CA2589055A1 - Methods for assessing patients with acute myeloid leukemia - Google Patents

Abstract

Methods for treating cancer, and preferably hematological malignancy, patients include analyzing gene expression profiles and/or molecular markers of a patient to determine status and/or prognosis of the patient. The invention also provides methods of analyzing whether a non-relapsed or non-refractory patient is likely to respond to treatment with farnesyl transferase inhibitors (FTIs) and, optionally, other therapeutics. The methods are also useful for monitoring patient therapy and for selecting a course of therapy. Genes modulated in response to FTI treatment are provided and are used in formulating the profiles.

Description

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TITLE OF THE INVENTION
METHODS FOR ASSESSING PATIENTS WITH ACUTE MYELOID LEUKEMIA
A "Sequence Listing" listing appendix is hereby incorporated by reference herein.
BACKGROUND OF THE INVENTION
This invention relates to diagnostics, prognostics, and treatments for acute myeloid leukemia (AML) based on the detection of molecular markers and/or gene expression analysis.
Karyotyping is currently effective in providing prognostic value while it also serves to identify biologically distinct subtypes of AML. In addition, mutations in genes such as FLT3, c-KIT, AML1, GATA1, CEBPA and N-RAS are implicated in the pathogenesis of the disease. It is clear- that screening for FLT3 and CEBPA
mutations can stratify groups that have different risks of relapse. Effective risk stratification can allow for the appropriate use of allogeneic stem cell transplantation or other adjuvant therapies. Two papers published recently describe gene expression profiling of newly diagnosed adult AML patients and its use in predicting clinical outcome. Bullinger et al. (2004); and Valk et al. (2004). These studies show how gene-expression profiling can further refine clinical outcome prediction.
Valk et al. (2004) evaluated 285 patients (bone marrow or peripheral blood) on the Affymetrix U133A chip. The patient samples encompassed a wide range of cytogenetic and molecular abnormalities. Only 16 clusters were identified indicating AML may not be as heterogeneous as previously thought. Several of the clusters corresponded well with the cytogenetically and molecularly defined sub-types of AML thus supporting their use in the WHO classification system. These clusters were also seen by Bullinger et al. (2004) and other previously published smaller studies. Schoch et al. (2002); Debemardi et al. (2003); and Kohlmann et al.
(2003). These clusters, not surprisingly, correlated with prognosis since they were associated with well known prognostic karyotypes.
Bullinger et al. (2004) investigated expression profiles from 116 adult patients (65 peripheral blood and 54 bone marrow) using cDNA arrays. In addition to the work done by Valk et al. (2004) they also developed a133 gene classifier for predicting clinical outcome across all cytogenetic risk groups. Using a training set of 59 samples and a testing set of 57 samples they showed that the 133 genes clustered patients into poorand good outcome.groups (p = 0.0061og rank; odds ratio, 10, 95% CI, 2.6-29.3).
Notably, the genes identified in both these studies overlap, only in part, to predictor genes previously identified in childhood leukemia. Yagi et al.
(2003).
Also, there is no overlap between the prognostic gene set identified by Bullinger et al. (2004) and the 3 genes recently identified that predict response to tipifarnib. US
patent application serial no. 10/883,436.
The farnesyl transferase (FTase) enzyme mediates the covalent attachment of a carbon farnesyl moiety to the C-terminal CAAX (C, cysteine; A, aliphatic residue;
X, any amino acid) recognition motif. Reiss et al. (1990). This farnesylation is further processed by cleavage of the 3 terminal amino acids (AAX) and methylation of the C-terminal isoprenyl-cysteine. The inhibition of protein famesylation abrogates the correct subcellular localization required for protein function.
Originally, the oncogenic Ras protein was thought to be the target for the antiproliferative effects of FTIs in cancer biology. Reuter et al. (2000).
However, it has since been shown that inhibition of Ras farnesylation does not account for all of actions of tipifarnib. For example, FTIs do not always require the presence of mutant Ras protein to produce antitumor effects. Karp et al. (2001). Indeed, while early clinical studies were designed around populations with a high frequency of ras mutations, such as advanced colorectal and pancreatic cancer, no significant difference in response rates were seen when compared to placebo. Van Cutsem et al. (2004); and Rao et al. (2004).
