppressers: the gene coding for the transforming growth factor beta receptor III (TGF(3R3), a platelet-derived growth factor receptor-like gene (PDGFRL), the suppression of tumorigenicity (ST13) gene, a gene coding for a reversion-inducing-cysteine-rich protein with kazal motif (RECIf) and the paternally expressed 3 (PEG3) gene.
[103] This observation suggests that the genes with still unidentified function are likely to be involved in cell cycle regulation, growth differentiation, signal transduction or transcription regulation. Some of them may act as tumor suppressors. Down-regulation in ovarian tumor or cell lines had been reported for just one of these genes, IGFBP5 which in our study was detected with 2 separate probe sets (see Table >3). See Welsh et al. (2001 ), supra.
[104] ~nly 6 genes coding for proteins of the exfiracellular matrix were noticed including laminin alpha 2 (LAMoc2). Yang and colleagues have reported that transient loss ~f LAMa,2 in the basement membrane of the pre-malignant epithelium and subsequent inactivation of Dab2 are common early event associated with tumorigenicity of the ovarian surface epithelium. See Yang et al., Cancer, Vol. 94, No. 9, pp. 2330-2392 (2002).
Interestingly, down-regulation of Dab2 (probe set 479 at) was also observed in our study with a CC value of 0.49.
[105] Taken together, these results indicated that most of the genes with a statistically significant decreased expression in the ovarian biopsies, are indeed involved in the development or progression of the tumors rather than detected because of a change in cell population or tissue organi~afiion, e.g., loss of connective tissue and fat cells.
~iscussi~n [106] The ~Itering and analytical methods that we used here, provided a list of genes differentially expressed between Normal and Tumor ovarian samples. Viie showed that a small subset (23-42 probe sets) is sufficient to accurately classify ovarian biopsies as Normal or Tumor based on their expression profiles. Validation of this expression signature was done on different biopsies profiled in an independent laboratory, and confirms that the difference in expression observed between the Normal or Tum~r samples reflects a biological process rather than of a laboratory or analytical error.
[107] Several factors not examined here that may affect the detection of differentially expressed genes include the number of samples in the test set, and the heterogeneity of the samples studied. Indeed, it is expected that biopsies and, in particular, Tumor biopsies, have a substantial level of heterogeneity: tumor type, grade, percentage of tumor cells, presence of connective and fat tissues, etc. We studied different types of ovarian tumors of various grades (see Table 1 ) to search for genes involved in common pathways of tumor development and progression, rather than genes involved more specifically in certain types of tumors as previously reported. See Ono et al. (2000), supra; and Welsh et al. (2001 ), supra.
[108] Our clustering analysis of the biopsy expression profiles, revealed that the vast majority of genes that differentiate Normal and Turrr~or samples were down-regulated in the tumors. Indeed, when the 900 most differentially-expressed probes were ranked based on the CC between their expression in all 36 biopsies and the Normal and Tumor status, the top 220 probes (R from 0.865-0.644) were down-regulated in the tumors. We examined more closely the function of the top 100 genes (R from 0.865-0.72), and the top 10 genes over-expressed in the tumors (R from 0.643-0.443). Among the most differentially-expressed genes, we detected several genes already known to be up-regulated in ovarian tumors, as well as potential new markers of ovarian cancers. However, most of the genes were down-regulated, most likely because we studied various types of late stage tumors of different origins, different grades and different tumor cell content. The involvement of many of these genes in transcription regulation, in cell cycle regulation, growth differentiation, signal transduction, cell deafih or tumor suppression underscores the need to further evaluate their role in ovarian cancer. The list of other genes of still unknown function points to n~vel potential players in tumor development and progression.
~lefhods of l~l~dif~lne~ RNA ~dbundances or~4cii~ities [109] fiflethods of modifying RNA abundances and activities currently fall within three classes: ribozymes, antisense species and RNA aptamers. See Good et al., Gene Then , Vol. 4, No. 1, pp. 45-54 (1997). Controllable application or exposure of a cell to these entities permits controllable perturbation ofi RNA abundances.
RibozyPnes [110] Ribozymes are RNAs which are capable of catalyzing RNA cleavage reactions. See Cech, Science, Vol. 236, pp. 1532-1539 (1987); PCT International Publication (1990); Sarver et al., Science, Vol. 247, pp. 1222-1225 (1990). "Hairpin" and "hammerhead"
RNA ribozymes can be designed to specifically cleave a particular target mRNA.
Rules have been established for the design of short RNA molecules with ribozyme activity, which are capable of cleaving other RNA molecules in a highly sequence specific way and can be targeted to virtually all kinds of RNA. See Haseloff et al., Nafure, Vol. 334, pp. 585-591 (1988); Koizumi et al., FEBS Lett., Vol. 228, pp. 228-230 (1988); and ICoizumi et al., FEBS Lett., Vol. 239, pp. 285-288 (1988). Ribozyme methods involve exposing a cell to, inducing expression in a cell, etc. of such small RNA ribozyme molecules. See Grassi and Marini, Annals of Med., Vol. 28, No. 6, pp. 499-510 (1996); and Gibson, Cancer Meta. Rev., Vol. 15, pp. 287-299 (1996).
[111] Ribozymes can be routinely expressed in vivo in sufficient number to be catalytically effective in cleaving mRNA, and thereby modifying mRNA abundances in a cell.
See Cotton et al., EMS~ J.; Vol. 8, pp. 3861-3866 (1989). In particular, a ribozyme coding DNA
sequence, designed according to the previous rules and synthesized, e.g., by standard phosphoramidite chemistry, can be ligated into a restriction enzyme site in the anticodon stem and loop of a gene encoding a tRNA, which can then be transformed into and expressed in a cell of interest by methods routine in the art. Preferably, an inducible promofier, e.g., a glucocorticoid or a tetracycline esponse element, is also introduced into this consfiruct so that ribozyme expression can be selectively confirolled. For saturating use, a highly and constituently active promoter can be used. tDNA genes, i.e., genes encoding tRNAs, are useful in this application because of their small size, high rate of transcription and ubiquitous expression in different kinds of tissues. Therefore, ribozymes can be routinely designed to cleave virtually any mRNA sequence, and a cell can be routinely firansformed with DNA coding for such ribozyme sequences such that a controllable and catalytically effective amount of the ribozyme is expressed. Accordingly, the abundance of virtually any RNA species in a cell can be modified or perturbed.
Antisense molecules [112] In another embodiment, acfiivity of a targefi RNA (preferably mRNA) species, specifically its rate of translafiion, can be controllably inhibited by the controllable application of antisense nucleic acids. Application at high levels results in a saturating inhibition. An "antisense" nucleic acid as used herein refers to a nucleic acid capable of hybridizing to a sequence-specific, e.g., non-poly A, portion of the target RNA, e.g., its translation initiation region, by virtue of some sequence complementary to a coding and/or non-coding region.
The, antisense nucleic acids of the invention can be oligonucleotides that are double-stranded or single-stranded, RNA or DNA or a modification or derivative thereof, which can be directly administered in a controllable manner to a cell or which can be produced intracellularly by transcription of exogenous, introduced sequences in controllable quantities sufficient to perturb translation of the target RNA.
[113] Preferably, antisense nucleic acids are of at least six nucleotides and are preferably oligonucleotides, ranging from 6 oligonucleotides to about 200 oligonucleotides. In specific aspects, the oligonucleotide is at least 10 nucleotides, at least 15 nucleotides, at least 100 nucleotides or at least 200 nucleotides. The oligonucleotides can be DNA
or RNA or chimeric mixtures or derivatives or modified versions thereof, single-stranded or double-stranded. The oligonucleotide can be modified at the base moiety, sugar moiety or phosphate backbone. The oligonucleotide may include other appending groups, such as peptides, or agents facilitating transport across the cell membrane [see, e.g., Letsinger et al., Proc. Natl. Acad. Sci. USA, Vol. 86, pp. 6553-6556 (1989); Lemaitre et al., Proc. Natl. Acad.
Sci. USA, Vol. 84, pp. 648-652 (1987); and PGT Publication No. W~ 88/09810 (1988)], hybridization-triggered cleavage agents [see, e.g., Krol et al., BioTechniques, Vol. 6, pp. 958-976 (1988)] or intercalating agents [see, e.g., Zon, Pharm. 6~es., Vol. 5, No.
9, pp. 539-549 (1988)].
[114] In a preferred aspect of the invention, an antisense oligonucleotide is provided, preferably as single-stranded DNA. The oligonucleotide may be modified at any position on its structure with constituents generally known in the art.
[115] The antisense oligonucleotides may comprise at least one modified base moiety which is selected from the group including, but not limited to, 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine, 5-(carboxyhydroxylmethyl) uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, [3-D-galacfiosylqueosine, inosine, N6-isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimefihylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-adenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, [i-D-mannosylqueosine, 5'-methoxycarboazymethyluracil, 5-methoxyuracil, 2-methylthio-1~6-isopentenyladenine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil, queosine, 2-fihiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid (v), 5-methyl-2-thiouracil, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w and 2,6-diaminopurine.
[116] In another embodiment, the oligonucleotide comprises at least one modified sugar moiety selected from the group including, but not limited to, arabinose, 2-fluoroarabinose, xylulose and hexose.
[117] In yet another embodiment, the oligonucleotide comprises at least one modified phosphate backbone selected from the group consisting of a phosphorothioate, a phosphorodithioate, a phosphoramidothioate, a phosphoramidate, a phosphordiamidate, a methylphosphonate, an alkyl phosphotriester and a formacetal or analog thereof.

[118] In yet another embodiment, the oligonucleotide is a 2-a-anomeric oligonucleotide.
An a-anomeric oligonucleotide forms specific double-stranded hybrids with complementary RNA in which, contrary to the usual B-units, the strands run parallel to each other. See Gautier et al., Nucl. Acids Res., Vol. 15, pp. 6625-6641 (1987).
[119] The oligonucleotide may be conjugated to another molecule, e.g., a peptide, hybridization triggered cross-linking agent, transport agent, hybridization-triggered cleavage agent, etc.
[120] The antisense nucleic acids of the invenfiion comprise a sequence complementary to at least a porfiion of a target RNA species. However, absolute complementary, although preferred, is not required. A sequence "complementary to at least a portion of an RNA", as referred to herein, means a sequence having sufficient complementary to be able to hybridize with the RNA, forming a stable duplex; in the case of double-stranded antisense nucleic acids, a single strand of the duplex DNA may thus be tested or triplex formation may be assayed. The ability to hybridize will depend on both the degree of complementary and the length of the antisense nucleic acid. Generally, the longer the hybridizing nucleic acid, the more base mismatches with a target RNA it may contain and still form a stable duplex (or triplex, as fihe case may be). ~ne skilled in the ark can ascertain a tolerable degree of mismatch by use of standard procedures to determine the melting point of the hybridized complex. The amount of antisense nucleic acid thafi will be effecfiive in the inhibiting translation of the target RNA can be determined by standard assay techniques.
[121] ~ligonucleofiides of the invention may be synthesized by standard methods known in the art, e.g., by use of an aufiomated DNA synthesizer, such as are commercially-available from Siosearch, Applied Biosystems, etc. As examples, phosphorothioate oligonucleotides may be synthesized by the method of Stein et al., NucL Acids Res., Vol. 16, p.
3209 (1988), methylphosphonate oligonucleotides can be prepared by use of controlled pore glass polymer supports, etc. See Sarin et al., Proc. Natl. Acad. Sci. USA, Vol. 85, pp. 7448-7451 (1988). In another embodiment, the oligonucleotide is a 2'-0-methylribonucleotide [see Inoue et al., Nucl. Acids Res., Vol. 15, pp. 6131-6148 (1987)] or a chimeric RNA-DNA
analog [see Inoue et al., FEBS Leit., Vol. 215, pp. 327-330 (1987)].
[122] The synthesized antisense oligonucleotides can then be administered to a cell in a controlled or saturating manner. For example, the antisense oligonucleotides can be placed in the growth environment of the cell at controlled levels where they may be taken up by the cell. The uptake of the antisense oligonucleotides can be assisted by use of methods well-known in the art.

Antisense M~lecules Expressed Intracellularly [123] In an alternative embodiment, the antisense nucleic acids of the invention are controllably expressed intracellularly by transcription from an exogenous sequence. If the expression is controlled to be at a high level, a saturating perturbation or modification results.
For example, a vector can be introduced in vivo such that it is taken up by a cell, within which cell the vector or a portion thereof is transcribed, producing an antisense nucleic acid (RNA) of the invention. Such a vector would contain a sequence encoding the antisense nucleic acid. Such a vector can remain episomal or become chromosomally integrated, as long as it can be transcribed to produce the desired antisense RNA. Such vecfiors can be constructed by recombinant DNA technology methods standard in the art. Vectors can be plasmid, viral or others known in the art, used for replication and expression in mammalian cells.
Expression of the sequences encoding the antisense RNAs can be by any promoter known in the art to act in a cell of interest. Such promoters can be inducible or constitutive. Most preferably, promoters are controllable or inducible by the administration of an exogenous moiety in order to achieve controlled expression of the antisense oligonucleotide. Such controllable promoters include the Tet promoter. ~ther usable promoters for mammalian cells include, but are not limited to, the SV40 early promoter region [see Sernoist and Chambon, Nature, Vol. 290, pp. 304-310 (1931 )], the promoter contained in the 3' long terminal repeat of Rous sarcoma virus [see Yamamoto et al., Cell, Vol. 22, pp.