Several other famesylated proteins have been implicated as candidate targets that may mediate the antitumorigenic effects of FTIs including the small GTPase proteins Rho B, the centromere proteins CENP-E and CENP-F, the protein tyrosine phosphatase PTP-CAAX, and the nuclear membrane structural lamins A and B. The inhibition of famesylation of these proteins may lead to the antiproliferative effect of FTIs and also indirectly modulate several important signaling molecules including TGFORII, MAPK/ERK, PI3K/AKT2, Fas (CD95), NF-KB, and VEGF. Adnane et al. (2000); Morgan et al. (2001); Jiang et al. (2000); Na et al. (2004);
Takada et al.
(2004); and Zhang et al. (2002). Regulation of these signaling pathways leads to the modulation of cell growth, proliferation, and apoptosis. Thus, FTIs may have complex inhibitory effects on several cellular events and pathways.

There are currently no methods for determining status or predicting overall survival of these patients.
BRIEF SUMMARY OF THE INVENTION
The invention is a method of using one or more gene signatures for predicting prognosis in patients with acute myeloid leukemia (AML). These signatures can be used alone or in combination depending upon the type of drug treatment.
The present invention provides a method of assessing acute myeloid leukemia (AML) status by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are indicative of AML status.
The present invention provides a method of staging acute myeloid leukemia (AML) patients by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are indicative of AML survival.
The present invention provides a method of determining acute myeloid leukemia (AML) patient treatment protocol by obtaining a biological sample from an AML
patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are sufficiently indicative of response to therapy to enable a physician to determine the degree and type of therapy recommended to provide appropriate therapy.
The present invention provides a method of treating a acute myeloid leukemia (AML) patient by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are indicate a response to therapy and; treating the patient with adjuvant therapy if they have a responder profile.
The present invention provides a method of determining whether a acute myeloid leukemia (AML) patient is high or low risk of mortality by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3 where the expression levels of the Marker genes above or below pre-determined cut-off levels are sufficiently indicative of risk of mortality to enable a physician to determine the degree and type of therapy recommended.
The present invention provides a method of generating an acute myeloid leukemia (AML) prognostic patient report by determining the results of any one of the above-described methods; and preparing a report displaying the results and reports generated thereby.
The present invention provides a kit for conducting an assay to determine acute myeloid leukemia (AML) prognosis in a biological sample comprising: materials for detecting isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.
The present invention provides articles for assessing acute myeloid leukemia (AML) status comprising: materials for detecting isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.
The present invention provides a microarray or gene chip for performing the above-described methods.
The present invention provides a diagnostic/prognostic portfolio comprising isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.
BRIEF DESCRIPTON OF DRAWINGS
Figure 1. Unsupervised clustering of relapsed and refractory AML patients. The dendogram shows the unsupervised k-means clustering of 58 relapsed or refractory AML patients, where each column represents a patient and each row represents a gene. The expression ratio for each gene was calculated by dividing the expression level of that gene in a patient by the mean of all other patients. The color bar indicates the fold-change (log2). Red is upregulated, blue is down-regulated.
White indicates no change. The presence of 6 main clusters is shown.
Figure 2. Real-time RT-PCR of 2 genes. AHR and AKAP13 were measured by real-time RT-PCR. The HPRT or PBGD control genes were used to normalize gene 5 expression values. Error bars are standard deviations. The resulting values were plotted against the corresponding microarray data and linear regression analysis was performed.
Figure 3 depicts the predictive value of the AKAP 13 gene. Panel A shows a 2x2 table generated from a LOOCV performed using AKAP 13 expression as a classifier on the responders (R) and non-responders (NR). Panel B shows the AKAP 13 expression values for the same 58 patients. The P value indicates a significant difference in the gene expression between the mean values of each response group.
Panel C shows the Kaplan-Meier curves generated from patients classified by the AKAP13 gene as being responders and non-responders.
Figure 4 provides identification of a minimal set of predictive markers. In Panel A, a LOOCV was performed using a sensitivity of 100%. Independent classifiers were tested that contained from 1 to 19 genes. The resulting error rate is plotted.
Panel B shows a 2x2 table generated from a LOOCV performed using the 3-gene signature as a classifier on the responders (R) and non-responders (NR). Panel C
shows the scores generated from the 3-gene classifier. The P value indicates a significant difference in the gene expression between the response groups.
Panel D
is the Kaplan-Meier curves generated from patients classified by the 3-gene signature as being responders and non-responders. Median survival times are also indicated.
Figure 5. A Kaplan-Meier analysis was performed on patients classified by the 3-gene signature as being predicted responders and non-responders. The survival curve of patients who were clinically defined as non-responders but classified as responders using the 3-gene signature is shown. Median survival times are also indicated.
Figure 6 depicts over-expression of AKAP 13 in an AML cell line. Cell counts were normalized to cultures with no drug (indicated at -12 log units) to give a percentage of control. Error bars indicate standard errors of the mean. Open data points indicate results from a second experiment exploring higher concentrations of drug.

Figure 7 provides a model of FTI action in relapsed or refractory AML. A. In responders the IL3RA and AKAP13 genes are lowly expressed allowing for down-regulation of the ras, and RhoA, and lamin B pathways, respectively.
Up-regulation of RhoH leads to increased inhibition of cellular transformation 5 pathways. Together this allows for greater efficacy in FTI
antitumorigenicity. B.

The opposite expression profile is seen in non-responders allowing for the expression of compensatory pathways.
Figure 8. The Zarnestra predictive gene signature has superior utility to an independent prognostic gene signature. In panel A columns represent AML
samples from relapsed or refractory patients and rows represent 167 probe sets that correspond to 103 of the 133 prognostic genes identified by Bullinger et al.
ordered according to hierarchical clustering. Panel B shows Kaplan-Meier survival estimates of the cluster-defined groups of patients. In panel C the 3-gene classifier has been used to identify responders of tipifarnib in the good and poor prognostic groups defined by the Bullinger signature. Kaplan-Meier survival curves are shown for patients identified as being responders to tipifarnib in the good (Zn+.clusterl) and poor (Zn+.cluster2) prognostic groups. The median survival times for each group are indicated.
Figure 9 is a flow chart depicting how the genes from Bullinger et al. (2004) were matched to 167 probe sets (103 unique genes) on the Affymetrix U133A
chip.
Figure 10 shows the utility of the 167 probe set signature in relapsed or refractory AML patients. In panel A columns represent AML samples from relapsed or refractory patients and rows represent 167 probe sets that correspond to 103 of the 133 prognostic genes identified by Bullinger et al. (2004), ordered according to hierarchical clustering. Panel B shows Kaplan-Meier survival estimates of the cluster-defined groups of patients.
Figure 11 provides comparisons of prognostic and Zamestra predictive gene signatures. Panel A shows the Kaplan-Meier survival curves for the good and poor prognostic clusters as defined by the subset of 103 Bullinger et al. (2004) genes.
Panel B shows the Kaplan-Meier survival curves for the good and poor prognostic clusters as defined by the 3-gene signature that predicts response to Zarnestra. Panel C shows the Kaplan-Meier survival curves for the good and poor prognostic clusters from Panel A further stratified by the 3-gene Zamestra signature. Panel D
shows the Kaplan-Meier survival curves for patients who are predicted to have a poor prognosis and not respond to Zarnestra versus the remainder of patients.
Figure 1.2 Identification of a minimal set of predictive markers. a) A LOOCV
was performed selecting for genes with a sensitivity of 100%, specificity of 40% and fold-change > 2. Independent classifiers were tested that contained from 1 to genes ranked by the AUC . The resulting error rate is plotted. b) A 2x2 table generated from a LOOCV performed using AKAP 13 as a classifier on the responders (R) and non-responders (NR). c) The gene-expression values of AKAP 13. The P value indicates a significant difference in the gene expression between the response groups. d) The Kaplan-Meier curves generated from patients classified by AKAP 13 as being responders and non-responders. Median survival times are also indicated.
Figure 13 depicts an overview of gene expression analysis.
Figure 14 depicts AML samples maintain FTI-mediated global gene expression changes following termination of tipifarnib treatment.
Figure 15 depicts predictive expression profiles and testing of predictive classifiers in newly diagnosed AML.
Figure 16 depicts the 6-gene classifier stratifies newly diagnosed AML.
DETAILED DESCRIPTION OF THE INVENTION
A subset of genes previously described to have prognostic value in newly diagnosed AML is shown here to have utility in relapsed and refractory AML
patients treated with a molecularly targeted therapy (Zarnestra). Currently there is no method for predicting response to farnesyl transferase inhibitors (such as Zarnestra). Also, current methods for understanding the prognosis of patients with AML is limited to histological subtype and karyotyping, both of which are not ideal markers for determining clinical outcome. The current signatures expand upon these traditional technologies by providing better stratification of prognostic high risk and low risk patients.
US Patent Application Serial No. 10/883,436 demonstrates that a 3-gene classifier (including AHR, AKAP13 and MINA53) predicts relapsed, refractory AML patient response to tipifarnib (Zarnestra , Rl 15777) with the lowest error rate.