(1930)], the herpes thymidine kinase promoter [see 11llagner et al., Pr~c.
Natl. Acad. ~ci.
USA, Vol. 73, pp. 1449-1445 (1931)], the regulatory sequences of the metallothionein gene, etc. [see Srinster et al., Nature, Vol. 296, pp. 39-42 (1932)].
[124] Therefore, antisense nucleic acids can be routinely designed to target virtually any mRNA sequence, and a cell can be routinely transformed with or exposed to nucleic acids coding for such antisense sequences such that an effective and controllable or saturating amount of the antisense nucleic acid is expressed. Accordingly the translation of virtually any RNA species in a cell can be modified or perturbed.
RNA A~atamers [125] Finally, in a further embodiment, RNA aptamers can be introduced into or expressed in a cell. RNA aptamers are specific RNA ligands for proteins, such as for Tat and Rev RNA
[see Good et al. (1997), supra] that can specifically inhibit their translation.
Methods of Modifyina Protein Abundances [126] Methods of modifying protein abundances include, inter alia, those altering protein degradation rates and those using antibodies, which bind to proteins affecting abundances of activities of native target protein species. Increasing (or decreasing) the degradation rates of a protein species decreases (or increases) the abundance of that species.
Methods for increasing the degradation rate of a target protein in response to elevated temperature and/or exposure to a particular drug, which are known in the arty can be employed in this invention. For example, one such method employs a heat-inducible or drug-inducible N-terminal degron, which is an IV terminal protein fragment that exposes a degradation signal promoting rapid protein degradation at a higher temperature, e.g., 37°C, and which is hidden to prevent rapid degradation at a lower fiemperature, e.g., 23°C. See ~ohmen et al., Science, Vol. 263, pp. 1273-1276 (1994). Such an exemplary degron is Arg-~HFRts, a variant of murine dihydrofiolate reductase in which the N-terminal Val is replaced by Arg and the Pro at position 66 is replaced with Leu. According to this method, e.g., a gene for a target protein, P, is replaced by standard gene targeting methods known in the art [see Lodish et al., M~lecular ~i~I~gy of fhe Cell, W.H. Freeman and Co., NY, especially Chapter 3 (1995)] with a gene coding for the fusion protein Ub-Arg-~HFR~-P ("Ub" stands for ubiquitin).
The N-terminal ubiquitin is rapidly cleaved after translation exposing the f~
terminal degron.
At lower temperatures, lysines internal to Arg-~HFRts are not exposed, ubiquitination of the fusion protein does not occur, degradation is slow and active target protein levels are high.
At higher temperatures (in the absence of methotrexate), lysines internal to Arg-~HFRt~ are exposed, ubiquitination of the fusion protein occurs, degradation is rapid and active target protein levels are low. This technique also permits controllable modification of degradation rates since heat activation of degradation is controllably blocked by exposure methotrexate.
This method is adaptable to other f~l terminal degrons which are responsive to other inducing factors, such as drugs and temperature changes.
Modifyincr Protein Activity With Antibodies [127] Target protein activities can also be decreased by (neutralizing) antibodies. By providing for controlled or saturating exposure to such antibodies, protein abundancesiactivities can be modified or perturbed in a controlled or saturating manner. For example, antibodies to suitable epitopes on protein surfaces may decrease the abundance, and thereby indirectly decrease the activity, of the wild-type active form of a target protein by aggregating active forms into complexes with less or minimal activity as compared to the wild-type unaggregated wild-type form. Alternately, antibodies may directly decrease protein activity by, e.g., interacting directly with active sites or by blocking access of substrates to active sites. Conversely, in certain cases, (activating) antibodies may also interact with proteins and their active sites to increase resulting activity. In either case, antibodies (of the various types to be described) can be raised against specific protein species (by the methods to be described) and their effects screened. The effects of the antibodies can be assayed and suitable antibodies selected that raise or lower the target protein species concentration and/or activity. Such assays involve introducing antibodies into a cell (see below) and assaying the concentration of the wild-type amount or activities of the target protein by standard means, such as immunoassays, known in the art. The net activity of the wild-type form can be assayed by assay means appropriate to the known activity of the target protein.
[128] Antibodies can be introduced into cells in numerous fashions, including, e.g., microinjection of antibodies into a cell [see Morgan et al., Immunol. Today, Vol. 9, pp. 84-86 (1988)] or transforming hybridoma mRNA encoding a desired antibody into a cell [see Burke et al., Cell, Vol. 36, pp. 847-858 (1984)]. In a further technique, recombinant antib~dies can be engineering and ectopically expressed in a wide variety of non-lymphoid cell types to bind to target proteins, as well as to block target protein activities. See Biocca et al., Trends CeN
Biol., Vol. 5, pp. 248-252 (1995). Expression of the antibody is preferably under control of a controllable promoter, such as the Tet promoter, or a constitutively active promoter (for production of saturating perturbations). A first step is the selection of a particular monoclonal antibody with appropriate specificity to fibs target protein (see below). Then sequences encoding the variable regions of the selected antibody can be cloned into various engineered antibody formats, including, e.g., whole antibody, Fab fragments, Fv fragments, single-chain Fv (ScFv) fragments (VH and V~ regions united by a peptide linker), diabodies (two associated ScFv fragments with different specificities) and so forth. See Hayden et al., Ccerr:
~pin. Irrmunol., Vol. 9, pp. 210-212 (1997). Intracellularly-expressed antibodies of the various formats can be targeted into cellular compartments, e.g., the cytoplasm, the nucleus, the mitochondria, etc., by expressing them as fusions with the various known intracellular leader sequences. See Bradbury et al., Antibody Engineerinq, Borrebaeck, Editor, Vol. 2, pp. 295-361, IRL Press (1995). In particular, the ScFv format appears to be particularly suitable for cytoplasmic targeting.
[129] Antibody types include, but are not limited to, polyclonal, monoclonal, chimeric, single-chain, Fab fragments and an Fab expression library. Various procedures known in the art may be used for the production of polyclonal anfiibodies to a target protein. For production of the antibody, various host animals can be immunized by injection with the target protein, such host animals include, but are not limited to, rabbits, mice, rats, etc.
Various adjuvants can be used to increase the immunological response, depending on the host species and include, but are not limited to, Freund's (complete and incomplete); mineral gels, such as aluminum hydroxide; surface active substances, such as lysolecithin, pluronic polyols, polyanions, peptides, oil emulsions, dinitrophenol; and potentially useful human adjuvants, such as Bacillus Calmette-Guerin (BCG) and corynebacterium parvum.
[130] For preparation of monoclonal antibodies directed towards a target protein, any technique that provides for the production of antibody molecules by continuous cell lines in culture may be used. Such techniques include, but are not restricted to, the hybridoma technique originally developed by Kohler and Milstein, Nature, Vol. 256, pp.
495-497 (1975), the trioma technique, the human B-cell hybridoma technique (see Kozbor et al., Immunol.
T~da,y, V~I. 4, p. 72 (1983)] and fihe EBV hybridoma technique to produce human monoclonal antibodies [see Cole et al., Mon~c!~nal Antibodies and Cancer Therapy, Alan R.
Liss, Inc., pp. 77-96 (1985)]. In an additional embodiment of the invention, monoclonal antibodies can be produced in germ-free animals utilizing recent technology.
See PCT/US90i02545. According to the invention, human antibodies may be used and can be obtained by using human hybridomas (see Cote et al., Proc. IVatl. Acad. Sei.
USA, Vol. 80, pp. 2026-2030 (1983)], or by transforming human B cells with EI3V virus in vitr~ (see Cole et al. (1985), scepra]. In fact, according to the invention, techniques developed for the production of "chimeric antibodies" [see Morrison et al., Pr~e. IVatl. Acad.
Sci. USA, V~I. 81, pp. 6851-6855 (1984); Neuberger et al., Nature, Vol. 312, pp. 604-608 (1984);
Takeda et al., hlattare, Vol. 314, pp. 452-454 (1985)] by splicing the genes from a mouse antibody molecule specific for the target protein together with genes from a human antibody molecule of appropriate biological activity can be used; such antibodies are within the scope of this invention.
(131] Additionally, where monoclonal antibodies are advantageous, they can be alternatively selected from large antibody libraries using the techniques of phage display.
See Marks et al., J. ~ioL Chem., Vol. 267, pp. 16007-16010 (1992). Using this technique, libraries of up to 10'Z different antibodies have been expressed on the surface of fd filamentous phage, creating a "single pot" in vitro immune system of antibodies available for the selection of monoclonal antibodies. See Griffiths et al., EM8~ J., Vol.
13, pp. 3245-3260 (1994). Selecfiion of antibodies from such libraries can be done by techniques known in the art, including contacting the phage to immobilized target protein, selecting and cloning phage bound to the target and subcloning the sequences encoding the antibody variable regions into an appropriate vector expressing a desired antibody format.
[132] According to the invention, techniques described for the production of single-chain antibodies (see U.S. Patent No. 4,946,778) can be adapted to produce single-chain antibodies specific to the target protein. An additional embodiment of the invention utilizes the techniques described for the construction of Fab expression libraries [see Huse et al., Science, Vol. 246, pp. 1275-1281 (1989)] to allow rapid and easy identification of monoclonal Fab fragments with the desired specificity for the target protein.
[133] Antibody fragments that contain the idiotypes of the target protein can be generated by techniques known in the art. For example, such fragments include, but are not limited to, the F(ab')2 fragment which can be produced by pepsin digestion of the antibody m~lecule;
the Fab' fragments that can be generated by reducing the disulfide bridges of the F(ab')~
fragment, the Fab fragments that can be generated by treating the antibody molecule with papain and a reducing agent and Fv fragments.
[134] In the production of antibodies, screening for the desired antibody can be accomplished by techniques known in fihe art, e.g., ELISA. To select antibodies specific to a target protein, one may assay generated hybridomas or a phage display antibody library for an antibody that binds to the target protein.
IVlefhocls of i4fodit5~ine~ Protein Acfivifies [135] Methods of directly modifying protein activities include, inter alia, dominant negative mufiations, specific drugs or chemical moieties and also the use of antibodies, as previously discussed.
[136] ~ominant negative mutations are mutations to endogenous genes or mutant exogenous genes that when expressed in a cell disrupt the activity of a targefied protein species. ~epending on the sfiructure and activity of the targeted protein, general rules eazist that guide the selection of an appropriate strategy for constructing dominant negative mutations that disrupt activity of that target. See Hershkowitz, Nature, Vol.
329, pp. 219-222 (1987). In the case of active monomeric forms, over expression of an inactive form can cause competition for natural substrates or ligands sufficient to significantly reduce net activity of the target protein. Such over expression can be achieved by, e.g., associating a promoter, preferably a controllable or inducible promoter, or also a constitutively expressed promoter, of increased activity with the mutant gene. Alternatively, changes to active site residues can be made so that a virtually irreversible association occurs with the target ligand.
Such can be achieved with certain tyrosine kinases by careful replacement of active site serine residues. See Perlmutter et al., Curr. Opin. Immunol., Vol. 8, pp. 285-290 (1996).
[137] In the case of active multimeric forms, several strategies can guide selection of a dominant negative mutant. Multimeric activity can be decreased in a controlled or saturating manner by expression of genes coding exogenous protein fragments that bind to multimeric association domains and prevent multimer formation. Alternatively, controllable or saturating over-expression of an inactive protein unit of a particular type can tie up wild-type active units in inactive multimers, and thereby decrease multimeric activity. See Nocka et al., EMB~ J., Vol. 9, pp. 1805-1813 (1990). For example, in the case of dimeric DNA binding proteins, the DNA binding domain can be deleted from the DNA binding unit, or the activation domain deleted from the activation unit. Also, in this case, the DNA binding domain unit can be expressed without the domain causing association with the activation unit.
Thereby, DNA
binding sites are tied up without any possible activation of expression. In the case where a particular type of unit normally undergoes a conformational change during activity, expression of a rigid unit can inactivate resultant complexes. For a further example, proteins involved in cellular mechanisms, such as cellular motility, the mitotic process, cellular architecture and so forth, are typically composed of associations of many subunits of a few types. These structures are often highly sensitive to disruption by inclusion of a few monomeric units with structural defects. Such mutant monomers disrupt the relevant protein activities and can be expressed in a cell in a controlled or saturating manner.
[138] In addition to dominant negative mufiations, mutant target proteins that are sensitive to temperature (or other exogenous facfiors) can be found by mutagenesis and screening procedures that are well-known in the art.
[139] Also, one of skill in the art will appreciate that expression of anfiibodies binding and inhibiting a target protein can be employed as another dominant negative strategy.
~7~clli5~ir~e~ Pr~~eir~s I~i~M Small f~7olectele ~ru~s [140] Finally, activities of certain target proteins can be modified or perturbed in a controlled or a saturating manner by exposure to exogenous drugs or ligands.
Since the methods of this invention are often applied to testing or confirming the usefulness of various drugs to treat cancer, drug exposure is an important method of modifying/perturbing cellular constituents, both mRNAs and expressed proteins. In a preferred embodiment, input cellular constituents are perturbed either by drug exposure or genetic manipulation, such as gene deletion or knockout; and system responses are measured by gene expression technologies, such as hybridization to gene transcript arrays (described in the following).
[141] In a preferable case, a drug is known that interacts with only one target protein in the cell and alters the activity of only that one target protein, either increasing or decreasing the activity. Graded exposure of a cell to varying amounts of that drug thereby causes graded perturbations of network models having that target protein as an input.
Saturating exposure causes saturating modification/perturbation. For example, Cyclosporin A is a very specific regulator of the calcineurin protein, acting via a complex with cyclophilin. A titration series of Cyclosporin A therefore can be used to generate any desired amount of inhibition of the calcineurin protein. Alternately, saturating exposure to Cyclosporin A
will maximally inhibit the calcineurin protein.
Measurement Meth~ds [142] The experimental methods of this invention depend on measurements of cellular constituents. The cellular constituents measured can be from any aspecfi of the bi~logical state of a cell. They can be from the transcriptional state, in which RNA
abundances are measured, the translation state, in which protein abundances are measured, the activity state, in which protein activities are measured. The cellular characteristics can also be from mixed aspects, e.g., in which the activities of one or more proteins are measured along with the RNA abundances (gene expressions) of other cellular constituents. This section describes exemplary methods for measuring the cellular constituents in drug or pathway responses. This invention is adaptable to other methods of such measurement.
[143] Prefierably, in this invention the transcriptional state of the ofiher cellular constituents are measured. The firanscriptional state can be measured by techniques of hybridization to arrays of nucleic acid or nucleic acid mimic probes, described in the next subsection, or by other gene expression technologies, described in the subsequent subsection.
However measured, the result is data including values representing mRNA abundance and/or ratios, which usually refilect ~NA expression ratios (in the absence of differences in RNA
degradafiion rates).
[144] In various alternative embodiments of the present invention, aspects of the biological state ~fiher than the transcriptional state, such as the translational state, the activity state or mixed aspects can be measured.
[145] In all embodiments, measurements of the cellular constituents should be made in a manner that is relatively independent of when the measurement are made.
Transcriational Stafe Measurement [146] Preferably, measurement of the transcriptional state is made by hybridization to transcript arrays, which are described in this subsection. Certain other methods of transcriptional state measurement are described later in this subsection.
Transcript Arrays Generalhr [147] In a preferred embodiment the present invention makes use of "transcript arrays", also called herein "microarrays". Transcript arrays can be employed for analyzing the transcriptional state in a cell, and especially for measuring the transcriptional states of cancer cells.
[148] In one embodiment, transcript arrays are produced by hybridizing detectably-labeled polynucleotides representing the mRNA transcripts present in a cell, e.g., fluorescently-labeled cDNA synthesized from total cell mRNA, to a microarray. A microarray is a surface with an ordered array of binding, e.g., hybridization, sites for products of many of the genes in the genome of a cell or organism, preferably most or almost all of the genes. Microarrays can be made in a number of ways, of which several are described below. However produced, microarrays share certain characteristics. The arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared with each other.
Preferably the microarrays are small, usually smaller than 5 cm~ and they are made from materials that are stable under binding, e.g. nucleic acid hybridization, conditions. A given binding site or unique set of binding sites in the microarray will specifically bind the product of a single gene in the cell. Although there may be more than one physical binding site (hereinafter "site") per specific mRNA, for the sake of clarity the discussion below will assume that there is a single site. In a specific embodiment, positionally-addressable arrays containing affixed nucleic acids of known sequence at each location are used.
[149] It will be appreciated that when cDNA complementary to the RNA of a cell is made and hybridized to a microarray under suitable hybridization condifiions, the level of hybridization fio the site in the array corresponding to any parfiicular gene will reflect the prevalence in the cell of mRNA transcribed from that gene. For example, when detestably labeled, e.g., with a fluorophore, cDNA complementary to the total cellular mRNA is hybridized to a microarray, the site on the array corresponding to a gene, i.e., capable of specifically binding the product of the gene, that is not transcribed in the cell will have little or no signal, e.g., fluorescent signal, and a gene for which the encoded mRNA is prevalent will have a relatively strong signal.
Pre,oarafion of Microarra~rs [150] Microarrays are known in the art and consist of a surface to which probes that correspond in sequence to gene products, e.g., cDNAs, mRNAs, cRNAs, polypeptides and fragments thereof, can be specifically hybridized or bound at a known position. In one embodiment, the microarray is an array, i.e., a matrix, in which each position represents a discrete binding site for a product encoded by a gene, e.g., a protein or RNA, and in which binding sites are present for products of most or almost all of the genes in the organism's genome. In a preferred embodiment, the "binding site", hereinafter "site", is a nucleic acid or nucleic acid analogue to which a particular cognate cDNA can specifically hybridize. The nucleic acid or analogue of the binding site can be, e.g., a synthetic oligomer, a full-length cDNA, a less than full-length cDNA or a gene fragment.
[151] Although in a preferred embodiment the microarray contains binding sites for products of all or almost all genes in the target organism's genome, such comprehensiveness is not necessarily required. lJsually the microarray will have binding sites corresponding to at least about 50% of the genes in the genome, often at least about 75%, more often at least about 55%, even more often more than about 90%, and most often at least about 99%. Preferably, the microarray has binding sites for genes relevant to testing and confirming a biological network model of interest. A "gene" is identified as an open reading frame (~RF) of preferably at least 50, 75 or 99 amino acids from which a mRNA is transcribed in the organism, e.g., if a single cell, or in some cell in a multicellular organism.
The number of genes in a genome can be estimated from the number of mRNAs expressed by the organism, or by extrapolation from a well-characterized portion of the genome. When the genome of fihe organism of interest has been sequenced, the number of ~RFs can be determined and mRNA coding regions identified by analysis of the DNA sequence.
For example, the SaceMar~Pnyees cerevisiae genome has been completely sequenced and is reported to have approximately 6,275 ~RFs longer than 99 amino acids. Analysis of these ~RFs indicates that there are 5,555 ORFs that are likely to specify protein products. See Goffeau et al., Science, Vol. 274, pp. 546-567 (1996), which is incorporated by reference in its entirety for all purposes. In contrast, the human genome is estimated to contain approximately 105 genes.
Pre~aarina Nucleic Acids f~r IVlieroarrays [152] As noted above, the "binding site" to which a particular cognate cDNA
specifically hybridizes is usually a nucleic acid or nucleic acid analogue attached at that binding site. In one embodiment, the binding sites of the microarray are DNA polynucleotides corresponding to at least a portion of each gene in an organism's genome. These DNAs can be obtained by, e.g., PCR amplification of gene segments from genomic DNA, cDNA, e.g., by RT-PCR, or cloned sequences. PCR primers are chosen, based on the known sequence of the genes or cDNA, that result in amplification of unique fragments, i.e., fragments that do not share more than 10 bases of contiguous identical sequence with any other fragment on the microarray.
Computer programs are useful in the design of primers with the required specificity and optimal amplification properties. See, e.g., Oligo pl version 5.0, National Biosciences. In the case of binding sites corresponding to very long genes, it will sometimes be desirable to amplify segments near the 3' end of the gene so that when oligo-dT primed cDNA
probes are hybridized to the microarray, less-than-full length probes will bind efficiently. Typically each gene fragment on the microarray will be between about 50 by and about 2000 bp, more typically between about 100 by and about 1000 bp, and usually between about 300 by and about 800 by in length. PCR methods are well-known and are described, e.g., in Innis et al., eds., PGR Pr~tocols: A Guide to Methods and Applieati~ns, Academic Press Inc., San Diego, CA (1990), which is incorporated by reference in its entirety for all purposes. It will be apparent that computer-controlled robotic systems are useful for isolating and amplifying nucleic acids.
[153] An alternative means for generating the nucleic acid for the microarray is by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N
phosphonate or phosphoramidite chemistries. See Froehler et al., Nucleie Acid Res., Vol. 14, pp. 5399-5407 (1986); and McBride et al., Tetrahedr~n Lett., Vol. 24, pp. 245-248 (1983).
Synthetic sequences are between about 15 bases and about 500 bases in length, more typically betuveen about 20 bases and about 50 bases. In some embodiments, synthetic nucleic acids include non-natural bases, e.g., inosine. As noted above, nucleic acid analogues may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid. See, e.g., Egholm et al., Nature, Vol. 365, pp. 566-568 (1993); and also U.S. Patent No. 5,539,083.
[154] In an alternative embodiment, the binding (hybridization) sites are made from plasmid or pha~ge clones of genes, cDl~As, e.g., ea~pressed sequence tags, or inserts therefrom. See Nguyen et al., Geno'nics, Vol. 29, pp. 207-209 (1995). In yet another embodiment, the polynucleotide of the binding sites is RNA.
Attaching Nucleic Acids to the Solid Surface [155] The nucleic acid or analogue are attached to a solid support, which may be made from glass, plastic, e.g., polypropylene and nylon, polyacrylamide, nitrocellulose or other materials. A preferred method for attaching the nucleic acids to a surface is by printing on glass plates, as is described generally by Schena et al., Science, Vol. 270, pp. 467-470 (1995). This method is especially useful for preparing microarrays of cDNA.
See, also, DeRisi et al., Nat. Genet., Vol. 14, pp. 457-460 (1996); Shalon et al., Genome Res., Vol. 6, pp. 639-645 (1996); and Schena et al., Proc. Natl. Acad. Sci. USA, Vol. 93, pp. 10539-11286 (1995). Each of the aforementioned articles is incorporated by reference in its entirety for all purposes.