This was also seen when a leave-five-out cross validation was performed. When more genes were added the error rate increased indicating that additional genes introduced noise to the classifier. For the 3-gene classifier the LOOCV
demonstrated a sensitivity of 86% and specificity of 70% with an overall diagnostic accuracy of 74%. Kaplan-Meier analysis again showed a significant difference in survival between the predicted responder group and the non-responder group.
Moreover, comparing the incorrectly classified non-responders to the correctly classified non-responders, the misclassified non-responders showed a better overall survival.
Zarnestra is an orally available non-peptidomimetic competitive farnesyl transferase inhibitor (FTI) that has been shown to inhibit the proliferation of a variety of human tumor cell lines both in vitro and in vivo. End et al.
(2001); and Cox et al. (2002). A phase I clinical trial of tipifarnib demonstrated a 32%
response rate in patients with refractory or relapsed acute myeloid leukemia. Karp et al.
(2001). Activity has also been seen in early clinical trials for myelodysplastic syndrome (MDS) (Kurzrock et al. (2004)), multiple myeloma (MM) (Alsina et al.
(2003)) and chronic myeloid leukemia (CML). Cortes et al. (2003). Complete remission was defined as less than 5% bone marrow blasts with a neutrophil count greater than 1000/ L, a platelet count less than 100,000/ L, and no extramedullary disease. While it is clear that FTIs function by inhibiting protein farnesylation, it is still not known what genes are implicated in the antitumor effects of tipifarnib in hematopoietic malignancies. Microarray technology allows for the measurement of the steady-state mRNA level of thousands of genes simultaneously, thereby representing a powerful tool for identifying genes and gene pathways that correlate with FTI action. Global gene expression monitoring was therefore employed in a phase 2 clinical study of tipifarnib in relapsed and refractory AML to identify genes that predict response to this FTI in hematologic malignancies.
The mere presence of nucleic acid sequences having the potential to express proteins or peptides ("genes") within the genome is not determinative of whether a protein or peptide is expressed in a given cell. Whether or not a given gene capable of expressing proteins or peptides or transcribing RNA does so and to what extent such expression or transcription occurs, if at all, is determined by a variety of complex factors. Nevertheless, assaying gene expression can provide useful information about the cellular response to a given stimulus such as the introduction of a drug or other therapeutic agent. Relative indications of the degree to which genes are active or inactive can be found in such gene expression profiles. In some instances, the presence of a molecular marker can, by itself or with the use of gene expression information, provide useful information about treatment efficacy too.
The gene expression profiles and molecular markers of this invention are used to identify and treat AML patients.
Cancers, including hematological malignancies, typically arise from mutations in a variety of genes. The same type of cancer may arise as a result of, or coincident with, one or more mutations that differ from those of another patient having the same type of cancer. The fact that there are often multiple molecular bases underlying the same cancers is consistent with the observation that some therapies that affect one patient do not necessarily equally affect another patient with the same type of cancer. Further, from a diagnostic point of view, the presence of particular mutations such as translocations, deletions, or SNPs can have powerful implications.
In some instances, such molecular markers are themselves useful indicators for diagnosis, prognosis, or treatment response determinations. This is particularly true where the molecular mutations can be associated with response to particular treatments.
A Biomarker is any indicia of the level of expression of an indicated Marker gene. The indicia can be direct or indirect and measure over- or under-expression of the gene given the physiologic parameters and in comparison to an internal control, normal tissue or another carcinoma. Biomarkers include, without limitation, nucleic acids (both over and under-expression and direct and indirect). Using nucleic acids as Biomarkers can include any method known in the art including, without limitation, measuring DNA amplification, RNA, micro RNA, loss of heterozygosity (LOH), single nucleotide polymorphisms (SNPs, Brookes (1999)), microsatellite DNA, DNA hypo- or hyper-methylation. Using proteins as Biomarkers can include any method known in the art including, without limitation, measuring amount, activity, modifications such as glycosylation, phosphorylation, ADP-ribosylation, ubiquitination, etc., imunohistochemistry (IHC). Other Biomarkers include imaging, cell count and apoptosis markers.
The indicated genes provided herein are those associated with a particular tumor or tissue type. A Marker gene may be associated with numerous cancer types but provided that the expression of the gene is sufficiently associated with one tumor or tissue type to be identified using the algorithm described herein to be specific for a lung cancer cell, the gene can be used in the claimed invention to determine cancer status and prognosis. Numerous genes associated with one or more cancers are known in the art. The present invention provides preferred Marker genes and even more preferred Marker gene combinations. These are described herein in detail.
5 A Marker gene corresponds to the sequence designated by a SEQ ID NO when it contains that sequence. A gene segment or fragment corresponds to the sequence of such gene when it contains a portion of the referenced sequence or its complement sufficient to distinguish it as being the sequence of the gene. A gene expression product corresponds to such sequence when its RNA, mRNA, or cDNA hybridizes 10 to the composition having such sequence (e.g. a probe) or, in the case of a peptide or protein, it is encoded by such mRNA. A segment or fragment of a gene expression product corresponds to the sequence of such gene or gene expression product when it contains a portion of the referenced gene expression product or its complement sufficient to distinguish it as being the sequence of the gene or gene expression product.
The inventive methods, compositions, articles, and kits of described and claimed in this specification include one or more Marker genes. "Marker" or "Marker gene"
is used throughout this specification to refer to genes and gene expression products that correspond with any gene the over- or under-expression of which is associated with a tumor or tissue type. The preferred Marker genes are described in more detail in Table 8.
The present invention provides a method of assessing acute myeloid leukemia (AML) status by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are indicative of AML status.
The present invention provides a method of staging acute myeloid leukemia (AML) patients by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are indicative of AML survival.

The present invention provides a method of determining acute myeloid leukemia (AML) patient treatment protocol by obtaining a biological sample from an AML
patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are sufficiently indicative of response to therapy to enable a physician to determine the degree and type of therapy recommended to provide appropriate therapy.
The present invention provides a method of treating a acute myeloid leukemia (AML) patient by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 where the expression levels of the Marker genes above or below pre-determined cut-off levels are indicate a response to therapy and; treating the patient with adjuvant therapy if they have a responder profile.
The present invention provides a method of determining whether a acute myeloid leukemia (AML) patient is high or low risk of mortality by obtaining a biological sample from an AML patient; and measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3 where the expression levels of the Marker genes above or below pre-determined cut-off levels are sufficiently indicative of risk of mortality to enable a physician to determine the degree and type of therapy recommended.
The method provided herein may further include, contain or utilize measuring the expression level of at least one gene constitutively expressed in the sample.
Preferably, the method provided herein results in a specificity of at least about 40%.
Preferably, the method provided herein results in a sensitivity of at least at least about 80%. Preferably, the method provided herein results in a p-value of less than 0.05.
The method provided herein may be performed by measuring gene expression on a microarray or gene chip. The microarray can be a cDNA array or an oligonucleotide array and may further contain one or more internal control reagents.
The method provided herein may be performed by determining gene expression by nucleic acid amplification conducted by polymerase chain reaction (PCR) of RNA extracted from the sample. The PCR can be reverse transcription polymerase chain reaction (RT-PCR) and can further contain one or more internal control reagent.
The method provided herein may be performed by measuring or detecting a protein encoded by the gene. Te protein can be detected by an antibody specific to the protein.
The method provided herein may be performed by measuring a characteristic of the gene. Characteristics include, without limitation, DNA amplification, methylation, mutation and allelic variation.
The present invention provides a method of generating an acute myeloid leukemia (AML) prognostic patient report by determining the results of any one of the above-described methods; and preparing a report displaying the results and reports generated thereby. The report may contain an assessment of patient outcome and/or probability of risk relative to the patient population and/or likelihood or response to chemotherapy.
The present invention provides a kit for conducting an assay to determine acute myeloid leukemia (AML) prognosis in a biological sample comprising: materials for detecting isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9. The kit can further contain reagents for conducting a microarray analysis and/or a medium through which said nucleic acid sequences, their complements, or portions thereof are assayed.
The present invention provides articles for assessing acute myeloid leukemia (AML) status containing materials for detecting isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9. The articles can further contain reagents for conducting a microarray analysis and/or a medium through which said nucleic acid sequences, their complements, or portions thereof are assayed.
The present invention provides a microarray or gene chip for performing the above-described methods. The microarray may contain isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9. Preferably, the microarray provides a measurement or characterization at least 1.5-fold over- or under-expression. Preferably, the microarray provides a measurement with a statistically significant p-value over- or under-expression. More preferably, the p-value is less than 0.05. The microarray can be any known in the art including, without limitation, cDNA array or an oligonucleotide array and can further contain internal control reagents.
The present invention provides a diagnostic/prognostic portfolio comprising isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9. Preferably, the measurement or characterization is at least 1.5-fold over- or under-expression. Preferably, the measurement provides a statistically significant p-value over- or under-expression. More preferably, the p-value is less than 0.05.