[156] A second preferred method for making microarrays is by making high-density oligonucleotide arrays. Techniques are known for producing arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for synthesis in situ [see Fodor et al., Seience, Vol. 251, pp. 767-773 (1991 ); Pease et al., Proc. NatL Acad. Sci. USA, Vol. 91, No. 11, pp. 5022-5026 (1994); Lockhart et al., Nat. ~iotechnol., Vol. 14, p. 1675 (1996); and tJ.S.
Patent Nos. 5,578,832; 5,556,752; and 5,510,270, each of which is incorporated by reference in its entirety for all purposes] or other methods for rapid synthesis and deposition of defined oligonucleotides [see Blanchard et al., Biosens. Si~electr~n., Vol. 11, pp.
687-690 (1996)].
When these methods are used, oligonucleotides, e.g., 20 mers, of known sequence are synthesized directly on a surface such as a derivafiized glass slide. Usually, the array produced is redundant, with several oligonucleotide molecules per RNA.
Oligonucleotide probes can be chosen to detect alternatively spliced mRNAs.
[157] Other methods for making microarrays, e.g., by masking, may also be used. See Niaskos and Southern, Nucleic Acids Res., Vol. 20, pp. 1679-1684 (1992). In principal, any type of array, e.g., dot blots on a nylon hybridisation membrane [see Sambrook et al., Molecular Cloning__A Laiaoratory Manual, 2"d Edition, Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, Nlf (1989), which is incorporated in its entirety fior all purposes], could be used, although, as will be recognised by those of skill in the art, very small arrays will be preferred because hybridisation volumes will be smaller.
Generatine~ Labeled Probes [158] f~iethods for preparing total and poly(A)+ RNA are well-known and are described generally in Sambrook et al. (1989), supra. In one embodiment, RNA is extracted from cells of the various types of interest in this invention using guanidinium thiocyanate lysis followed by CsCI centrifugation. See Chirgwin et al., Biochemistry, Vol. 18, pp. 5294-5299 (1979).
Poly(A)'" RNA is selected by selection with oligo-dT cellulose. See Sambrook et al. (1989), supra. Cells of interest include wild-type cells, drug-exposed wild-type cells, cells with modifiedlperturbed cellular constituent(s), and drug-exposed cells with modified/perturbed cellular constituent(s).
[159] Labeled cDNA is prepared from mRNA by oligo dT-primed or random-primed reverse transcription, both of which are well-known in the art. See, e.g., Klug and Berger, Methods Enzymol., Vol. 152, pp. 316-325 (1987). Reverse transcription may be carried out in the presence of a dNTP conjugated to a detectable label, most preferably a fluorescently-labeled dNTP. Alternatively, isolated mRNA can be converted to labeled antisense RNA

synthesized by in vitro transcription of double-stranded cDNA in the presence of labeled dNTPs. See Lockhart et al. (1996), supra, which is incorporated by reference in its entirety for all purposes. In alternative embodiments, the cDNA or RNA probe can be synthesized in the absence of detecfiable label and may be labeled subsequently, e.g., by incorporating biotinylated dNTPs or rNTP, or some similar means, e.g., photo-cross-linking a ps~ralen derivative of biotin to RNAs, followed by addition of labeled streptavidin, e.g., phycoerythrin-conjugated streptavidin or the equivalent.
[160] When fluorescently-labeled probes are used, many suitable fluorophores are known, including fluorescein, lissamine, phycoeryfihrin, rhodamine (Perkin Elmer Cetus), Cy2, Cy3, Cy3.5, CyS, Cy5.5, Cy7, FIuorX (Amersham) and others. See, e.g., ICricka, Nonis~topic DNA
Probe Techniques, Academic Press, San Diego, CA (1992). It will be appreciated that pairs of flu~rophores are chosen that have distinct emission spectra so that they can be easily distinguished.
[161] In another embodiment, a label other than a fluorescent label is used.
For example, a radioactive label, or a pair of radioactive labels with distinct emission spectra, can be used.
See ~hao et al., Gene, Vol. 156, p. 207 (1995); and Pietu et al., Genopne Res., Vol. 6, p. 492 (1996). However, because of scattering of radioactive particles, and the consequent requirement for widely-spaced binding sites, use of radioisotopes is a less-preferred embodiment.
[162] In one embodiment, labeled cDNA is synthesized by incubating a mixfiure containing 0.5 mM dGTP, dATP and dCTP plus 0.1 mM dTTP plus fluorescent deoxyribonucleotides, e.g., 0.1 mM Rhodamine 110 UTP (Perken Elmer Cetus) or 0.1 mM Cy3 dIJTP
(Amersham), with reverse transcriptase, e.g., SuperScript.TM. II, LTI Inc., at 42°C
for 60 minutes.
Hybridization to Microarra~es [163] Nucleic acid hybridization and wash conditions are chosen so that the probe "specifically binds" or "specifically hybridizes" to a specific array site, i.e., the probe hybridizes, duplexes or binds to a sequence array site with a complemenfiary nucleic acid sequence but does not hybridize to a site with a non-complementary nucleic acid sequence.
As used herein, one polynucleotide sequence is considered complementary to another when, if the shorter of the polynucleotides is s25 bases, there are no mismatches using standard base-pairing rules or, if the shorter of the polynucleotides is longer than 25 bases, there is no more than a 5% mismatch. Preferably, the polynucleotides are perfectly complementary (no mismatches). It can easily be demonstrated that specific hybridization conditions result in specific hybridization by carrying out a hybridization assay including negative controls. See, e.g., Shalon et al. (1996), supra; and Ghee et al., supra.
[164] ~ptimal hybridization conditions will depend on the length, e.g., oligomer vs.
polynucleotide >200 bases; and type, e.g., RNA, DNA and PNA, of labeled probe and immobilized polynucleotide or oligonucleotide. General parameters for specific, i.e., stringent, hybridization conditions for nucleic acids are described in Sambrook et al. (1996), supra; and Ausubel et al., Current Pr~toeols in Molecular Si~I~gy, Greene Publishing and Wiley-Interscience, Nl~ (1987), which is incorporated in its entirety for all purposes. Vllhen the cDNA microarrays of Schena et al. are used, typical hybridization conditions are hybridization in 5 x SSC plus 0.2% SDS at 65°C for 4 hours followed by washes at 25~C in low-stringency wash buffer (1 x SSC plus 0.2% SDS) followed by 10 minutes at 25°C in high-stringency wash buffer (0.1 x SSC plus 0.2% SDS). See Shena et al., Pr~c.
Natl. Acad. Sci.
USA, Vol. 93, p. 10614 (1996). Useful hybridization conditions are also provided. See, e.g., Tijessen, Hybridization lMith Nucleic Acid Pr~bes, Elsevier Science Publishers B.V. (1993);
and Kricka (1992), supra.
Signal ~eteeti~r~ and ~ata A~al~sis [165] Vlihen filuorescently-labeled probes are used, the fluorescence emissions at each site of a transcript array can be, preferably, detected by scanning confocal laser microscopy.
In one embodiment, a separate scan, using the appropriate excitation line, is carried out for each of the two fluorophores used. Alternatively, a laser can be used that allows simultaneous specimen illumination at wavelengths specific to fihe two fluorophores and emissions from the two fluorophores can be analyzed simultaneously. See Shalon et al.
(1996), supra, which is incorporated by reference in ifis enfiirety for all purposes. In a preferred embodiment, the arrays are scanned with a laser fluorescent scanner with a computer-controlled X-Y stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line, mixed gas laser and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al. (1996), supra and in other references cited herein.
Alternatively, the fiber-optic bundle described by Ferguson et al., Nat.
BioteehnoL, Vol. 14, pp. 1681-1684 (1996), may be used to monitor mRNA abundance levels at a large number of sites simultaneously.
[166] Signals are recorded and, in a preferred embodiment, analyzed by computer, e.g., using a 12-bit analog to digital board. In one embodiment the scanned image is de-speckled using a graphics program, e.g., Hijaak Graphics Suite, and then analyzed using an image gridding program that creates a spreadsheet of the average hybridization at each wavelength at each site. If necessary, an experimentally determined correction for "cross talk" (or overlap) between the channels for the two fluorophores may be made. For any particular hybridization site on the transcript array, a ratio of the emission of the two fluoroph~res is preferably calculated. The ratio is independent of the absolute expression level of the cognate gene, but is useful for genes whose expression is signii=tcantly modulated by drug administration, gene deletion or any other tested event.
[167] Preferably, in addition to identifying a perturbation as positive or negative, it is advantageous to determine the magnitude of the perturbation. This can be carried oufi by methods that will be readily apparent to those of skill in the art.
~fher Methods of Transcriptions! State Measurerrrent [168] The transcriptions! state of a cell may be measured by other gene expression technologies known in the art. Several such technologies produce pools of resfiriction fragments of limited complexity for electrophoretic analysis, such as methods combining double restriction enzyme digestion with phasing primers [see, e.g., EP 0 534858 A1 (1992), ~abeau et al.], or methods selecting restriction fragmenfis with sites closest to a defined mRNP, end [see, e.g., Prashar et al., Pr~e, fVatl. Acad. Sei. USA, Vol. 93, pp. 659-663 (1996)]. ~fiher methods statistically sample cDNA pools, such as by sequencing sufficient bases, e.g., 20-50 bases, in each of multiple cDN,4s to identify each cDNA, or by sequencing shock tags, e.g., 9-10 bases, which are generated at known positions relative to a defined mf~NA end pathway pattern. See, e.g., Velculescu, Science, Col. 2~0, pp. 484-48~ (1995).
Meascarement ofi ~ther Aspects [169] In various embodiments of the present invention, aspects of the biological state other than the transcriptions! state, such as the translations! state, the activity state or mixed aspects can be measured in order to obtain drug and pathway responses. Details of these embodiments are described in this section.
Translations! State Measurements [170] Measurement of the translations! state may be pertormed according to several methods. For example, whole genome monitoring of protein, i.e., the "proteome"
[see Goffeau et al. (1996), supra], can be carried out by constructing a microarray in which binding sites comprise immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome. Preferably, antibodies are present for a substantial fraction of the encoded proteins, or at least for those proteins relevant to testing or confirming a biological network model of interest. Methods for making monoclonal antibodies are well-known. See, e.g., Harlow and Lane, Antibodies: A
Lab~ratory Manual, Cold Spring Harbor, NY (1988), which ~is incorporated in its entirety for all purposes. In a preferred embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell. With such an antibody array, proteins from the cell are contacted to the array and their binding is assayed with assays known in the art.
(171] Alternatively, proteins can be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and typically involves iso-electric focusing along a first dimension followed by SDS-PAGE
electrophoresis along a second dimension. See, e.g., Hames et al., Gel Electrophoresis of Proteins: A
Practical Appr~ach, IRL Press, NY (1990); Shevchenko et al., Pr~c. Natl. Acad. Sci. USA, Vol. 93, pp. 1440-1445 (1996); Sagliocco et al., Yeast, Vol. 12, pp. 1519-1533 (1996);
Lander, Science, Vol. 274, pp. 536-539 (1996). The resulting electropherograms can be analyzed by numerous techniques, including mass spectrometric techniques, western blotting and immunoblot analysis using polyclonal and monoclonal antibodies, and internal and N-terminal micro-sequencing. lJsing these techniques, it is possible to identify a substantial fraction of all the proteins produced under given physiological conditions, including in cells, e.g., in yeast; exposed to a drug or in cells modifiied by, e.g., deletion or over-expression of a specific gene.
Ernb~diments Sased on ~ther As~aects ofi the Si~I~erical State [17~] Although monitoring cellular constituents other than mRi~A abundances currently presents certain technical difficulties not encountered in monitoring mRNAs, it will be apparent to those ofi skill in the art that the use of methods of this invention that the activities of proteins relevant to the characterization of cell function can be measured, embodiments of this invention can be based on such measurements. Activity measurements can be performed by any functional, biochemical or physical means appropriate to the particular activity being characterized. Where the activity involves a chemical transformation, the cellular protein can be contacted with the natural substrates and the rate of transformation measured. Where the activity involves association in multimeric units, e.g., association of an activated DNA-binding complex with DNA, the amount of associated protein or secondary consequences of the association, such as amounts of mRNA transcribed, can be measured.
Also, where only a functional activity is known, e.g., as in cell cycle control, performance of the function can be observed. However known and measured, the changes in protein activities form the response data analyzed by the foregoing methods of this invention.

[173] In alternative and non-limiting embodiments, response data may be formed of mixed aspects of the biological state of a cell. Response data can be constructed from, e.g., changes in certain mRNA abundances, changes in certain protein abundances, and changes in certain protein activities.
G~m~outer lm,clementations (174] In a preferred embodiment, the computation steps of the previous methods are implemented on a computer system or on one or more networked computer systems in order to provide a powerful and convenient facility for forming and testing models of biological systems. The computer sysfiem may be a single hardware platform comprising internal components and being linked to external components. The infiernal components of this computer system include processor element interconnected with a main memory.
For example computer system can be an Intel Pentium based processor of 200 Mhz or greater clock rate and with 32 MS or more of main memory.
[175] The external components include mass data storage. This mass storage can be one or more hard disles, which are typically packaged together with the processor and memory. Typically, such hard disks provide for at least 1 Go3 of storage.
~ther external components include user interface device, which can be a monitor and keyboards, together with pointing device, which can be a "mouse" or other graphic input devices.
Typically, the computer system is also linked to other local computer systems, remote computer systems, or wide area communication networks, such as the Internet. This network link allows the computer system to share data and processing tastes with other computer systems.
[176] Loaded into memory during operation of this system are several software components, which are both standard in the art and special to the instant invention. These software components collectively cause the computer system to function according to the methods of this invention. These software components are typically stored on mass storage.
Alternatively, the software components may be stored on removable media such as floppy disks or CD-R~M (not illustrated). The software component represents the operating system, which is responsible for managing the computer system and its network interconnections. This operating system can be, e.g., of the Microsoft Windows family, such as Windows 95, Windows 98 or Windows NT; or a Unix operating system, such as Sun Solaris. Software include common languages and functions conveniently present on this system to assist programs implementing the methods specific to this invention.
Languages that can be used to program the analytic methods of this invention include C, C++ or, less preferably, JAVA. Most preferably, the methods of this invention are programmed in mathematical software packages which allow symbolic entry ofi equations and high-level specification of processing, including algorithms to be used, thereby freeing a user of the need to procedurally program individual equations or algorithms. Such packages include, e.g., Matlab from Mathworks (Natick, Mass.), Mathematica from Wolfram Research (Champaign, IL) and MathCAD from Mathsoft (Cambridge, MA).
[177] In preferred embodiments, the analytic software component actually comprises separate software components which interact with sash other. Analytic soffinrare represents a database containing all data necessary for the operation of the system. Such data will generally include, but is not necessarily limited to, results ofi prior experiments, genome data, experimental procedures and cost and other infiormation which will be apparent to those skilled in the art. Analytic software includes a data reduction and computation component comprising one or more programs which execute the analytic methods of the invention.
[17~] Analytic software also includes a user interi'ace which provides a user of the computer system with control and input of test network models, and, optionally, experimental data. The user interface may comprise a drag-and-drop interfiace fi~r specifying hypotheses to the system. The user interface may also comprise means fior loading experimental data from the mass storage component, e.g., the hard drive; from removable media, e.g., floppy disks or C~-o~~M; or from a dififerent computer system communicating with the instant sysfiem over a network, e.g., a local area network, or a wide area communication network, such as the Internet.
[17g] Alternative systems and methods for implementing the analytic methods of this invention will be apparent to one ofi skill in the art and are infiended to be comprehended within the accompanying claims. In particular, the accompanying claims are intended to include the alternative program structures for implementing the methods of this invention that will be readily apparent to one of skill in the art.

Table 2. Quantification of Purified Total RNA and cRNA
Weight RNA Yield cRNA Yield cRNA Used Sample code (mg) Og) ~NJ) (N9) p2437 - - 30.0 15.0 p2709 - - 36.3 15.0 p5720 - - 29.0 15.0 p5721 - -21.5 15.0 p6166 56.3 45.0 58.8 15.0 p6167 80.0 9.4 47.1 15.0 p6168 30.0 7.8 42.4 15.0 p6169 65.6 7.4 48.4 15.0 p6170 51.9 40.9 13.2 13.2 p6171 90.0 16.1 52.9 15.0 p6172 47.0 9.1 29.6 15.0 p6173 88.0 15.7 31.5 15.0 p6174 86.1 26.6 13.9 13.9 p6175 38.0 - -0.8 p6176 80.2 57.1 14.1 14.1 p6177 71.0 ~ 33.2 19.6 15.0 p6178 77.0 16.4 35.4 15.0 p6179 47.4 - -1.1 p6180 60.5 89.9 36.4 15.0 p6181 24.2 8.2 19.6 15.0 p6182 70.0 63.7 15.0 15.0 p6183 58.6 7.1 9.8 -p6184 84.5 38.1 12.8 12.8 p6185 ~ --90.0 6.7 25.2 15.0 Table 3. Summary of Experiment QC
of Laboratory Sample CodeChip Baclc-Scaling Genes GAPDHB-actin(where DesignationgroundFactor Present3'lS'3'15' proftes were oeneratea) p2437 p2437e 893 0.18 52.46 2.04 4.51 PG

p2709 p2709e 776 0.35 46.26 2.09 1.08 PG

p5720 p5720-Zee 74 3.05 48.09 1.63 1.14 PG

p5721 p5721-Zee 55 3.24 47.90 5.09 6.91 PG

p6166 p6166ee 60 1.72 50.27 1.55 1.38 PG

p6167 p6167ee 48 2.71 53.12 2.35 3.03 PG

p6168 p6168ee 46 2.71 53.81 2.76 2.54 PG

p6169 p6169ee 59 14.62 22.55 5.36 10.38 PG

p6170 p6170ee 55 2.14 38.24 1.60 2.06 PG

p6171 p6171 ee 47 2.27 55.19 2.47 2.99 PG

p6172 p6172ee 55 3.73 46.84 4.43 4.53 PG

p6173 p6173ee 61 2.43 51.01 7.62 5.99 PG

p6174 p6174ee 82 1.19 53.04 1.38 1.80 PG

p6176 p6176ee 60 2.28 48.59 1.49 2.44 PG

p6177 p6177ee 97 2.31 46.64 1.73 2.06 PG

p6178 p6178ee 73 2.65 41.76 1.65 1.81 PG

p6180 p6180ee 43 10.59 27.84 4.46 13.90 PG

p6181 p6181 ee 65 2.60 46.08 1.93 1.94 PG

p6182 p6182ee 43 5.39 37.16 4.48 10.86 PG

p6184 p6184ee 59 3.46 49.31 2.44 2.36 PG

p6185 p6185ee 4.9 8.18 40.63 3.67 4.51 PG

OVR1T h91ms01031301122 1.55 43.62 1.62 1.61 GNF

OVR2T h91ms01030602119 1.70 43.16 1.69 1.91 GNF

OVRST h91ms0103060386 0.92 52.46 1.30 2.33 GNF

OVRBT h91ms0103060476 1.27 50.49 1.65 1.93 GNF

OVR10T h91ms0103071480 1.12 49.86 1.15 2.41 GNF

OVR11T h91ms0103060571 1.40 47.29 1.90 3.34 GNF

OVR12T h91ms0103060678 1.69 43.68 1.57 1.68 GNF

OVR13T h91ms0103060775 0.75 51.90 1.26 1.80 GNF

OVR16T h91ms01030501168 0.86 39.03 1.68 2.95 GNF

OVR19T h91ms0103050280 1.22 43.54 1.49 2.17 GNF

OVR22T h91ms0103060883 1.22 43.54 1.61 2.82 GNF

OVR26T h91ms0103060985 1.01 46.21 1.38 2.66 GNF

OVR27T h91ms01030503104 0.72 49.99 1.40 2.02 GNF

OVR28T h91ms01030504140 1.40 35.17 2.80 6.37 GNF

OVR102N h91ms00102618130 2.02 33.01 3.49 5.60 GNF

OVR278EN h9Ims0103050559 3.70 32.96 2.20 3.60 GNF

OVR278SN h91ms0103071557 3.12 36.10 1.87 2.53 GNF
~

of Laboratory Chip Back- Scaling Genes GAPDH B-actin(where Profies Sample DesignationgroundFactor Present3'/5' 3'/5' were venerated) Code HUOVR h91ms0010262263 1.16 51.60 1.43 2.72 GNF

Table 4. Comparison of Predicted Vs. ~bserved Status of 1~ ~varian Test Samples With a 2~ Probe Sets Expression Profile Biopsy status ~bs. (Exp.) r S 0.920 r > 0.920 Total Normal 1 (4.44) 7 (3.56) 8 Tumor 9 (5.56) 1 (4.44) 1 Total 10 3 1 ~

Note: The number of observations is shown for each group of samples, with the value expected under random association in parentheses.
"r" = the PCC value of the 23 probe set profile of a biopsy sample with the mean Normal profile.
OR = 63 (95% CI: 3.3-1194.7), p=0.0029 Tabie 5. C~mpariaon of Predicted Va. ~b~erved Status of ~~a ~varian Samples With a ~~, 3~ or 4~ Probe Sets Expression Profile Number of Probe Sets 25 32 42 Correlation threshold 0.914 0.933 0.651 used 95% CI 14.4-4432.9 14.6-10339.69.6-4682.5 p-value (Fischers exact 2.3 x 10-~ 1.0 x 10'~ 1.3 x 10-~
test) Table 6. List of Genes Up-Regulated in ~varian Tumors Absolute CC values are shown for expression levels analyzed in the 18 test samples only (R1 ) or in all 36 samples (R2).
Probe Gene Cytogenefiic Set Symbol ~escription Location R1 R2 Name 40145_atT~P2A Topoisomerase (DNA) II 17q21-q22 0.6430.636 alpha (170kD) 39109 C200RF1 Chromosome 20 ~RF 1 20q11.2 0.6230.564 at 39829 ARL7 ADP-ribosylation factor-like2q37.2 0.6180.656 at 7 37985 LMNB1 Lamin B1 5q23.3-q31.10.5650.608 at 2092_s SPP1 Secreted phosphoprotein 4q21-q25 0.5600.679 at ( 1 osteopontin) 38116 ICIAA0101iCIAA0101 gene product 15q22.1 0.5560.595 at 34259 tCIAA0664KIAA0664 protein 17p13.3 0.5440.541 at 35276 CLDN4 Claudin 4 7q11.23 0.4710.575 at 149 at DDXL Nuclear RNA helicase 19p13.13 0.4570.544 37131 I<LICB iCallikrein 8 (neuropsinlovasin)19q13.3-q13.40.4430.585 at Table 7. Functional ~ategor6es of the host ~ifEerentially-~~pressed Genes in ~varian dancer ~stegories 1'0~ 2~ Probe Toga i~~~Pr~be Sets Sets Cell cycle regulation 8 13*
Growth differentiation and cell death Tumor suppression Transcription regulation 1 17*

Signal transduction ~ 3 9*

Metabolic enzymes 5 9 Cytoskeletal proteins 1 5 Extracellular matrix 1 6 Others 1 14 Unknown 8 27 *For 5 genes (GPRK5, IGFBPS, IRS1, ITPR1 and RBPMS) similar results were obtained with 2 different probe sets.