Preferred methods for establishing gene expression profiles include determining the amount of RNA that is produced by a gene that can code for a protein or peptide.
This is accomplished by reverse transcriptase PCR (RT-PCR), competitive RT-PCR, real time RT-PCR, differential display RT-PCR, Northern Blot analysis and other related tests. While it is possible to conduct these techniques using individual PCR
reactions, it is best to amplify complementary DNA (cDNA) or complementary RNA (cRNA) produced from mRNA and analyze it via microarray. A number of different array configurations and methods for their production are known to those of skill in the art and are described in U.S. Patents such as: 5,445,934;
5,532,128;
5,556,752; 5,242,974; 5,384,261; 5,405,783; 5,412,087; 5,424,186; 5,429,807;
5,436,327; 5,472,672; 5,527,681; 5,529,756; 5,545,531; 5,554,501; 5,561,071;
5,571,639; 5,593,839; 5,599,695; 5,624,711; 5,658,734; and 5,700,637.
Microarray technology allows for the measurement of the steady-state mRNA
level of thousands of genes simultaneously thereby presenting a powerful tool for identifying effects such as the onset, arrest, or modulation of uncontrolled cell proliferation. Two microarray technologies are currently in wide use. The first are cDNA arrays and the second are oligonucleotide arrays. Although differences exist in the construction of these chips, essentially all downstream data analysis and output are the same. The product of these analyses are typically measurements of the intensity of the signal received from a labeled probe used to detect a cDNA
sequence from the sample that hybridizes to a nucleic acid sequence at a known location on the microarray. Typically, the intensity of the signal is proportional to the quantity of cDNA, and thus mRNA, expressed in the sample cells. A large number of such techniques are available and useful. Preferred methods for determining gene expression can be found in US Patents 6,271,002; 6,218,122;
6,218,114; and 6,004,755.
Analysis of the expression levels is conducted by comparing such signal intensities. This is best done by generating a ratio matrix of the expression intensities of genes in a test sample versus those in a control sample. For instance, the gene expression intensities from a diseased tissue can be compared with the expression intensities generated from benign or normal tissue of the same type. A
ratio of these expression intensities indicates the fold-change in gene expression between the test and control samples.
Gene expression profiles can also be displayed in a number of ways. The most common method is to arrange raw fluorescence intensities or ratio matrix into a graphical dendogram where columns indicate test samples and rows indicate genes.
The data are arranged so genes that have similar expression profiles are proximal to each other. The expression ratio for each gene is visualized as a color. For example, a ratio less than one (down-regulation) may appear in the blue portion of the spectrum while a ratio greater than one (indicating up-regulation) may appear as a color in the red portion of the spectrum. Commercially available computer software programs are available to display such data including "GENESPRING" from Silicon Genetics, Inc. and "DISCOVERY" and "INFER" software from Partek, Inc.
In the case of measuring protein levels to determine gene expression, any method known in the art is suitable provided it results in adequate specificity and sensitivity. For example, protein levels can be measured by binding to an antibody or antibody fragment specific for the protein and measuring the amount of antibody-bound protein. Antibodies can be labeled by radioactive, fluorescent or other detectable reagents to facilitate detection. Methods of detection include, without limitation, enzyme-linked immunosorbent assay (ELISA) and immunoblot techniques.
Modulated Markers used in the methods of the invention are described in the Examples. The genes that are differentially expressed are either up regulated or down regulated in patients with various lung cancer prognostics. Up regulation and down regulation are relative terms meaning that a detectable difference (beyond the contribution of noise in the system used to measure it) is found in the amount of expression of the genes relative to some baseline. In this case, the baseline is determined based on the algorithm. The genes of interest in the diseased cells are then either up- or down-regulated relative to the baseline level using the same 5 measurement method.
Assays for the gene expression status of a cell also can determine normal/abnormal tissue distribution for diagnostic purposes using techniques such as immunohistochemical analysis (IHC). Any method known in the art can be used, for example in the case of the LBC oncogene, the antibodies to LBC protein may be used 10 in conjunction with both fresh-frozen and formalin-fixed, paraffin-embedded tissue blocks prepared for study by IHC. Each tissue block may consist of 50 mg of residual "pulverized" tumor.
Briefly, frozen-sections may be prepared by rehydrating 50 ng of frozen pulverized tumor at room temperature in phosphate buffered saline (PBS) in small 15 plastic capsules; pelleting the particles by centrifugation; resuspending them in a viscous embedding medium (OCT); inverting the capsule and pelleting again by centrifugation; snap-freezing in -70 C isopentane; cutting the plastic capsule and removing the frozen cylinder of tissue; securing the tissue cylinder on a cryostat microtome chuck; and cutting 25-50 serial sections containing intact tumor cells.