Table ~ List of Genes Down-Regulated in Ovarian Tumors Functional Category Probe Set Gene Gytogenetic Name Symbol Description Location R1 R2 Rank Cell cycle tumor suppression regulation, growth differentiation, cell death, 38120 at PKD2 Polycystic kidney 4q21-q23 0.8520.76825 d isease 2 34257 at AIP1 Atrophin-1 interacting7q21 0.8460.78613 protein 1 38650 at IGFBPS Insulin-like growth2q33-34 0.8400.748 factor b inding protein 1396 at IGFBP5 Insulin-like growth2q33-q36 0.8210.756 factor b inding protein 1897 at TGF~3R3 Transforming growth1p33-p32 0.8090.77023 f actor, beta receptor III

36073 at NDN Necdin homolog 15q11.2-q12 0.8040.77421 36160 s PTPRN2 Protein tyrosine 7q36 0.7880.8463 at phosphatase, receptor type, N polypeptide 37643 at TNFRSF6 Tumor necrosis 10q24.1 0.7790.716 factor receptor superfamily, member 6 1640 at ST13 Suppression of 22q13.2 0.7690.734 tumorigenicity 35234_at RECIC Reversion-inducing-9p13-p12 0.7410.634 cysteine-rich protein with ka~al motifs 1731 apt PDGFR~, Platelet-derived 4q11-q13 0.7390.619 growth fiactor receptor, alpha polypeptide 1761 at PDGFRL Platelet-derived 8p22-p21.3 0.7360.78910 growth f actor receptor-like 1327 s at MAP31~C5Mitogen-activated 6q22.33 0.7290.626 protein kinase kinase kinase 39701 at PEG3 Paternally expressed19q13.4 0.7240.611 36948 at CRI1 CREBBP/EP300 inhibitory15q21.1-q21.20.7030.76627*

protein 1 32668 at SSBP2 Single-stranded 5q14.1 0.5030.8096*
DNA

b inding protein Transcriptionalregulation 1577 at AR Androgen receptor Xq11.2-q12 0.7360.76726 32664 at RNASE4 Ribonuclease, RNase14q11.1 0.8480.749 A

f amily, 4 38439 at NFE2L1 Nuclear factor 17q21.3 0.8290.736 (erythroid-d erived 2)-like Probe Set Gene Cytogenetic Name Symbol Description Location R1 R2 Rank 38047 at RBPMS RNA-binding protein8p12-p11 0.8220.756 gene w ith multiple splicing 40775 at ITM2A Integral membrane Xq13.3-Xq21.20.8180.666 p rotein 2A

34163_g_atRBPMS RNA-binding protein8p12-p11 0.7760.709 gene w ith multiple splicing 40570 at FOX01A Forkhead box ~1A 13q14.1 0.7750.668 (rhabdomyosarcoma) 35681 r ~FHX1 zinc fiinger homeobox2q22 0.7690.644 at B 1 b 40202 at BTEB1 Basic transcription9q13 0.7540.606 . element binding protein 1 41505 r MAF v-maf musculoaponeurotic16q22-q23 0.7380.638 at fibrosarcoma oncogene homolog 34355 at MECP2 Methyl CpG bindingXq28 0.7280.662 p rotein 2 (Rett syndrome) 32259 at EZH1 Enhancer of zeste 17q21.1-q21.30.7280.611 homolog 1 41000 at CHES1 Checkpoint suppressor14q24.3-q310.7270.677 34740 at F~X03A Forkhead box O3A 6q21 0.7230.520 39243 s PSIP2 PC4 and SFRS1 9p22.1 0.7210.702 at interacting protein Signal transduction 755 at ITPR1 Inositol1,4,5-triphosphate3p26-p25 0.8090.8224 receptor, type 38176 at GN(i5 Guanine nucleotide15q15.3 0.8050.729 b inding protein (G protein), beta 5 39397 at NR2F2 Nuclear receptor 15q26 0.7680.700 subfamily 2, group F, member 872_i at IRS1 Insulin receptor 2q36 0.7560.77024 s ubstrate 1 40994_at GPRK5 G protein-coupled 10q24-qter 0.7490.627 receptor kinase 5 37908 at GNG11 Guanine nucleotide7q31-q32 0.7480.557 b inding protein 41049 at IRS1 Insulin receptor 2q36 0.7440.694 , substrate 1 32778 at ITPR1 Inositol1,4,5-triphosphate3p26-p25 0.7430.689 receptor, type 1135 at GPRi<5 G protein-coupled 10q24-qter 0.7320.652 receptor kinase 5 34877 at JAi<1 Janus kinase 1 1 p32.3-p31.30.7200.671 Probe Set Gene Cytogenetic Name Symbol Description Location R1 R2 Rank 41796 at PLCL2 Phospholipase C-like3p24.3 0.6890.77917*

Metabolic enzymes 32764_at PH1PH Phytanoyl-CoA 10pter-p11.20.8030.691 hydroxylase (Refsum disease) 37628 at MAOB Monoamine oxidase Xp11.4-p11.30.8650.8087 B

41859 at UST Uronyl-2-sulfotransferase6q24.3-q25.10.8650.8771 38220 at DPYD ~ihydropyrimidine 1 p22 0.8440.8205 dehydrogenase 37015 at ALDH1A1 Aldehyde dehydrogenase9q21 0.8200.751 1 family, member 1290_g_at GSTM5 Glutathione S-transferase1 p13.3 0.8140.77619 37599 at AO?C1 Aldehyde oxidase 2q33 0.7430.652 32805 at AKR1 C1 Aldo-keto reductase10p15-p14 0.7360.586 f amily 1, member 34169 ~ OCRL Oculocerebrorenal Xq25-q26.1 0.7220.661 at syndrome of Lowe 32618 at BLlIRA Biliverdin reductase7p14-cen 0.4300.78611*
A

Cytoskeleton 32145 at A~~1 Adducin 1 (alpha) 4p16.3 0.8170.78612 40488 at ~MD ~ysfirophin (muscularXp21.2 0.7780.698 d ystrophy, Duchenne and Becker types) 38669 ~t SLIC Ste20-related serine10q25.1 0.7700.712 /
threonine kinase 41738 at CAL~1 Caldesmon 1 7q33 0.7550.707 34772_at CORO2B Coronin, actin 15q22.2-q22.310.7290.748 binding protein, 2B

Extracellular matrix 39673 i ECM3 Extracellular matrix9q22.3 0.8270.698 at p rotein 3 39674 r ECM2 Extracellular matrix9q22.3 0.8110.699 at protein 2 36917 at LAMa2 Laminin, alpha 6q22-q23 0.8100.752 41449 at SGCE Sarcoglycan, epsilon7q21-q22 0.7780.76228 36627 at SPARCL1 SPARC-like 1 (mast9,4q21.3 0.7700.675 hevin) Probe Set Gene Cytogenetic Name Symbol Description Location R1 R2 Rank 32535 at FBN1 Fibrillin 1(Marfan15q21.1 0.7290.582 syndrome) ~thers 35717 at ABCA8 ATP-binding cassette,17q24 0.8470.78316 subfamily A (ABC1 ), member 8 37394 at C7 Complement component5p13 0.7910.656 40767 at TFPI Tissue factor pathway2q31-q32.1 0.7790.640 inhibitor 41137 at PPP1 Protein phosphatase1 q32.1 0.7680.631 R12B 1, regulatory (inhibitor) subunit 12B

38122 at SLC23A1 Solute carrier 20p13 0.7610.707 family 23 (nucleobase transporters), member 1 32526 at JAMS functional adhesion11q25 0.7490.631 molecule 3 38119 at GYPC Glycophorin C (Gerbich2q14-q21 0.7470.669 b lood group) 38634 at RBP1 Retinol binding 3q23 0.7450.577 protein 1, cellular 32109 ~t F?~YD1 F?CYD domain containing19q13.1 0.7440.670 ion transport regulator 40496_at C1S Complement comp~nant12p13 0.7340.617 1, s subcomponent 41138_at MIC2 Antigen identifiedXp22.32; 0.7310.565 by monoclonal antibodiesYp11.3 12E7, F21 and 013 40786 at PPP2R5C Protein phosphatase3p21 0.7250.705 2, regulatory subunit B (B56), gamma isoform 35354 at RPL3 Ribosomal protein 22q13 0.7230.667 36873 at VLDLR Very low density 9p24 0.7220.633 l ipoprotein receptor Unknown 40423 at KIAA0903KIAA0903 protein 2p13.3 0.8440.78514 35742 at LKAP Limkain b1 16p13.2 0.8150.708 39750 at Unknown - 0.7960.718 35645_at Unknown - 0.7950.714 38717 at DKFZP586DKFZP586A0522 protein12q11 0.7930.7899 36867 at Unknown LOC92710 1 q31.1 0.7850.739 Probe Set Gene Cytogenetic Name Symbol Description Location R1 R2 Rank 39852_at TAHCCP1 Transactivated 13q13.1 0.7830.738 by hepatitis C virus core protein 40063 at NDP52 Nuclear domain 17q23.2 0.7770.729 10 protein 41685 at KIAA0752 I<IAA0752 protein 5q35.3 0.7770.8712 37446 at KIAA0443 ?Cq22.1 0.7710.613 36894 at Unknown 22q12.3-13.10.7680.634 41273 at Unknown - 0.7660.758 40861 at MRGX MORF-related gene Xq22 0.7600.689 X

35164 at WFS1 Wolfram syndrome 4p16 0.7580.621 (wolframin) 39400 at KIAA1055 15q24.1 0.7580.634 38113 at SYNE-1 Synaptic nuclei 6q25 0.7540.681 expressed gene 1 34760 at KIAA0022 4CIAA0022 gene 2q24.2 0.7490.699 product 33690 at Unknown - 0.7470.565 41478_at KIAA1043 22q12.1 0.7450.653 32076 at DSCR1 Down syndrome critical6p12.3 0.7410.713 L1 region gene 1-like 40853 at ATP10D ATPase,Class V, 4p12 0.7400.647 type 10D

39714_at SH313GRL SH3 domain bindingXq13.3 0.7380.743 glutamic acid-rich protein like 36577 at IUIIG2 Mitogen inducible 14q22.1 0.7360.549 38643 at Unknown - 0.7350.714 38968_at SH3SP5 SH3-domain binding3p24.3 0.7290.683 protein 5 (STIC-associated) 37743 at FEZ1 Fasciculation and 11 q24.2 0.7290.617 elongation protein zeta 1 (zygin I) 32251 at FLJ21174 Hypothetical proteinXq22.1 0.7280.615 39743 at FLJ20580 1 p33 0.7020.77520*

36396 at Unknown - 0.6880.78515*

35173 at DXS1283E ?fp22.3 0.6170.77422*

38394 at i<IAA0089 3p22.2 0.5000.7908*

40916 at FLJ10097 I Xq22.1-q22.30.3590.77718*
( I I I I

Note: Gene symbols in bold indicated genes detected with 2 separate probe sets. Absolute CC
values are shown for expression levels analyzed in all 36 samples (R1 ) and in the 18 test samples only (R2). In each functional category, probe sets are listed by descending R1 values.
*Indicates genes from the 28 classification set not ranked within the 100 highest R1 values.

Table 9. Full Set of 900 Genes Differentially Affected in Ovarian Cancer Cytogenetic Probe Sets CC Gene Symbol Location 1 41859 at 0.877 UST 6q24.3-q25.1 2 41685 at 0.871 KIAA0752 5q35.3 3 36160 s at 0.846 PTPRN2 7q36 4 755 at 0.822 ITPR1 3p26-p25 38220 at 0.82 DPYD 1 p22 6 32668 at 0.809 SSBP2 5q 14.1 7 37628 at 0.808 MA~B Xp11.4-p11.3 8 38394 at 0.79 KIAA0089 3p22.2 9 38717 at 0.789 DKF~P586A0522 12q11 1761 at 0.789 PDGFRL 8p22-p21.3 11 32618 at 0.786 BLVRA 7p14-cen 12 32145 at 0.786 ADD1 7p16.3 13 34257 at 0.786 AIP1 7q21 14 40423 at 0.785 KIAA0903 2p13.3 36396 at 0.785 cDNA DKFZp586N 4p16.3 16 35717 at 0.783 ABCAB 17q24 17 41796 at 0.779 PLCL2 3p24.3 18 40916 at 0.777 Gene f~r hyp~thetical pr~tein 19 1290_g ~t 0.776 GSTM5 1 p13.3 39743 at 0.775 FLJ20580 1 p33 21 36073 at 0.774 NDN 15q11.2-q12 22 35173 at 0.774 DXS1283E Xp22.3 23 1897 at 0.77 TGFBR3 1 p33-p32 24 872 i pat 0.77 IRS1 2q36 38120 at 0.768 PKD2 4q21-q23 26 1577 afi 0.767 AR Xq 11.2-q 12 27 36948 at 0.766 CRI1 15q21.1-q21.2 28 41449 at 0.762 SGCE 7q21-q22 29 40480 s at 0.761 FYN 6q21 34842 at 0.759 SNRPN 15q12 31 41273 at 0.758 EST

32 1396 at 0.756 IGFBP5 2q33-q36 33 38047 at 0.756 RBPMS 8p12-p11 34 35738 at 0.755 HMGN4 6p21.3 40876 at 0.754 GYG 3q24-q25.1 36 35783 at 0.753 VAMP3 1 p36.23 37 37242 at 0.753 MGC5149 16q12.2 38 36917 at 0.752 LAMA2 6q22-q23 39 37015 at 0.751 ALDH1A1 9q21.13 32664 at 0.749 RNASE4 14q11.1 Cytogenetic Probe Sets CC Gene Symbol Location 41 34772 at 0.748 COR02B 15q22.2-q22.31 42 38650 at 0.748 IGFBP5 2q33-q36 43 39025 at 0.746 TOM7 7p21.3 44 40961 at 0.745 SMARCA2 9p22.3 45 32777 at 0.743 WRB 21 q22.3 46 39714 at 0.743 SH3BGRL Xq13.3 47 35316 at 0.743 RAGA 9p21.2 48 38318 at 0.741 FAM8A1 6p22-p23 49 38802 at 0.739 PGRMC1 Xq22-q24 50 36867 at 0.739 cDNA FLJ34019 fis 51 39852 at 0.738 TAHCCP1 13q13.1 52 35435 s at 0.736 HADHSC 4q22-q26 53 38439 at 0.736 NFE2L1 17q21.3 54 1909 at 0.736 BCL2 18q21.3 55 33942 s at 0.735 STXBP1 9q34.1 56 1640 at 0.734 ST13 22q13.2 57 227_g at 0.733 PRI<AR1 A 17q23-q24 58 2010 at 0.732 SKP1A 5q31 59 40063 at 0.729 NDP52 17q23.2 38176 at 0.729 GNB5 15q15.3 61 39350 at 0.727 GPC3 ~Cq26.1 62 39037 at 0.726 MLLT2 4q21 63 851 s at 0.725 IRS1 2q36 64 39556 at 0.725 SPTBN1 2p21 2039 s at 0.723 FYN 6q21 66 41744 at 0.723 OPTN 10p12.33 67 32695 at 0.722 HTATSF1 3~q26.1-q27.2 68 36915 at 0.72 CTSO 4q31-q32 69 38982 at 0.719 TERF21P 16q22.3 1348 s at 0.718 PCCA 13q32 71 39750 at 0.718 EST

72 37643 at 0.716 TNFRSF6 10q24.1 73 39376 at 0.715 Nbak2 1p11.2 74 38695 at 0.715 NDUFS4 5q11.1 35645 at 0.714 cDNA DKFZp586G1520 76 38643 at 0.714 LOC92689 4p15.1 77 38375 at 0.713 ESD 13q14.1-q14.2 78 32076 at 0.713 DSCR1 L1 6p12.3 79 38669 at 0.712 SLIC 10q25.1 37373 at 0.71 UGP2 2p14-p13 81 37532 at 0.71 ACADM 1 p31 82 39165 at 0.71 NIFU 12q24.1 83 34163_g at 0.709 RBPMS 8p12-p11 Cytogenetic Probe Sets CC Gene Symbol Location 84 35742 at 0.708 LI<AP 16p 13.13 85 40607 at 0.708 DPYSL2 8p22-p21 86 41738 at 0.707 CALD1 7q33 87 38122 at 0.707 SLC23A1 2Op13 88 32747 at 0.706 ALDH2 12q24.2 89 40786 at 0.705 PPP2R5C 3p21 90 33198 at 0.705 BART1 16q13 91 38745 at 0.702 LIPA 10q23.2-q23.3 92 39243 s at 0.702 PSIP2 9p22.1 93 33936 at 0.702 GALC 14q31 94 39397 at 0.7 NR2F2 15q26 95 41147 at 0.699 MGC4276 9q22.1 .

96 34760 at 0.699 KIAA0022 2q24.2 97 39674 r at 0.699 ECM2 9q22.3 98 39401 at 0.699 IMAGE clone 3460701 99 40488 at 0.698 DMD Xp21.2 100 39673 i at 0.698 ECM2 9q22.3 101 39864 at 0.698 CIRBP 19p13.3 102 1127 at 0.697 RPS611~A1 3 103 40674 s at 0.697 HOXC6 12q13.3 104 36975 at 0.696 MGC8721 8p12 105 41049 at 0.694 IRS1 2q36 106 32593 at 0.692 I~IAA0084 3p24.3 107 1629 s afi 0.692 PTPN13 4q21.3 108 36620 afi 0.692 SOD1 21q22.11 109 32764 at 0.691 PGCP 8q22.2 110 35785 afi 0.691 GABARAPL1 12p13.1 111 39681 at 0.69 ZNF145 11q23.1 112 39438 at 0.69 CREBL2 12p13 113 32778 at 0.689 ITPR1 3p26-p25 114 40861 at 0.689 MRGX Xq22 115 34990 at 0.688 SETBP1 18q21,1 116 1736 at 0.688 IGFBP6 12q13 117 41771_g at 0.687 MAOA Xp11.4-p11.3 118 31852 at 0.687 DfCFZP564O043 7p21 119 36542 at 0.686 SLC9A6 Xq26.3 120 37379 at 0.684 SF3A3 1 p35.2 121 38968 at 0.683 SH3BP5 3p24.3 122 39691 at 0.683 SH3GLB1 1p22 123 38211 at 0.681 ZNF288 3q13.2 124 38113 at 0.681 SYNE-1 6q25 125 40601 at 0.68 BBP 1 p32.1 126 2092 s at 0.679 SPP1 4q21-q25 Cytogenetic Probe Sets CC Gene Symbol Location 127 33830 at 0.679 HSOBRGRP 1 128 34637 f at 0.678 ADH1A 4q21-q23 129 38634 at 0.677 RBP1 3q23 130 35643 at 0.677 NUCB2 11p15.1-p14 131 41000 at 0.677 CHES1 14q24.3-q31 132 37828 at 0.677 FLJ 11220 1 p 11.2 133 35163 at 0.676 ICIAA1041 1 pier-q31.3 134 36627 at 0.675 SPARCL1 4q21.3 135 853 at 0.674 NFE2L2 2q31 136 34356 at 0.673 SURB7 12p12.3 137 38013 at 0.672 ATIP1 8p22 138 38664 at 0.672 CFDP1 16q22.2-q22.3 139 32087 at 0.672 HSF2 6q22.33 140 38768 at 0.671 HADHSC 4q22-q26 141 34877 at 0.671 JAIC1 1 p32.3-p31.3 142 1090 f at 0.671 ?

143 32109 at 0.67 FXhD1 19q13.1 144 34859 at 0.669 MAGED2 Xp11.4-p11.1 145 38119 at 0.669 GIfPC 2q14-q21 146 31510 s at 0.668 H3F3B 17q25 147 1058 at 0.668 1NASF3 13q12 148 40570 at 0.668 FOXO1A 13q14.1 149 39091 at 0.668 Jl/1!A 3p14 150 32057 at 0.667 P37NB 7q11.22 151 31993 f at 0.667 EST

152 35354 at 0.667 SYNGR1 22q13.1 153 40775 at 0.666 ITM2A Xq13.3-Xq21.2 154 40140 at 0.666 ZFP103 2p11.2 155 37406 at 0.665 MAPRE2 18q12.1 156 38685 at 0.665 ST?C12 1 p35-34.1 157 34363 at 0.665 SEPP1 5q31 158 33351 at 0.664 GC20 3p21.33 159 41655 at 0.664 MID2 Xq22 160 39072 at 0.662 MXI1 10q24-q25 161 36544 at 0.662 clone IMAGE:3610040 162 32542 at 0.662 FHL1 ?Cq26 163 35767 at 0.662 GABARAPL2 16q22.3-q24.1 164 34355 at 0.662 MECP2 Xq28 165 1578_g at 0.661 AR ?Cq11.2-q12 166 41656 at 0.661 NMT2 10p12.33-p12.32 167 34169 s at 0.661 OCRL Xq25-q26.1 168 41662 at 0.661 DKFZP566B183 12p13.32 169 40203 at 0.66 SU11 17 5~
Cytogenetic Probe Sets CC Gene Symbol Location 170 39315 at 0.659 ANGPT1 8q22.3-q23 171 226 at 0.657 PRKAR1A 17q23-q24 172 33878 at 0.657 FLJ13612 2q36.1 173 38408 at 0.656 TM4SF2 Xq11.4 174 35704 at 0.656 HRASLS3 11 q13.1 175 37394 at 0.656 C7 5p13 176 39829 at 0.656 ARL7 2q37.2 177 40770 f at 0.655 HNRPDL 4q13-q21 178 35846 at 0.655 THRA 17q11.2 179 176 at 0.654 PPP2R5C 3p21 180 36690 at 0.654 NR3C'I 5q31 181 39351 at 0.653 CD59 11 p13 182 41478 at 0.653 4CIAA1043 22q12.1 183 950 at 0.653 TLOC1 3q26.2-q27 184 35359 at 0.653 PUM2 2p22-p21 185 1135 at 0.652 GPRi<5 10q24-qter 186 36091 at 0.652 SCAP2 7p21-p15 187 2003 s at 0.652 MSH6 2p16 188 35246 at 0.652 TYROS 15q15.1-q21.1 189 40576 f at 0.652 HNRPDL 4q13-q21 190 37599 at 0.652 AOX1 2q33 191 35209 at 0.652 iCIAA0766 3p22.1 192 38916 at 0.651 CX~rf6 Xq28 193 33126 at 0.651 AD-017 3p~1.31 194 37706 at 0.65 GLG1 16q22-q23 195 40077 at 0.65 ACO1 9p22-p13 196 37294 at 0.649 BTG1 12q22 197 32597 at 0.648 RBL2 16q12.2 198 32768 at 0.648 FLJ21007 13q21.1 199 36543 at 0.648 F3 1 p22-p21 200 37736 at 0.647 PCMT1 6q24-q25 201 40853 at 0.647 ATP10D 4p12 202 41830 at 0.647 i<IAA0494 1 pter-p22.1 203 37197 s_at 0.647 DKF~P564A033 2 204 37205 at 0.645 FBXL7 ~ 5p15.1 205 40617 at 0.645 SAH 16p13.11 206 35681 r at 0.644 ZFHX1 B 2q22 207 41594 at 0.644 JAi<1 1 p32.3-p31.3 208 192 at 0.643 TAF7 5q31 209 654 at 0.643 MXI1 10q24-q25 210 41742 s at 0.642 OPTN 10p12.33 211 32676 at 0.642 ALDH6A1 14q24.3 212 35752 s at 0.642 PROS1 3p11-q11.2 Cytogenetic Probe Sets CC Gene Symbol Location 213 34774 at 0.642 PPT1 1 p32 214 38892 at 0.642 KIAA0240 6p21.1 215 659_g at 0.641 THBS2 6q27 216 41288 at 0.641 CALM1 14q24-q31 217 35955 at 0.64 218 924 s at 0.64 PPP2CB 8p12-p11.2 219 39360 at 0.64 SNX3 6q22.1 220 40767 at 0.64 ~~ TFPI 2q31-q32.1 221 237 s at 0.639 PPP2CA 5q23-q31 222 35782 at 0.638 ICIAA0657 2q36.3 223 1678_g at 0.638 IGFBP5 2q33-q36 224 41505 r at 0.638 MAF 16q22-q23 225 39715 at 0.638 cDNA FLJ31079 fis 226 34198 at 0.638 PTPN13 4q21.3 227 34821 at 0.638 DKFZP586D0623 6q23.1-q24.1 228 33868 at 0.637 dJ222E13.2 22q13.2 229 2086 s at 0.637 TYROS 15q15.1-q21.1 230 1147 at 0.637 NR2F1 5q14 231 40145 at 0.636 TOP2A 17q21-q22 232 40211 at 0.636 HNRPA1 ~ 12q13.1 233 37617 at 0.636 KIAA1128 10q23.31 234 36489 at 0.636 PRPS1 Xq21-q27 235 36488 at 0.636 EC3FL5 9q32-q33.3 236 33443 at 0.635 TDE1 L 6q22.32 237 39369 at 0.635 I~IAA0935 4p16.1 238 36894 at 0.634 CB~7 22q13.1 239 35234 at 0.634 RECD 9p13-p12 240 39400 at 0.634 ICIAA1055 15q24.1 241 38074 at 0.634 AP3S1 5q22 242 34803 at 0.633 USP12 5q33-q34 243 39441 at 0.633 LANCL1 2q33-q35 244 36873 at 0.633 VLDLR 9p24 245 38985 at 0.633 LEPROTL1 8p21.2-p21.1 246 33911 at 0.633 cDNA DKFZp564P116 247 41638 at 0.633 KIAA0073 5q12.3 248 41277 at 0.632 SAP18 13q11 249 38342 at 0.632 I~IAA0239 5q31.1 250 35754 at 0.632 TMP21 14q24.3 251 32526 at 0.631 JAMS 11 q25 252 41137 at 0.631 PPP1 R12B 1 q32.1 253 33857 at 0.63 p47 20p13 254 871 s at 0.63 HLF 17q22 255 40399 r at 0.63 MEO>f2 7p22.1-p21.3 Cytogenetic Probe Sets CC Gene Symbol Location 256 39428 at 0.63 LNK 12q24 257 31872 at 0.63 SS18 18q11.2 258 41634 at 0.629 KIAA0256 15q15.1 259 39731 at 0.629 RBMX Xq26 260 33136 at 0.628 ?

261 38353 at 0.628 TUBGCP3 13q34 262 32708_g at 0.627 KATNA1 6q25.1 263 40994 at 0.627 GPRI~C5 10q24-qter 264 33222 at 0.626 F~D7 2q33 265 1327 s at 0.626 MAP3K5 6q22.33 266 40039_g at 0.625 ST7 7q31.1-q31.3 267 35228 at 0.624 CPT1B 22q13.33 268 38693 at 0.623 ATPSL 3q27 269 39431 at 0.622 NPEPPS 17q21 270 35784 at 0.622 VAMP3 1 p36.23 271 538 at 0.622 CD34 1 q32 272 36119 at 0.622 CAV1 7q31.1 273 218 at 0.621 II~C 5q31.3 274 35164 at 0.621 !~llFS1 4p16 275 39856 at 0.62 RPL36AL 14q21 276 41529_g at 0.62 cDNA DKFZp434M162 277 37715 at 0.619 SNW 1 14~q24.3 278 33302 at 0.619 SSPN 12p11.2 279 1731 at 0.619 PDGFRA 4q11-q13 280 35741 at 0.619 PIP51~C2B 17q12 281 35356 at 0.619 MGC9651 4p16.1 282 39582 at 0.618 cDNA DKF~p586D1122 283 911 s at 0.618 CALM2 2p21 284 41620 at 0.618 KIAA0716 7q21.13 285 33249 at 0.618 NR3C2 4q31.1 286 40841 at 0.618 TACC1 8p11 287 37595 at 0.618 cDNA DKFZp547E184 288 37958 at 0.617 BCMP1 Xp11.4 289 37748 at 0.617 i<IAA0232 4p16.1 290 40496 at 0.617 C1 S 12p13 291 37743 at 0.617 FE~1 11 q24.2 292 35335 at 0.616 R~CI<2 2p24 293 33862 at 0.615 PPAP2B 1 pter-p22.1 294 32251 at 0.615 FLJ21174 Xq22.1 295 37486 f at 0.615 MEIS3 17p11.2 296 38101 at 0.615 BDG-29 16q24.2 297 40213 at 0.615 SMARCA1 Xq25 298 32851 at 0.614 CUGBP2 10p13 Cytogenetic Probe Sets CC Gene Symbol Location 299 1211 s at 0.614 CRADD 12q21.33-q23.1 300 34819 at 0.614 CD164 6q21 301 34808 at 0.614 KIAA0999 11 q23.3 302 37446 at 0.613 I<IAA0443 Xq22.1 303 32792 at 0.612 P29 1 p36.13-p35.1 304 36650 at 0.612 CCND2 12p13 305 38438 at 0.612 NFKB1 4q24 306 39701 at 0.611 PEG3 19q13.4 307 34215 at 0.611 D?CYS155E Xp22.32 308 32259 at 0.611 E~H1 17q21.1-q21.3 309 40839 at 0.611 UBL3 13q12-q13 310 39055 at 0.61 SRI 7q21.1 311 40508 at 0.61 GSTA4 6p12.1 312 37985 at 0.608 LMNB1 5q23.3-q31.1 313 33799 at 0.607 SIAH2 3q25 314 37638 at 0.607 DOCIC1 10q26.13-q26.3 315 33140 at 0.607 B3GNT6 11 q12.1 316 40202 at 0.606 BTEB1 9q13 317 39033 at 0.806 C1 orf8 1 p36-p31 318 34789 at 0.606 SERPINB6 6p25 319 33817 at 0.606 HNRPA3 10q11.1 320 1719 at 0.606 MSH3 5q11-q12 321 38923 at 0.605 FRG1 4q35 322 41338 at 0.605 AES 19p13.3 323 35751 at 0.605 SDHB 1p36.1-p35 324 1377 at 0.605 i~FI~B1 4~q24 325 33123 at 0.605 HRIHFB2206 16q22.1 326 933 fi at 0.604 ZNF91 19p13.1-p12 327 32696 at 0.604 PBX3 9q33-q34 328 1323 at 0.604 UBB 17p12-p11.2 329 34349 at 0.603 SEC63L 6q21 330 37731 at 0.602 EPS15 1 p32 331 37315 f at 0.602 C14orf11 14q12 332 36695 at 0.602 cDNA FLJ40364 fis 333 31867 at 0.602 ? 3q13.12 334 31944 at 0.601 TULP3 12p13.3 335 1070 at 0.601 GTF2B 1 p22-p21 336 38254 at 0.601 KIAA0882 4q31.1 337 37710 at 0.6 MEF2C 5q14 338 33343 at 0.6 RNF14 5q23.3-q31.1 339 32779 s at 0.599 ITPR1 3p26-p25 340 33865 at 0.599 BS69 10p14 341 31508 at 0.599 TXN IP 1 q 12 Cytogenetic Probe Sets CC Gene Symbol Location 342 40419 at 0.599 STOM 9q34.1 343 33915 at 0.599 FLJ23027 14q32.32 344 38364 at 0.599 BCE-1 9q21.32 345 38050 at 0.599 BTF 6q22-q23 346 38046 at 0.599 IK 5q31.3 347 38820 at 0.598 15-Sep 1 p31 348 32713 at 0.598 GOLGA1 9q34.11 349 32107 at 0.598 C21 orf25 21 q22.3 350 38727 at 0.597 SDNSF 2p21 351 38839 at 0.597 PFN2 3q25.1-q25.2 352 38033 at 0.597 DKFZP564M1416 8q11.22 353 729 i at 0.597 MUC3A 7q22 354 1507 s at 0.596 EDNRA 4q31.21 355 33103 s at 0.596 ADDS 10q24.2-q24.3 356 39436 at 0.596 BNIP3L 8p21 357 39097 at 0.595 SON 21q22.11 358 39294 at 0.595 NR2F1 5q14 359 41333 at 0.595 CENTB2 3q29 360 38116 at 0.595 KIAA0101 15q22.1 361 32780 at 0.594 BPAC1 6p12-p11 362 41385 at 0.594 EP841 L3 18p11.32 363 38400 at 0.594 DKFZP434D1335 19q13.12 364 32841 at 0.594 ~NF9 3q21 365 41420 at 0.593 IGFBP5 2q33-q36 366 34860_g at 0.593 MAGED2 Xp11.4-p11.1 367 38415 at 0.593 PTP4.A2 1 p35 368 38317 at 0.593 TCEAL1 ~tq22.1 369 39939 at 0.593 COL4A6 Xq22 370 41405 at 0.592 SFRP4 7p14.1 371 36980 at 0.592 PROL2 6q16.1 372 39147 g at 0.592 ATRX Xq13.1-q21.1 373 32700 at 0.592 GBP2 1 p22.1 374 39986 at 0.591 DKFZP586D0919 12q13.2 375 38690 at 0.591 C3ort4 3p11-q11 376 1848 at 0.591 RAP 1 A 1 p 13.3 377 36636 at 0.59 OAT 10q26 378 37230 at 0.59 KIAA0469 1 p36.23 379 37107 at 0.59 PPM1 D 17q23.1 380 33870 at 0.589 C5orf7 5q31 381 33229 at 0.589 RPS6KA3 Xp22.2-p22.1 382 36159 s_at 0.589 PRNP 20pter-p12 383 32337 at 0.589 RPL21 10q26.13 384 508 at 0.589 SUPT4H1 17q21-q23 Cytogenetic Probe Sets CC Gene Symbol Location 385 41424 at 0.588 PONS 7q21.3 386 . 32667 at 0.588 COL4A5 ~Cq22 387 39979 at 0.588 F10 13q34 388 34753 at 0.587 SYBL1 )Cq28 389 39082 at 0.587 ANXA6 5q32-q34 390 39989 at 0.587 RAGS Xp11.21 391 33235 at 0.587 NAV3 392 36825 at 0.587 TRIM22 11 p15 393 41747 s at 0.587 MEF2A 15q26 394 38279 at 0.587 GNAZ 22q11.22 395 32511 at 0.586 cDNA FLJ37094 fis 396 32805 at 0.586 AICR1C 10p15-p14 397 1529 at 0.586 13CDNA73 13q12.3 398 34570 at 0.586 RPS27A 2p16 399 31932 f at 0.586 BTF3 5q 13.1 400 35055 at 0.586 BTF3 5q 13.3 401 32822 at 0.585 SLC25A4 4q35 402 37131 _at 0.585 I~LKB 19q 13.3-q 13.4 403 35318 at 0.585 I~IAA0475 1 p36.13-q41 404 36526 at 0.584 EXTL2 1 p21 405 38837 at 0.584 DJ971N18.2 20p12 406 36492 at 0.583 PSMD9 12q24.31-q24.32 407 36515 at 0.583 GNE 9p11.2 408 35737 at 0.583 HMGN4~ 6p21.3 409 32535 at 0.582 FBN1 15q21.1 410 39838 at 0.582 CLASP1 2q21.3 411 1307 at 0.582 ?SPA 9q22.3 412 40971 at 0.581 KIAA0229 6p21.2 413 1319 at 0.58 DDR2 1q12-q23 414 33892 at 0.579 PKP2 12p11 415 33800 at 0.579 ADCY9 16p13.3 416 39790 at 0.579 ATP2A2 12q23-q24.1 417 37725 at 0.578 PPP1CC 12q24.1-q24.2 418 38711 at 0.578 CLASP2 3p22.2-p22.1 419 32662 at 0.578 KIAA0170 6pter-p21.31 420 32582 at 0.578 MYH11 16p13.13-p13.12 421 41013 at 0.578 cDNA DKFZp586M2022 422 32743 at 0.577 KIAA0453 1p36.31-p36.11 423 35325 at 0.577 RAB14 9q32-q34.11 424 36626 at 0.577 HSD17B4 5q21 425 39038 at 0.576 FBLN5 14q32.1 426 32160 at 0.576 SIAH1 16q12 427 1501 at 0.576 IGF1 12q22-q23 Cytogenetic Probe Sets CC Gene Symbol Location 428 32244 at 0.576 KIAA0737 14q11.1 429 450_g_afi 0.576 CGR19 14q22.1 430 39083 at 0.575 UBE2D3 4q22.2 431 36596 r at 0.575 GATM 15q14 432 35276 at 0.575 CLDN4 7q 11.23 433 36578 at 0.575 BIRC2 11q22 434 32153 s at 0.575 ?