Permanent-sections may be prepared by a similar method involving rehydration of the 50 mg sample in a plastic microfuge tube; pelleting; resuspending in 10%
formalin for 4 hr fixation; washing/pelleting; resuspending in warm 2.5% agar;
pelleting; cooling in ice water to harden the agar; removing the tissue/agar block from the tube; infiltrating and embedding the block in paraffin; and cutting up to 50 serial permanent sections.
For the IHC assay, the sections are overlaid with a blocking solution containing:
3% bovine serum albumin (BSA) in PBS or other blocking reagents. The blocking reagents include non-specific serum or dry milk. Blocking is allowed to proceed for 1 hr at room temperature. Anti-LBC protein antibody is diluted with PBS buffer containing 3% BSA, 0.1% TritonXTM-100 and t-octylphenoxypolyethoxyethanol, at a ratio of 1:100. The sample sections are generally overlaid with the antibody solution for 16 hr at 4 C. The duration and temperature conditions may be varied according to the antibody selected and the material tested. The optimal conditions are determined empirically. The antibody treated sections are then washed three times in PBS
for 15 min, each to remove unbound antibody and then overlaid with PBS containing 3%
BSA and a secondary antibody at a dilution of 1:2000. The secondary antibodies may be coupled to a chromogenic enzyme such as: horseradish peroxidase, alkaline phosphatase, fluorescein isothiocyanate, or other suitable enzymes.
Alternatively, the secondary antibody may be conjugated to biotin and used in conjunction with chromophore-labeled avidin.
Another exemplary method for detecting the presence of a gene is via in situ hybridization. Generally, in situ hybridization comprises the following major steps:
(1) fixation of tissue or biological structure to be analyzed; (2) prehybridization treatment of the biological structure to increase accessibility of target DNA, and to reduce nonspecific binding; (3) hybridization of the mixture of nucleic acids to the nucleic acid in the biological structure or tissue; (4) post-hybridization washes to remove nucleic acid fragments not bound in the hybridization and (5) detection of the hybridized nucleic acid fragments. The reagent used in each of these steps and the conditions for use vary depending on the particular application.
In this case, a hybridization solution comprising at least one detectable nucleic acid probe capable of hybridizing to a gene (at its chromosomal locus) is contacted with the cell under hybridization conditions. Any hybridization is then detected and compared to a predetermined hybridization pattern from normal or control cells.
Preferably, the probes are alpha-centromeric probes. Such probes can be made commercially available from a number of sources (e.g., from Visys Inc., Downers Grove, IL). In a preferred embodiment, the hybridization solution contains a multiplicity of probes, specific for an area on the chromosome that corresponds to the translocation of the sequences that make up the chimera (e.g., 15q24-25).
Hybridization protocols suitable for use with the methods of the invention are described, e.g., in Albertson (1984); Pinkel (1988); EP No. 430,402; and Methods in Molecular Biology, Vol. 33: In Situ Hybridization Protocols, Choo, ed., Humana Press, Totowa, NJ (1994), etc. In one particularly preferred embodiment, the hybridization protocol of Pinkel et al. (1998) or of Kallioniemi (1992) is used.
Methods of optimizing hybridization conditions are well known (see, e.g., Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology, Vol. 24:
Hybridization With Nucleic Acid Probes, Elsevier, NY).
In a preferred embodiment, background signal is reduced by the use of a detergent (e.g., C-TAB) or a blocking reagent (e.g., sperm DNA, cot-1 DNA, etc.) during the hybridization to reduce non-specific binding. Preferably, the hybridization is performed in the presence of about 0.1 to about 0.5 mg/ml DNA (e.g., cot-1 DNA).
The probes may be prepared by any method known in the art, including synthetically or grown in a biological host. Synthetic methods include but are not limited to oligonucleotide synthesis, riboprobes, and PCR.
The probe may be labeled with a detectable marker by any method known in the art. Methods for labeling probes include random priming, end labeling, PCR and nick translation. Enzymatic labeling is conducted in the presence of nucleic acid polymerase, three unlabeled nucleotides, and a fourth nucleotide which is either directly labeled, contains a linker arm for attaching a label, or is attached to a hapten or other molecule to which a labeled binding molecule may bind. Suitable direct labels include radioactive labels such as "P, 3H, and 35S and non-radioactive labels such as fluorescent markers, such as fluorescein, Texas Red, AMCA blue, lucifer yellow, rhodamine, and the like; cyanin dyes which are detectable with visible light;
enzymes and the like. Labels may also be incorporated chemically into DNA
probes by bisulfite-mediated transamination or directly during oligonucleotide synthesis.