435 33847 s at 0.574 CDKN1B 12p13.1-p12 436 40432 at 0.573 GNS 12q14 437 39346 at 0.573 iCHDRBS1 1 p32 438 38581 at 0.573 GNACa 9q21 439 37604 at 0.573 HNMT 2q21.3 440 41691 at 0.572 KIAA0794 3q29 441 201 s at 0.571 B2M 15q21-q22.2 442 41600 at 0.571 PA2G4 12q13 443 39762 at 0.571 ZNF262 1 p32-p34 444 32099 at 0.57 KIAA0138 19p13.3 445 41701 at 0.57 C6 5p13 446 39150 at 0.569 RNF11 1pter-p22.1 447 36474 at 0.569 4CIAA0776 6q16.3 448 39685 at 0.568 E46L 22q13.31 449 37391 at 0.567 CTSL 9q21-q22 450 35843 at 0.567 NEI~9 14q24.2 451 41136 s afi 0.567 APP 21 q21.3 452 35203 at 0.566 M~RF 10q22.2 453 34162 at 0.566 RBPMS 8p12-p11 454 35811 afi 0.566 RNF13 3q25.1 455 39110 at 0.566 EIF4B 12q13.13 456 35331 at 0.565 CTNNAL1 9q31.2 457 39663 at 0.565 MAN2A1 5q21-q22 458 41138 at 0.565 MIC2 Xp22.32 459 31936 s at 0.565 LiCAP 16p13.2 460 38470 i at 0.565 APPBP2 17q21-q23 461 33690 at 0.565 cDNA DKFZp434A202 462 39846 at 0.564 CTSF 11 q13 463 39109 at 0.564 C20ort1 20q11.2 464 34372 at 0.564 UREB1 Xp11.2 465 32521 at 0.563 SFRP1 8p12-p11.1 466 35936_g at 0.563 CPT1 B 22q13.33 467 40698 at 0.563 CLECSF2 12p13-p12 468 324 f at 0.562 BTF3 5q 13.3 469 333 s at 0.562 RBMS1 2q24.2 470 41606 at 0.562 DRG1 22q12.2 Cytogenetic Probe Sets CC Gene Symbol Location 471 40281 at 0.562 NEDD5 2q37 472 32240 at 0.562 PSMDS 9q33.3 473 33441 at 0.561 TCTA 3p21 474 39170 at 0.561 cDNA DiCFZp564J0323 475 35366 at 0.56 NID 1 q43 476 41271 at 0.56 SLC7A8 14q11.2 477 1530_g at 0.56 13CDNA73 13q12.3 478 33278 at 0.56 SAH 16p13.11 479 38754 at 0.56 P8 16p11.2 480 37891 at 0.559 cDNA D4CFZp586F1822 481 36727 at 0.558 ?

482 32506 afi 0.558 TBC1D1 4p14 483 37908 at 0.557 GNG11 7q31-q32 484 39117 at 0.557 PHF2 9q22.31 485 34320 at 0.557 PTRF 17q21.2 486 36791_g at 0.556 TPM1 15q22.1 487 539 at 0.556 RYK 3q22 488 40825 at 0.556 MAPRE3 2p23.3-p23.1 489 32169 at 0.556 FBX021 12q24.21 490 38782 afi 0.555 GTF2H 1 11 p 15.1-p 491 1677 at 0.555 IGFBP5 2q33-q36 492 33899 at 0.555 ALDH9A1 1 q23.1 493 40843 at 0.555 ICAP-1A 2p25.2 494 32172 at 0.554 SHARP 1p36.33-p36.11 495 35303 at 0.554 INSIG1 7q36 496 34235 afi 0.554 GPR116 6p12.3 497 818 s at 0.554 ATR?~ ~Cq13.1-q21.1 498 33113 at 0.554 CITED2 6q23.3 499 34287 at 0.553 C21 orf80 21 q22.3 500 33418 at 0.553 cDNA DKFZp434A012 501 36790 at 0.552 TPM1 15q22.1 502 40811 at 0.552 COASTER 6p11.1 503 41739 s at 0.552 CALD1 7q33 504 509 at 0.551 MADH4 18q21.1 505 37598 at 0.551 RASSF2 20pter-p12.1 506 36629 at 0.55 DSIPI ?Cq22.3 507 41462 at 0.55 SNX2 5q23 508 36032 at 0.55 509 39045 at 0.549 FLJ21432 12p13.31 510 36577 at 0.549 MIG2 14q22.1 511 39557 at 0.549 cDNA FLJ31246 fis 512 33819 at 0.549 LDHB 12p12.2-p12.1 513 38610 s at 0.549 KRT10 17q21-q23 Cytogenetic Probe Sets CC Gene Symbol Location 514 890 at 0.548 UBE2A ?Cq24-q25 515 32730 at 0.548 KIAA1750 8q22.1 516 1252 at 0.548 DP1 5q22-q23 517 32239 at 0.548 MATN2 :3q22 518 33405 at 0.548 CAP2 6p22.2 519 37266 at 0.548 ~NF32 10q22-q25 520 39686_g at 0.548 E46L 22q13.31 521 40155 at 0.547 ABLIM1 1~q25 522 35988 i at 0.547 MYST1 16p11.1 523 34314 at 0.547 RRM1 11 p15.5 524 35213 at 0.546 WBP4 13q13.3 525 37676 at 0.546 PDEBA 15q25.1 526 39545 at 0.546 CD1CN1 C 11 p15.5 527 37708 r at 0.545 ADH5 4q21-q25 528 41686 s at 0.545 KIAA0752 5q35.3 529 202 at 0.545 HSF2 6q22.33 530 33399 at 0.544 531 39380 at 0.544 GTAR 4q 13.3 532 35166 at 0.544 DSCR3 21 q22.2 533 39693 at 0.544 MCC5508 11q13.1 534 149 at 0.544 DD)C39 19p13.13 535 40522 at 0.544 GLUL 1 q31 536 40831 at 0.544 DIfF~P586B0923 10q22.2 537 32253 at 0.544 RERE 1p36.1-p36.2 538 1836 at 0.543 CCNI 4q13.3 539 36991 at 0.543 SFRS4 1 p35.2 540 171 at 0.54.3 VBP1 ?tq28 541 38508 s at 0.542 CREBL1 6p21.3 542 33856 at 0.542 C?C~C1 Xq26 543 36118 at 0.542 NCOA1 2p23 544 32038 s at 0.542 SRP46 11 q22 545 36964 at 0.542 MBTPS1 16q24 546 37005 at 0.541 NBL1 1p36.3-p36.2 547 34259 at 0.541 iCIAA0664 17p13.3 548 1725 s at 0.541 Oncogene E6-Ap, Papillomavirus 549 34344 at 0.541 1KBI<AP 9q34 550 33303 at 0.54 SSPN 12p11.2 551 32215 i_at 0.54 RHOBTB3 5q21.2 552 34675 at 0.54 cDNA FLJ13555 fis 553 718 at 0.54 PRSS11 10q26.3 554 35168 f at 0.54 COL16A1 1 p35-p34 555 33875 at 0.539 ATP6VOE 5q35.2 Cytogenetic Probe Sets CC Gene Symbol Location 556 32548 at 0.539 TEBP 12 557 38980 at 0.539 MAP3K71P2 6q25.1-q25.3 558 40998 at 0.539 TNRC11 ?Cql3 559 31907 at 0.539 RPL14 3p22-p21.2 560 41770 at 0.539 MAOA Xp11.4-p11.3 561 31605 at 0.539 LOC171220 12p13 562 34684 at 0.538 RECQL 12p12 563 41872 at 0.538 DFNA5 7p15 564 34853 at 0.538 FLRT2 14q24-q32 565 40467 at 0.537 SDHD 11 q23 566 39405 at 0.537 KIAA0266 13q12.2-q13.3 567 36925 at 0.537 HSPA2 14q24.1 568 32564 at 0.537 SEC61 B 9q22.32-q31.3 569 33431 at 0.536 FMOD 1 q32 570 37248 at 0.536 CPZ 4p16.1 571 39931 at 0.535 DYRK3 1 q32 572 35753 at 0.535 PRPFB 17p13.3 573 41713 at 0.535 ~NF36 7q21.3-q22.1 574 32171 at 0.535 EIF5 14q32.33 575 1675 at 0.535 RASA1 5q 13.3 576 35644 at 0.535 HEPH Xq11-q12 577 32569 at 0.534 PAFAH1 B1 17p13.3 578 34370 at 0.533 ARCN1 11q23.3 579 38011 at 0.533 C19~rf2 19q12 580 41194 at 0.533 SRP14 15q22 581 39509 at 0.533 PDCD4 1 ~q24 582 32143 at 0.532 OSR2 8q22.1 583 40634 at 0.532 NAP1L1 12q14.1 584 34255 at 0.531 DGAT1 8qter 585 1101 at 0.531 APBB1 11 p15 586 35999 r at 0.531 KIAA0781 11 q23.2 587 40083 at 0.531 KIAA0625 9q34.3 588 663 at 0.531 EIF1A X

589 39884_g at 0.531 HSA9761 5q11-q14 590 1467 at 0.531 EPS8 12q23-q24 591 31866 at 0.529 PD2 19q13.1 592 1512 at 0.529 DYRK1A 21q22.13 593 39897 at 0.529 KIAA1966 4q13.1 594 38385 at 0.529 DSTN 20p11.23 595 32170_g_at 0.529 FBX021 12q24.21 596 1850 at 0.529 MLH1 3p21.3 597 39366 at 0.529 PPP1 R3C 10q23-q24 598 2062 at 0.528 IGFBP7 4q12 Cytogenetic Probe Sets CC Gene Symbol Location 599 32765 f at 0.528 PGGP 8q22.2 600 38035 at 0.528 MTMR6 13q12 601 37352 at 0.528 SP100 2q36.1 602 36169 at 0.528 NDUFA1 ?Cq24 603 37707 i at 0.528 ADH5 4q21-q25 604 41743 i at 0.528 OPTN 10p12.33 605 34890 at 0.527 ATP6V1 A1 3q 13.31 606 38351 at 0.527 cDNA DKF~p586L0120 607 38990 at 0.526 ICIC 6p12.3-p11.2 608 37389 at 0.526 SMAP 11 p15.1 609 34445 at 0.526 ICIAA0471 1 q24-q25 610 40859 at 0.526 FLJ11806 14.q31.3 611 37029 at 0.526 ATP5~ 21 q22.11 612 41490 at 0.525 PRPS2 Xp22.3-p22.2 613 39687 at 0.525 E46L 22q13.31 614 35247 at 0.525 SNAPG5 615 34417 at 0.525 cDNA DKFZp586E1120 616 40105 at 0.524 MUT 6p21 617 41379 at 0.524 SMCS 9q21.12 618 38059_g ~t 0.524 DPT 1 q 12-q23 619 39517 at 0.524 HTGN29 5q31.1 620 38743 f at 0.524 RAF1 ~ 3p25 621 41746 at 0.523 NHP2L1 22q13.2-q13.31 622 507 s at 0.523 ELF2 4~q28 623 36423 at 0.523 P8 16p11.2 624 40988 at 0.522 lfMElL1 10p14 625 34680 s_at 0.521 KIAA0107 3p14.3 626 40962 s at 0.521 SMARCA2 9p22.3 627 36792 at 0.521 TPM1 15q22.1 628 40191 s at 0.52 KIAA0582 2p14 629 37367 at 0.52 ATP6V1 E1 22q11.1 630 35221 at 0.52 PURA 5q31 631 38649 at 0.52 ICIAA0970 13q14.11 632 34740 at 0.52 FQX03A 6q21 633 41300 s at 0.519 ITM2B 13q14.3 634 40239_g at 0.519 MGC35048 16p13.13 635 36829 at 0.518 PER1 17p13.1-17p12 636 869 at 0.518 GTF2A2 15q21.2 637 32611 at 0.518 PBP 12q24.22 638 37672 at 0.518 USP7 16p13.3 639 36533 at 0.517 PTGIS 20q13.11-q13.13 640 40438 at 0.517 PPP1 R12A 12q15-q21 641 39118 at 0.517 DNAJA1 9p13-p12 Cytogenetic Probe Sets CC Gene Symbol _Location 642 39555 at 0.517 ING1 L 4q35.1 643 38518 at 0.517 SCML2 Xp22 644 37027 at 0.516 AHNAI< 11 q 12-q 13 645 40260 g at 0.516 RBM9 22q13.1 646 147 at 0.516 TSG101 11p15 647 37616 at 0.516 AUH 9q22.33 648 39809 at 0.516 HBP1 7q31.1 649 32119 at 0.515 cDNA D4CF~p586B211 650 35730 at 0.515 ADH1B 4q21-q23 651 36095 at 0.513 CLIPR-59 19q13.13 652 38654 at 0.513 HNRPU 1 q43 653 41768 at 0.513 PRI<AR1A 17q23-q24 654 38627 at 0.512 HLF 17q22 655 487_g_at 0.512 CASP9 1 p36.3-p36.1 656 33244 at 0.512 CHN2 7p15.3 657 38724 at 0.512 KIAA0515 9q34.2 658 39740_g at 0.512 NACA 12q23-q24.1 659 33850 at 0.512 MAP4 3p21 660 35304 at 0.511 RAB6A 11q13.3 661 36330 at 0.511 CCBL1 9q34.13 662 33240 at 0.511 SEMACAP3 3p13 663 32745 at 0.511 MRPL40 22q11.21 664 274 at 0.51 ZNF148 3q21 665 34792 at 0.51 AHCh'L1 1 p12 666 34781 at 0.509 DCTN6 8p12-p11 667 38626 at 0.509 ICIAA0399 17p13.3 668 38812 at 0.509 LAMB2 3p21 669 35736 at 0.508 GRINL1A 15q22.1 670 37359 at 0.507 4CIAA0102 11q13.3 671 1873 at 0.507 XPC 3p25 672 35258 f at 0.507 SFRS21P 12p11.21 673 40854 at 0.506 UQCRC2 16p12 674 40064 at 0.506 ALS2CR3 2q33 675 37407 s at 0.506 MYH11 16p13.13-p13.12 676 33444 at 0.505 M17S2 17q21.1 677 38826 at 0.505 6-Sep Xq24 678 40832 s at 0.505 LAP1 B 1 q24.2 679 1195 s at 0.505 ICAP-1A 2p25.2 680 35142 at 0.504 DKFZP564D172 5q14.3 681 38479 at 0.504 ANP32B 9q22.32 682 37025 at 0.504 PIG7 16p13.3-p12 Cytogenetic Probe Sets CG Gene Symbol Location 683 969 s at 0.503 USP9X Xp11.4 684 39739 at 0.502 NACA 12q23-q24.1 685 41195 at 0.502 LPP 3q27-q28 686 32576 at 0.502 EIF3S5 11 687 41242 at 0.502 UAP1 1q23.1 688 35301 at 0.502 cDNA DKFZp564E2222 689 33380 at 0.501 P~LS 5p15 690 37221 at 0.501 PRiCAR2B 7q22-q31.1 691 33916 at 0.501 NISCH 3p21.1 692 37895 at 0.5 SLC35A1 6q16.1 693 1120 at 0.5 GSTM3 1 p13.3 694 31463 s at 0.5 695 36899 at 0.499 SATB1 3p23 696 897 at 0.497 PiCD1 16p13.3 697 41174 at 0.497 RANBP2L1 2q12.3 698 38106 at 0.497 YR-29 5q13.3-q14.1 699 38673 s at 0.497 CDKN1C 11p15.5 700 41400 at 0.497 TK1 17q23.2-q25.3 701 41283 at 0.496 HNRPH3 1Oq22 702 33835 at 0.496 f~lAA0721 6q22.31 703 34359 at 0.496 CGI-130 6q13-q24.3 704 38875 r at 0.495 GREB1 2p25.1 705 40096 at 0.495 ATP5A1 18q12-q21 706 37529 at 0.494. CACNA1 H 16p13.3 707 39418 at 0.493 DI~F~P564M182 16p13.3 708 31897 at 0.493 D~C1 3q12.3 709 37381_g at 0.493 GTF2B 1 p22-p21 710 40280 at 0.492 B7 12p13 711 40136 at 0.492 KIAA0676 5q35.3 712 506 s at 0.492 STATSA 17q11.2 713 35812 at 0.492 TRN-SR 7q31.1 714 41853 at 0.491 PRPSAP2 17p11.2-p12 715 37022 at 0.491 PRELP 1 q32 716 38079 at 0.49 GNG12 1p31.2 717 36149 at 0.489 DPYSL3 5q32 718 31880 at 0.489 D8S2298E 8p12-p11.2 719 37199 at 0.488 CGI-60 2p25.1-p24.1 720 37671 at 0.488 LAMA4 6q21 721 31573 at 0.488 RPS25 11 q23.3 722 39696 at 0.488 PEG10 7q21 723 39723 at 0.487 CUL1 7q34-q35 724 1074 at 0.486 RAB 1 A 2p 14 725 32175 at 0.486 CDC10 7p14.3-p14.1 Cytogenetic Probe Sets CC Gene Symbol Location 726 35364 at 0.486 APPBP1 16q22 727 39019 at 0.486 LAPTM4A 2p24.3 728 41772 at 0.485 MAOA ~Cp11.4-p11.3 729 31670 s at 0.485 CAMK2G 10q22 730 33426 at 0.484 CHGB 20pter-p12 731 34393 r at 0.483 RAB1A 2p14 732 40903 at 0.483 ATP61P2 7Cq21 733 41251 at 0.483 TRIP3 17q21.1 734 39054 at 0.482 GSTM 1 1 p 13.3 735 33912 at 0.482 ZMPSTE24 1 p34 736 40709 at 0.482 ZNF271 18q12 737 38662 at 0.482 BCRP1 14q24.1 738 32755 at 0.481 ACTA2 10q23.3 739 39741 at 0.481 HADHB 2p23 740 35169 at 0.481 COL16A1 1 p35-p34 741 40555 at 0.48 TC10 2p21 742 315 at 0.48 PRDM2 1 p36 743 39180 at 0.48 FUS 16p11.2 744 35253 at 0.479 GAB2 11 q13.4 745 31536 at 0.479 RTN4 2p14-p13 746 767 at 0.478 M1fH11 16p13.13-p13.12 747 39797 at 0.477 I~IAA0349 6p21.1 748 32854 at 0.477 FBXlnl1 B 5q35.1 749 41191 at 0.477 I<IAA0992 4q32.3 750 1151 _at 0.476 RPL22 1 p36.3-p36.2 751 35294 at 0.476 SSA2 1 q31 752 1708 at 0.475 MAP~C10 4q22.1-q23 753 39031 at 0.475 COX7A1 19q13.1 754 37747 at 0.475 AN?CA5 4q28-q32 755 38433 at 0.475 AXL 19q13.1 756 38049_g at 0.473 RBPMS 8p12-p11 757 36595 s at 0.473 GATM 15q14 758 39124 r at 0.473 TRPC1 3q22-q24 759 706 at 0.473 NR3C1 5q31 760 37399 at 0.47 AKR1C3 10p15-p14 761 38265 at 0.47 RBBP6 16p12-p11.2 762 32563 at 0.469 ATP1 B3 3q22-q23 763 37734 at 0.469 DIP2 21 q22.3 764 39245 at 0.469 '?

765 40618 at 0.468 H41 3q22.2 766 31886 at 0.467 NTSE 6q14-q21 767 41807 at 0.467 cDNA FLJ31959 fis 768 1447 at 0.467 PSMB1 6q27 Cytogenetic Probe Sets CC Gene Symbol Location 769 41227 at 0.466 OCRL Xq25-q26.1 770 774_g_at 0.465 MYH11 16p13.13-p13.12 771 35794 at 0.465 EFA6R 8pter-p23.3 772 32254 at 0.464 FSTL3 19p13 773 38694 at 0.464 KIAA0738 7q33 774 34391 at 0.464 IGBP1 Xq13.1-q13.3 775 39183 at 0.463 PCTK1 Xp11.3-p11.23 776 31894 at 0.463 CENPC1 4q12-q13.3 777 39260 at 0.462 SLC16A4 1 p13.1 778 232 at 0.462 LAMC1 1 q31 779 1380 at 0.461 FGF7 15q15-q21.1 780 41737 at 0.46 SRRM 1 1 p36.11 781 36851_g at 0.46 N33 8p22 782 41488 at 0.459 LOC57149 16p11.2 783 39623 at 0.457 NDP Xp11.4 784 36970 at 0.456 ICIAA0182 16q24.1 785 40086 at 0.456 KIAA0261 1Oq23.31-q23.32 786 33421 s at 0.455 SCSDL 11 q23.3 787 4004~7_at 0.454 SBB103 12q12 788 39420 at 0.452 DDIT3 12q13.1-q13.2 789 37951 at 0.451 DLC1 8p22-p21.3 790 855 at 0.45 PDCD2 6q27 791 35739 at 0.45 MTMR3 22q12.2 792 33451 _s_at 0.449 RPL22 1 p36.3-p36.2 793 1278 at 0.448 AXL 19q13.1 794 38542 at 0.445 ?

795 31896 at 0.44 NAG 2p24 796 33341 at 0.439 GNB1 1 p36.33 797 1846 at 0.439 LGALS8 1 q42-q43 798 288 s at 0.438 LBR 1 q42.1 799 31672_g at 0.438 RBMS1 P

800 36120 at 0.437 FVT1 18q21.3 801 38371 at 0.437 PSMA1 11p15.1 802 31812 at 0.436 GMPR 6p23 803 35311 at 0.436 CREG 1 q24 804 41837 at 0.436 DiCFZp761 F2014 14q32.2 805 39775 at 0.436 SERPING1 11q12-q13.1 806 37765 at 0.433 LMOD1 1q32 807 39733 at 0.431 HERPUD1 16q12.2-q13 808 38075 at 0.43 SYPL 7q11.23 809 41289 at 0.43 NCAM 1 11 q23.1 810 38459_g at 0.429 CYB5 18q23 811 40461 at 0.427 TIX1 20q12 Cyto,genetic Probe Sets CC Gene Symbol Location 812 ~ 33220 at 0.427 ZNF187 6p21.31 813 32769 at 0.426 ALFY 4q21.23 814 1394 at 0.425 ARHA 3p21.3 815 35720 at 0.422 KIAA0893 1 p13.2 816 34366_g at 0.42 PPIE 1 p32 817 38737 at 0.418 IGF1 12q22-q23 818 38326 at 0.417 GOS2 1 q32.2-q41 819 34378 at 0.417 ADFP 9p21.2 820 38458 at 0.417 CYB5 1 Sq23 821 35286 r at 0.415 RY1 2p13.1 822 37309 at 0.413 ARHA 3p21.3 823 36634 at 0.412 BTG2 1 q32 824 753 at 0.412 NID2 14q21-q22 825 37195 at 0.411 CYP11A 15q23-q24 826 37536 at 0.409 CD83 f p23 827 32066_g at 0.407 CREM 10p12.1-p11.1 828 41759 at 0.403 SKP1A 5q31 829 36968 s at 0.403 OIP2 13q13.1 830 40471 at 0.402 PXF 1 q22 831 35740 at 0.397 EMILIN 2p23.3-p23.2 832 32242 at 0.394 DI~F~p5661C192 833 1596_g at 0.393 TEFL 9p21 834 34785 at 0.392 I<IAA1025 12q24.21 835 37718 at 0.391 SNRFC 3p21.31 836 37701 at 0.391 RGS2 1 q31 83 33756 at 0.39 AOC3 17q21 838 40621 at 0.389 PA1NV R 12q21 839 583 s at 0.387 ~/CAM1 1p32-p31 840 34793 s at 0.387 PLS3 Xq24 841 39163 at 0.385 KIDINS220 2p24 842 36681 at 0.384 APOD 3q26.2-qter 843 37623 at 0.384 NR4A2 2q22-q23 844 38054 at 0.382 HBXIP 1p13.1 845 33848 r_at 0.38 CDKN1 B 12p13.1-p12 846 280_g at 0.377 NR4A1 12q13 847 1787 at 0.374 CDICN1 C 11 p15.5 848 37694 at 0.373 PHF3 849 36458 at 0.372 KIAA1018 15q12 850 32849 at 0.371 SMC1 L1 Xp11.22-p11.21 851 39046 at 0.371 H2AV 7p13 852 36974 at 0.37 PSMF1 20p12.2-p13 853 547 s_at 0.37 NR4A2 2q22-q23 854 479 at 0.37 DAB2 5p13 Cytogenetic Probe Sets CC Gene Symbol Location 855 1737 s at 0.366 IGFBP4 17q12-q21.1 856 32847 at 0.363 MYLIC 3q21 857 37732 at 0.363 RYBP 3p13 858 32184 at 0.358 LMO2 11p13 859 41046 s at 0.357 ZNF261 Xq13.1 860 40487 at 0.356 MC7 11 p11.2 861 32067 at 0.353 CREM 10p12.1-p11.1 862 39561 at 0.352 DNAL4 22q13.1 863 36569 at 0.347 TNA 3p22-p21.3 864 39373 at 0.345 FADS 1 11 q 12.2-q 13.1 865 38466 at 0.344 CTSI< 1 q21 866 34784 at 0.336 DJ37E16.5 22cen-q12.3 867 37842 at 0.336 HIC 7q21.11 868 33255 at 0.334 NASP 8q 11.23 869 1005 at 0.329 DUSP1 5q34 870 41864 at 0.329 ?

871 1241 at 0.327 PTP4A2 1 p35 872 38228_g at 0.323 MITF 3p14.1-p12.3 873 32340 s at 0.322 NSEP1 1 p34 874 38312 at 0.316 cDNA DI<F~p564O222 875 32313 at 0.305 TPM2 9p13.2-p13.1 876 773 at 0.302 Ml'H11 16p13.13-p13.12 877 36065 at 0.301 LDB2 4p16 878 39066 at 0.29 MFAP4 17p11.2 879 34826 at 0.288 SDHA 5p15 880 38430 at 0.286 FABP4 8q21 881 31855 at 0.274 SRPX 3cp21.1 882 33440 at 0.273 TCFB 10p11.2 883 40856 at 0.258 SERPINF1 17p13.1 884 40282 s at 0.251 DF 19p13.3 885 36165 at 0.234 CO?C6C 8q22-q23 886 36201 at 0.234 GL01 6p21.3-p21.1 887 36521 at 0.233 DZIP1 13q32.1 888 36931 at 0.225 TAGLN 11 q23.2 889 32314_g at 0.222 TPM2 9p13.2-p13.1 890 40824 at 0.206 RANBP16 8p21 891 33790 at 0.203 CCL14 17q11.2 892 38734 at 0.198 PLN 6q22.1 893 39690 at 0.182 ALP 4q35 894 31830 s at 0.176 SMTN 22q12.2 895 31831 at 0.157 SMTN 22q12.2 896 34203 at 0.15 CNN1 19p13.2-p13.1 897 1197 at 0.134 ACTG2 2p13.1 Cytogenetic Probe Sets CC Gene Symbol Location 898 38995 at 0.114 CLDN5 22q11.21 899 38994 at 0.114 SOCS2 12q 900 36892 at 0.042 ITGA7 12q13 Table 10. Ranking of the Top 100 Probe Sets Based on PCC Values Probe Probe set Probe set Set R1 R2 name R1 R2 name R1 R2 Name 37628 0.8650.808 37529 at 0.6690.494 201 s at 0.5770.571 at 41859 0.8650.877 32175 at 0.6690.486 774_g_at 0.5760.465 at 38120 0.8520.768 35753 at 0,6670.535 40998 at 0.5760.539 at 32664 0.8480.749 38875 r 0.6670.495 41772 at 0.5730.485 at at 35717 0.8470.783 32779 s 0.6650.599 40522 at 0.5720.544 at at 34257 0.8460.786 41385 at 0.6650.594 41768 at 0.5710.573 at 38220 0.8440.820 1319 at 0.6640.580 37828 at 0.5690.677 at 40423 0.8440.785 32593 at 0.6640.692 280_g_at 0.5690.377 at 38650 0.8400.748 38101 at 0.6630.615 33431 at 0.5680.536 at 38439 0.8290.736 39864 at 0.6630.698 1278 at 0.5660.448 at 39673 0.8270.698 39037 at 0.6630.726 35736 at 0.5660.508 i at 38047 0.8220.756 32057 at 0.6630.667 37985 at 0,5650.608 at 1396 at 0.8210.756 38518 at 0.6630.517 38326 at 0.5650.417 37015 0.8200.751 39556 at 0.6620.725 37197 s 0.5620.647 at at 40775 0.8180.666 40841 at 0.6610.618 41529_g 0.5600.620 at ~t 32145 0.8970.786 39117 at 0.6600.557 39838 at 0.5600.582 at 35742 0.8150.708 36695 at 0.6590.602 2092 s 0.5600.679 at at 1290_g 0.8140.776 39681 at 0.6590.690 31672_g 0.5590.4.38 at ~t 39674 0.8110.699 1850 at 0.6590.529 37701 at 0.5590.391 r at 36917 0.8100.752 38013 at 0.6570.672 40260 g_at0.5580.516 at 1897 at 0.8090, 1377 at 0. 0. 33249 at 0.5580.

755 at 0.8090.822 4.1739 0.6550.552 33198 at 0.5570.705 s at 38176 0.8050, 36095 at 0.6540.513 1708 at 0.5570.475 at 729 36073 0.8040.774 35169 at 0.6530.481 38116 at 0.5560.595 at 32764 0.8030.691 36533 at 0.6530.517 40832 s 0.5550.505 at at 39750 0.7960.718 40698 at 0.6510.563 37958 at 0.5550.617 at 35645 0.7950.714 33936 at 0.6510.702 1787 at 0.5550.374 at 38717 0.7930.789 1507 s 0.6480.596 333 s at 0.5540.562 at at 37394 0.7910.656 38211 at 0.6480.681 36690 at 0.5540.654 at 36160 0.7880.846 38351 at 0.6480.527 40839 at 0.5530.611 s at 36867 0.7850.739 32239 at 0.6470.548 32569 at 0.5530.534 at 39852 0.7830.738 32582 at 0.6460.578 33302 at 0.5530.619 at 37643 0.7790.716 31880 at 0.6460.489 1058 at 0.5520.668 at 40767 0.7790.640 38342 at 0.6460.632 38990 at 0.5520.526 at 41449 0.7780.762 33240 at 0.6450.511 506 s at 0.5510.492 at 40488 0.7780.698 33136 at 0.6450.628 871 s at 0.5500.630 at Probe Probe set Probe set Set R1 R2 name R1 R2 name R1 R2 Name 40063 0.7770.729 39438 at 0.6450.690 33113 at 0.5490.554 at 41685 0.7770.871 38035 at 0.6440.528 507 s at 0.5480.523 at 34163_g 0.7760.709 33278 at 0.6440.560 40876 at 0.5480.754 at 40570 0.7750.668 33140 at 0.6440.607 35754 at 0.5480.632 at 37446 0.7710.613 40145 at 0.6430.636 34287 at 0.5460.553 at 38669 0.7700.712 34215 at 0.6420.611 39775 at 0.5450.436 at 36627 0.7700.675 538 at 0.6420.622 41174 at 0.5450.497 at 1640 at 0.7690.734 35846 at 0.6410.655 34417 at 0.5450.525 35681 0.7690.644 39545 at 0.6410.546 34259 at 0.5440.541 r at 36894 0.7680.634 32521 at 0.6410.563 38724 at 0.5440.512 at 41137 0.7680.631 34320 at 0.6400.557 32769 at 0.5430.426 at 39397 0.7680.700 39939 at 0.6400.593 33916 at 0.5410.501 at 41273 0. 0.758 40419 at 0.6390.599 38470 i 0, 0.565 at 766 at 541 38122 0.7610.707 36791_g_at0.6380.556 38438 at 0.5400.612 at 40861 0.7600.689 40674 s 0.6380.697 31573 at 0.5400.488 at afi 35164 0.7580.621 32143 at 0.6380.532 31536 at 0.5390.479 at 39400 0.7580.634 41770 at 0.6360.539 39376 at 0.5390.715 at 872 i 0.7560.770 1909 at 0.6360.736 34859 at 0.5380.669 at 41738 0.7550.707 38581 at 0.6360.573 39733 at 0.5380.431 at 38113 0.7540.689 41227 at 0.6360.466 1211 s 0.5380.614 at at 40202 0.7540.606 39790 at 0.6350.579 38059_g 0.5370.524 at at 34760 0.7490.699 36091 at 0.6350.652 31510 s 0.5370.668 at at 32526 0.7490.639 41191 at 0.6330.477 1596_g_at 0.5370.393 at 4.0994 0.7490.627 33817 at 0.6310.606 38711 at 0.5370.578 at 37908 0.7480.557 36790 at 0.6310.552 32676 at 0.5360.642 at 38119 0.7470.669 34162 at 0.6310.566 38768 at 0.5360.671 at 33690 0.7470.565 39691 at 0.6300.683 34819 at 0.5350.614 at 41478 0.7450.653 38049_g 0.6300.473 36629 at 0.5350.550 at at 38634 0.7450.677 38754 at 0.6280.560 36578 at 0.5350.575 at 32109 0.7440.670 31605 at 0.6280.539 35335 at 0.5340.616 at 41049 0.7440.694 37230 at 0.6280.590 39687 at 0.5340.525 at 32778 0.7430.689 41300 s 0.6280.519 37532 at 0.5340.710 at at 37599 0.7430.652 31897 at 0.6270.493 41634 at 0.5340.629 at 32076 0.7410.713 34789 at 0.6270.606 34821 at 0.5340.638 at 35234 0.7410.634 40508 at 0.6260.610 38627 at 0.5330.512 at 40853 0.7400.647 33303 at 0.6260.540 34445 at 0.5330.526 at 1731 at 0.7390.619 35221 at 0.6260.520 32755 at 0.5330.481 39714 0.7380.743 37638 at 0.6260.607 1578_g_at 0.5320.661 at 41505 0.7380.638 41744 at 0.6250.723 34349 at 0.5300.603 r at 1761 at 0.7360.789 41405 at 0.6250.592 33426 at 0.5290.484 7~
Probe Probe set Probe set Set R1 R2 name R1 R2 name R1 R2 Name 36577 0.7360.549 41594 at 0.6250.644 39441 at 0.5290.633 at 32805 0.7360.586 35782 at 0.6250.638 39066 at 0.5280.290 at 1577 at 0.7360.767 37221 at 0.6250.501 38318 at 0.5270.741 38643 0.7350.714 39147_g 0.6240.592 40438 at 0.5270.517 at at 40496 0.7340.617 38727 at 0.6240.597 1090 f 0.5250.671 at at 1135 at 0.7320.652 39109 at 0.6230.564 40077 at 0.5240.650 41138 0.7310.565 40399 r 0.6230.630 38079 at 0.5230.490 at at 38968 0.7290.683 39436 at 0.6230.596 35811 at 0.5220.566 at 1327 s 0.7290.626 37710 at 0.6220.600 1530_g 0.5220.560 at at 34772 0.7290.748 36727 at 0.6210.558 38690 at 0:5200.591 at 32535 0.7290.582 33911 at 0.6210.633 41147 at 0.5200.699 at 37743 0.7290.617 36634 at 0.6210.412 39693 at 0.5170.544 at 34355 0.7280.662 36118 at 0.6200.542 1147 at 0.5170.637 at 32259 0.7280.611 39019 at 0.6200.486 34808 at 0.5170.614 at 32251 0.7280.615 33244 at 0.6200.512 39055 at 0.5170.610 at 41000 0.7270.677 39557 at 0.6190.549 39986 at 0.5160.591 at 40786 0.7250.705 40962 s 0.6190.521 33380 at 0.5150.501 at at 39701 0.7240.611 39829 at 0.6180.656 34793 s 0.5120.387 at at 34740 0.7230.520 31852 at 0.6170.687 37842 at 0.5120.336 at 35354 0.7230.667 818 s at 0.6170.554 39685 at 0.5110.568 at 34169 0. 0.661 37407 s 0.6170.506 38279 at 0.59 0.587 s at 722 at 9 36873 0.7220.633 36829 at 0.6170.598 41420 of 0.5100.593 at 39243 0.7210.702 35173 at 0.6170.774 547 s at 0.5060.370 s at 34877 0.7200.671 39846 at 0.6170.564 1252 at 0.5060.548 at 36119 0.7200.622 897 at 0.6160.497 32597 at 0.5060.648 at 38364 0.7190.599 40607 at 0.6160.708 32847 at 0.5050.363 at 39025 0.7980.746 32087 at 0.6160.672 1678_g 0.5040.638 at at 32747 0.7170.706 36488 at 0.6150.636 35999 r 0.5040.531 at at 36975 0.7170.696 487_g at 0.6150.512 36964 at 0.5040.542 at 33443 0.7160.635 40617 at 0.6150.645 753 at 0.5040.412 at 32542 0.7160.662 853 at 0.6140.674 32153 s 0.5040.575 at at 32765 0.7160.528 1101 at 0.6120.531 32668 at 0.5030.609 f at 41013 0.7150.578 35359 at 0.6120.653 38394 at 0.5000.790 at 37707 0.7150.528 32851 at 0.6120.614 37623 at 0.5000.384 i at 35785 0.7150.691 41195 at 0.6100.502 41759 at 0.5000.403 at 35783 0.7130.753 40825 at 0.6080.556 37027 at 0.5000.516 at 36515 0.7120,583 33235 at 0.6070.587 33756 at 0.5000.390 at 924 s 0.7110.640 40155 at 0.6060.547 1120 at 0.4980.500 at 33857 0.7100.630 37617 at 0.6060.636 40971 at 0.4970.581 at 35704 0.7090.656 40213 at 0.6060.615 1307 at 0.4970.562 at Probe Probe set Probe set Sefi R1 R2 name R1 R2 name R1 R2 Name 41747 0.7090.587 39260 at 0.6060.462 35955 at 0.4960.640 s at 35316 0.7090.743 39294 at 0, 0.595 38459_g 0.4940.429 at 606 at 38508 0.7080.542 2062 at 0,6050.528 38466 at 0.4930.344 s at 35644 0.7070.535 31886 at 0.6050.467 32708_g 0.4930.627 at at 35366 0.7070.560 38695 at 0.6050.715 36521 at 0.4930.233 at 37005 0.7060.541 767 at 0.6040.478 37315 f 0.4920.602 at at 40961 0.7050.745 33878 at 0.6040.657 33440 at 0.4920.273 at 36948 0.7030.766 32119 at 0.6030.515 35843 at 0.4910.567 at 39743 0.7020.775 38228_g 0.6020.323 34235 at 0.4910.554 at at 39369 0.7020.635 39979 at 0.6020.588 479 at 0.4900.370 at 36596 0.7010.575 35740 at 0.6010.397 40140 at 0.4900.666 r at 39038 0.7000.576 32768 at 0.6010.648 41379 at 0.4900.524 at 851 s 0.6990.725 34637 f 0.6000.678 39623 at 0.4890.457 at at 41655 0.6990.664 35794 at 0.6000.465 41277 at 0.4890.632 at 35246 0.6970.652 36792 at 0.5990.521 36458 at 0.4880.372 at 38317 0.6960.593 39366 at 0.5990.529 35166 at 0.4880.544 at 37294 0. 0. 40770 f 0, 0. 654 at 0.4880.643 at 696 649 at 599 655 35168 0.6960.540 38673 s 0.5980.497 38826 at 0.4880.505 f at at 36159 0.6960.589 1675 at 0.5980.535 33915 at 0.4870.599 s at 35752 0.6950.642 33399 at 0.5980.544 38626 at 0.4870.509 s at 35325 0.6940.577 32700 at 0.5980.592 38074 at 0.4860.634 at 718 at 0.6930.540 32337 at 0.5970.589 35730 at 0.4860.515 37708 0.6920.545 41872 at 0.5970.538 31932 f 0.4.850.586 r at at 34363 0.6910.665 1836 at 0.5970.543 34803 at 0.4850.633 at 2086 s 0.6910.637 176 at 0.5960.654 32066_g 0.4840.407 at at 37406 0.6900.665 37598 at 0.5950.551 37199 at 0.4840.488 at 41796 0.6890.779 41490 at 0.5930.525 33123 at 0.4830.605 at 35331 0.6890.565 37266 at 0.5930,548 36120 at 0.4830.437 at 1736 at 0.6880.688 39054 at 0.5920.482 35294 at 0.4830.476 32107 0.6880.598 31936 s 0.5910.565 39856 at 0.4820.620 at at 34853 0.6880.538 1629 s 0.5910.692 1323 at 0.4820.604 at at 36396 0.6880.785 40461 at 0,5880.427 33830 at 0.4810.679 at 32780 0.6870.594 39031 at 0.5880.475 38743 f 0.4810.524 at at 32254 0,6870.464 1529 at 0.5880.586 41656 at 0.4800.661 at 37248 0.6870.536 39555 at 0.5880.517 38106 at 0.4790.497 at 33800 0.6870.579 1005 at 0.5880.329 39420 at 0.4790.452 at 38837 0.6860.584 38745 at 0.5870.702 39110 at 0.4780.566 at 39360 0.6850.640 35784 at 0.5870.622 31463 s 0.4780.500 at at 41638 0.6830.633 706 at 0.5870.473 41686 s 0.4760.545 at at 34198 0.6830.638 41620 at 0.5870.618 41289 at 0.4760.430 at Probe Probe set Probe set Set R1 R2 name R1 R2 name R1 R2 Name 38033 0.6830.597 226 at 0.5870.657 34860_g 0.4760.593 at at 1467 at 0.6820.537 35739 at 0.5860.450 38980 at 0.4750.539 40203 0.6820.660 37205 at 0.5860.645 36636 at 0.4750.590 at 38685 0.6800.665 2010 at 0.5850.732 36423 at 0.4740.523 at 35741 0.6800.679 37391 of 0.5850.567 36065 at 0.4730.301 at 39150 0.6800.569 38433 at 0.5850.475 1873 at 0.4730.507 at 37604 0.6790.573 39165 at 0,5840.770 35055 at 0.4720.586 at 38812 0.6790.509 1677 at 0.5840.555 38694 at 0.4710.464 at 32696 0.6780.60~F 1380 at 0.5830.461 35276 at 0.4710.575 at 38375 0.6780.713 34842 at 0.5830.759 33799 at 0.4690.607 at 38254 0.6780.609 35767 at 0.5830.662 773 at 0.4690.302 at 39082 0.6770.587 36825 at 0.5830.587 40211 at 0.4670.636 at 32215 0.6770.540 38385 at 0.5830.529 35303 at 0.4650.554 i at 39582 0.6770.698 34675 at 0.5820.540 41283 at 0.4640.496 at 36915 0.6760.720 1737 s 0.5870.366 37195 at 0.4630.411 at at 40576 0.6760.652 38982 at 0.5800.799 40811 at 0.4630.552 f at 539 at 0.6750.556 41271 at 0.5800.560 1348 s 0.4630.798 at 37595 0.6740.618 36595 s 0.5790.473 32038 s 0.4620.512 at at at 33868 0.6730.637 38923 at 0.5780.605 33856 at 0.4620.542 at 37676 0.6730.546 37765 at 0.5780.433 36543 at 0.4610.648 at 39124 0.6730.473 37373 at 0.5780.710 37022 at 0.4610.491 r at 38649 0.6730.520 37389 at 0.5780.526 38610 s 0.4590.549 at at 41771_g 0.6730.687 38916 at 0.5780.651 33444 at 0.4590.505 at 227_g 0.6720.733 33899 at 0.5780.555 32713 at 0.4590.598 at Probe Probe Set Set R1 R2 Name R1 R2 Name 37486 0.4590.615 1848 at 0.2880.591 f at 38802 0.4580.739 34203 at 0.2880.150 at 32695 0.4580.722 40280 at 0.2860.492 at 35142 0.4570.504 37359 at 0.2860.507 at 149 at 0.4570.544 33790 at 0.2850.203 39561 0.4570.352 38542 at 0.2830.445 _at 33835 0.4570.496 33870 at 0.2810.589 at 32244 0.4550.576 869 at 0.2810.518 at 32313 0.4530.305 315 at 0.2770.480 at 37242 0.4520.753 36970 at 0.2750.456 at 38353 0.4510, 38782 at 0.2750.555 at 628 32743 0.4510.577 39739 at 0.2750.502 at 36149 0.4490.489 41662 at 0.2750.661 at 38050 0.4490.599 37748 at 0.2740.617 at 39072 0.4480.662 33441 at 0.2720.561 at Probe Probe Set Set R1 R2 Name R1 R2 Name 32777 0.4470.743 35228 at 0.2720.624 at 38458 0.4470.417 32160 at 0.2690.576 at 37731 0.4460.602 41853 at 0.2680.491 _at 38265 0.4460.470 41746 at 0.2680.523 at 41194 0.4450.533 32854 at 0.2680.477 at 40856 0.4450.258 1070 at 0.2670.601 at 36650 0.4440.612 35737 afi 0.2660.583 at 39431 0.4430.622 232 at 0.2660.462 _at 39380 0.4430.544 34785 at 0.2590.392 at 37131 0.4430.585 34792 at 0.2590.510 at 32576 0.4420.502 36542 at 0.2570.686 at 36991 0.4410.543 192 at 0.2550.643 _at 324 f 0.4410.562 36330 at 0.2550.511 at 41837 0.4400.436 35364 at 0.2540.486 at 34753 0.4390.587 1846 at 0.2520.439 at 40083 0.4380.539 32172 at 0.2520.554 at 35936_g 0.4370.563 37616 at 0.2490.516 at 32067 0.4340.353 32822 at 0.2470.585 at 39045 0.4320.549 40105 afi 0.2460.524 afi 36489 0.4320.636 509 at 0.2450.551 at 41242 0.4310.502 37352 at 0.2440.528 at 1501 _at 0.4310.576 36169 at 0.2430.528 39315 0.4300.659 34890 at 0.2420.527 at 32618 0.4300.786 34~370_at 0.2420.533 afi 40136 0.4270.492 40709 at 0.2400.482 afi 36626 0.4260.571 33865 at 0.2360.599 afi 32667 0.4.260.588 38820 at 0.2350.598 afi 37951 0.4260.451 35213 at 0.2350.546 _at 36569 0.4260.347 36492 at 0.2350.583 at 32611 0.4250.518 35356 at 0.2330.619 _at 34990 0.4240.688 35643 at 0.2300.677 at 32184 0.4240.358 33847 s 0.2290.574 at at 38693 0.4240.623 34359 at 0.2290.496 at 38737 0.4230.418 39346 at 0.2280.573 at 34372 0.4230.564 39517 at 0.2270.524 at 32792 0.4230.612 202 at 0.2250.545 at 36681 0.4230.384 35720 at 0.2250.422 _at 39091 0.4220.668 40859 at 0.2250.526 at 33126 0.4220.651 33343 at 0.2210.600 at 41333 0.4210.595 950 at 0.2210.653 at 41338 0.4200.605 32548 at 0.2190.539 at 38064 0.4190.672 35163 at 0.2190.676 at 37718 0.4190.391 38662 at 0.2180.482 at Probe Set Probe Name R1 R2 Set R1 R2 Name 38892 at 0.4180.642 39083 0.2130.575 at 39741 at 0.4170.481 41864 0.2110.329 at 40601 at 0.4170.680 39723 0.2100.487 at 39170 at 0.4170.561 36968 0.2070.403 s at 32314_g 0.4170.222 32564 0.2060.537 at at 34784 at 0.4160.336 35209 0.2050.652 at 35988 i 0.4160.547 41600 0.2040.571 at at 39809 at 0.4160.516 39428 0.2030.630 at 31872 at 0.4150.630 40480 0.1980.761 s at 33942 s 0.4120.735 39180 0.1980.480 at at 37399 at 0.4110.470 41830 0.1970.647 at 37379 at 0.4100.684 37309 0.1960.413 at 37895 at 0.4070.500 1241 at 0.1950.327 32099 at 0.4070.570 38415 0.189~.593 at 32253 at 0.4050.544 288 s 0.1870.438 at 39989 at 0.4030.587 37891 0.1860.559 _at 36980 at 0.4030.592 32745 0.1830.511 at 508 at 0.4020.589 38479 0.1820.504 at 38312 at 0.4020.316 39401 0.1810.
_at 699 39350 at 0.4010.727 36851_g 0.1810.460 at 37725 at 0.4010.578 39418 0.1800.493 at 39663 at 0.4000.565 31894 0.1770.463 at 39509 at 0.3980.533 31896 0.1750.440 at 34344 at 0.3980.541 32730 0.1730.548 at 33862 at 0.3980.615 40239_g 0.1690.519 ~t 39897 at 0.3970.529 2003 s 0.1690.652 at 39690 at 0.3960.182 32169 0.1650.556 at 729 i at 0.3960.597 39696 0.1650.488 at 41400 at 0.3950.497 32240 0.1630.562 at 32841 at 0.3950.594 34684 0.1610.538 at 41807 at 0.3950.467 35301 0.1610.502 at 659_g at 0.3910.641 38054 0.1600.382 at 33351 _at 0.3890. 41606 0.1590.562 664 at 1127 at 0.3860.697 40634 0.1580.532 at 33875 at 0.3840.539 37747 0.1570.475 at 39046 at 0.3830.371 40096 0.1560.495 at 36931 at 0.3820.225 36032 0.1550.550 at 41288 at 0.3810.641 41046 0.1530.357 s at 31670 s 0.3800.485 35738 0.1400.755 at at 40281 at 0.3800.562 35812 0.1400.492 at 911 s at 0.3790.618 40064 0.1380.506 at 34570 at 0.3740.586 40047 0.1370.454 at 1725 s 0.3730.541 32340 0.1350.322 at s at ~3 Probe Probe Set R1 R2 Set R1 R2 Name Name 40282 0.3720.251 31944 0.1330.601 s at at 32662 0.3710.578 32242 0.1330.394 at at 38985 0.3700.633 32170_g 0.1320.529 at at 583 s 0.3700.387 38408 0.1320.656 at at 38430 0.3690.286 36892 0.1150.042 at at 35203 0.3670.566 171 at 0.1150.543 at 38734 0.3670.198 31866 0.1150.529 at at 40432 0.3670.573 36925 0.1140.537 at at 1151 _at 0.3650.476 36620 0.1130.
at 692 37671 0.3650.488 34774 0.1090.642 at at 39797 0.3640.477 40039_g 0.1070.625 at at 40618 0.3630.468 40824 0.1060.206 at at 39740_g 0.3620.512 34314 0.1050.547 at at 39097 0.3620.595 40621 0.1030.389 at at 41251 0.3590.483 34393 0.1020.483 at r_at 40916 0.3590.777 33103 0.0980.596 at s at 34255 0.3580.531 35311 0.0980.436 at at 41701 0.3580.570 147 at 0.0930.516 at 36474 0.3580.569 33220 0.0910.427 at at 31831 0.3580.157 969 s 0.0910.503 at at 41713 0.3580.535 39884_g 0.0900.531 at at 38994 0.3550.114 32849 0.0900.371 at at 39686_g 0.3510.548 237 s 0.0890.639 ~t at 33222 0.3500.626 41742 0.0870.642 at s at 36526 0.3500.584 31867 0.0840.602 at at 40843 0.3470.555 3924.5 0.0830.469 at at 31812 0.3460.436 41488 0.0820.459 at at 450_g 0.3460.576 36974 0.0800.370 at at 39931 0.3460.535 33912 0.0760.482 at at 40831 0.3450.544 35286 0.0740.415 at r at 1719 at 0.3450.606 37734 0.0730.469 at 40471 0.3450.402 38011 0.0730.533 at at 39118 0.3440.517 39715 0.0710.638 at at 37107 0.3430.590 41136 0.0710.567 at s at 41462 0.3420.550 40467 0.0710.537 at at 38075 0.3410.430 36899 0.0690.499 at at 39163 0.3390.385 32171 0.0680.535 at at 39405 0.3390.537 1195 s 0.0670.505 at at 31830 0.3380.176 35318 0.0670.585 s at at 37367 0.3380.520 38839 0.0650.597 at at 39731 0.3370.629 34356 0.0650.673 at at 41691 0.3350.572 39033 0.0650.606 at at 37381_g 0.3340.493 40854 0.0620.506 at at Probe Probe Set R1 R2 Set R1 R2 Name Name 37706 0.3330.650 38654 0.0600.513 at at 37694 0.3300.373 41743 0.0590.528 at i at 33341 0.3290.439 35247 0.0520.525 at at 37715 0.3280.619 40086 0.0510.456 at at 35435 0.3250.736 33405 0.0480.548 s at at 32511 0.3240.586 36165 0.0440.234 _at at 33229 0.3230.589 33819 0.0410.549 at at 1512 at 0.3220.529 34378 0.0390.417 at 1197 at 0.3200.134 31993 0.0380.667 f at 40191 0.3190.520 663 at 0.0370.531 _s_at 34781 0.3180.509 34680 0.0370.521 _at s at 855 at 0.3150.450 33421 0.0360.455 s at 39351 0.3150.653 33892 0.0360.579 at at 38400 0.3140.594 34826 0.0350.288 at at 1074 at 0.3130.486 37029 0.0330.526 at 37732 0.3120.363 35253 0.0310.479 at at 41737 0.3120.460 38371 0.0260.437 at at 36544 0.3110.662 33850 0.0260.512 at at 31508 0.3090.599 40487 0.0250.356 at at 37536 0.3090.409 34366 0.0250.420 at g ~t 38046 0.3070.599 35751 0.0250.605 at at 37025 0.3050.504 33848 0.0240.380 at r at 33451 0.3040.449 274 at 0.0230.510 s at 2039 s 0.3040.723 36201 0.0210.234 at at 34391 0.3040.464 40555 0.0210.480 at at 37736 0.3030. 393 7 0.0190.345 at 6~ 3 at 31855 0.3030.274 35304 0.0170.511 at at 218 at 0.3010.621 39762 0.0170.571 at 32506 0.3000.558 33255 0.0150.334 at at 1394 at 0.3000.425 35258 0.0130.507 f at 31907 0.2990.539 40988 0.0110.522 at at 32563 0.2990.469 933 f 0.0110.604 at at 41424 0.2990.588 1447 at 0.0100.467 at 33418 0.2990.553 37672 0.0090.518 at at 39183 0.2980.463 38995 0.0060.114 at at 40903 0.2930.483 890 at 0.0060.548 at Note: Absolute CC values are shown for expression levels analyzed in all 36 samples (R1 ) and in the 18 test samples only (R2).
CCs z0.5 are italicized and underlined.

References Gited [180] All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. The discussion of references herein is intended merely to summarize the assertions made by their authors and no admission is made that any reference constitutes prior art. Applicants reserve the right to challenge the accuracy and pertinence of the cited references.
[181) In addition, all GenBank accession numbers, tJnigene Cluster numbers and protein accession numbers cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each such number was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.
[182] The present invention is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations of individual aspects of the invention. I'rAiany modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in fihe art. Functionally equivalent methods and apparatus within the scope of the invention, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing description and accompanying drawings. Such modifications and variations are intended to fall within the scope ofi the appended claims. The present invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims (18)

1. A method to determine if a patient is afflicted with ovarian cancer comprising:
a) obtaining a sample from the said patient;
b) determining the levels of gene expression of two or more of the genes listed in Table 9 in the sample from the patient;
c) comparing the levels of gene expression of the two or more genes determined in (b) to the levels of the same genes listed in Table 1;
d) determining the degree of similarity (DOS) between the levels of gene expression of the two or more genes determined in (c); and e) determining from the DOS between the level of gene expression of the two or more genes the probability that the sample shows evidence of the presence of ovarian cancer in the patient.

2. The method of Claim 1, wherein the levels of gene expression are determined for a subset of the genes listed in table comprising genes Nos. 1-28 in Table 9.

3. The method of Claim 1 or 2, wherein the sample comprises cells obtained from the patient.

4. The method of any one of Claims 1 to 3, wherein the sample comprises cells removed from a solid tumor in the said patient.

5. The method of any one of Claims 1 to 4, wherein the sample comprises blood cells and serum drawn from the said patient.

6. The method of any one of Claims 1 to 5, wherein the sample comprises a body fluid drawn from the patient.

7. The method of any one of Claims 1 to 6, wherein the method of determining the level of gene expression comprises measuring the levels of protein expression product in the sample from the patient.

8. The method of Claim 7, wherein the presence and level of the protein expression products are detected using a reagent which specifically binds with the proteins.

9. The method of Claim 7 or 8, wherein the reagent is selected from the group consisting of an antibody, an antibody derivative and an antibody fragment.

10. The method of any one of Claims 1 to 6, wherein the levels of expression in the sample are assessed by measuring the levels in the sample of the transcribed polynucleotides of the two or more gene in Table 9.

11. The method of Claim 10, wherein the transcribed polynucleotide is an mRNA.

12. The method of Claim 10 or 11, wherein the transcribed polynucleotide is a cDNA.

13. The method of any one of Claims 10 to 12, wherein the step of detecting further comprises amplifying the transcribed polynucleotide.

14. The method of any one of Claims 1 to 13, wherein the method is performed ex vivo.

15. A method of treating a subject afflicted with ovarian cancer, the method comprising providing to cells of the subject an antisense oligonuceotide complimentary to one or more of the genes whose expression is up-regulated in ovarian cancer as shown in Table 6.

16. A method of inhibiting ovarian cancer in a subject at risk for developing ovarian cancer, the method comprising inhibiting expression of one or more of the genes shown in Table 6 to be up-regulated in ovarian cancer.

17. A kit for use in determining treatment strategy for a patient with suspected ovarian cancer comprising:
a) two or more antibodies able to recognize and bind to the polypeptide expression product of the two or more of the genes in Table 9;
b) a container suitable for containing the said antibodies and a sample of body fluid from the said individual wherein the antibody can contact the polypeptide expressed by the two or more genes shown in Table 9 if they are present;
c) means to detect the combination of the said antibodies with the polypeptides expressed by the two or more genes shown in Table 9; and d) instructions for use and interpretation of the kit results.

18. A kit for use in determining the presence or absence of ovarian cancer in a patient comprising:
a) two or more polypeptides able to recognize and bind to the mRNA expression product of the two or more genes shown in Table 9;
b) a container suitable for containing the said polynucleotides and a sample of body fluid from the said individual wherein the said polynucleotide can contact the mRNA, if it is present;
c) means to detect the levels of combination of the said polynucleotide with the mRNA from the two or more genes shown in Table 9; and d) instructions for use and interpretation of the kit results.