Fluorescent markers can readily be attached to nucleotides with activated linker arms incorporated into the probe. Probes may be indirectly labeled by the methods disclosed above, by incorporating a nucleotide covalently linked to a hapten or other molecule such as biotin or digoxygenin, and performing a sandwich hybridization with a labeled antibody directed to that hapten or other molecule, or in the case of biotin, with avidin conjugated to a detectable label. Antibodies and avidin may be conjugated with a fluorescent marker, or with an enzymatic marker such as alkaline phosphatase or horseradish peroxidase to render them detectable. Conjugated avidin and antibodies are commercially available from companies such as Vector Laboratories (Burlingame, CA) and Boehringer Mannheim (Indianapolis, IN).
The enzyme can be detected through a colorimetric reaction by providing a substrate for the enzyme. In the presence of various substrates, different colors are produced by the reaction, and these colors can be visualized to separately detect multiple probes. Any substrate known in the art may be used. Preferred substrates for alkaline phosphatase include 5-bromo-4-chloro-3-indolylphosphate (BCIP) and nitro blue tetrazolium (NBT). The preferred substrate for horseradish peroxidase is diaminobenzoate (DAB).

Fluorescently labeled probes suitable for use in the in situ hybridization methods of the invention are preferably in the range of 150-500 nucleotides long.
Probes may be DNA or RNA, preferably DNA.
Hybridization of the detectable probes to the cells is conducted with a probe concentration of 0.1-500 ng/ L, preferably 5-250 ng/ L. The hybridization mixture will preferably contain a denaturing agent such as formamide. In general, hybridization is carried out at 25 C-45 C, more preferably at 32 C-40 C, and most preferably at 37 C-38 C. The time required for hybridization is about 0.25-96 hours, more preferably 1-72 hours, and most preferably for 4-24 hours. Hybridization time will vary based on probe concentration and hybridization solution content which may contain accelerators such as hnRNP binding protein, trialkyl ammonium salts, lactams, and the like. Slides are then washed with solutions containing a denaturing agent, such as formamide, and decreasing concentrations of sodium chloride or in any solution that removes unbound and mismatched probe.
The temperature and concentration of salt will vary depending on the stringency of hybridization desired. For example, high stringency washes may be carried out at 42 C-68 C, while intermediate stringency may be in the range of 37 C-55 C, and low stringency may be in the range of 30 C-37 C. Salt concentration for a high stringency wash may be 0.5-1 times SSC (0.15M NaC1, 0.015M Na citrate), while medium stringency may be 1-4 times, and low stringency may be 2-6 times SSC.
The detection incubation steps, if required, should preferably be carried out in a moist chamber at 23 C-42 C, more preferably at 25 C-38 C and most preferably at 37-38 C. Labeled reagents should preferably be diluted in a solution containing a blocking reagent, such as BSA, non-fat dry milk, or the like. Dilutions may range from 1:10-1:10,000, more preferably 1:50-1:5,000, and most preferably at 1:100-1:1,000. The slides or other solid support should be washed between each incubation step to remove excess reagent.
Slides may then be mounted and analyzed by microscopy in the case of a visible detectable marker, or by exposure to autoradiographic film in the case of a radioactive marker. In the case of a fluorescent marker, slides are preferably mounted in a solution that contains an antifade reagent, and analyzed using a fluorescence microscope. Multiple nuclei may be examined for increased accuracy of detection.
Additionally, assays for the expression product of the LBC oncogene can also be used to determine whether the LBC oncogene mutation has occurred. Most preferably, such assays are immunoassays. Immunoassays, in their most simple and direct sense, are binding assays. Certain preferred immunoassays are the various types of enzyme linked immunosorbent assays (ELISAs) and radioimmunoassays (RIA) known in the art. IHC detection using tissue sections is also particularly useful as are in situ hybridization and enzyme immunoassay.
In one exemplary ELISA, protein-specific antibodies are immobilized onto a selected surface exhibiting protein affinity, such as a well in a polystyrene microtiter plate. Then, a test composition containing the desired antigen, such as a clinical sample, is added to the wells. After binding and washing to remove non-specifically bound immune complexes, the bound antigen may be detected. Detection is generally achieved by the addition of another antibody, specific for the desired antigen, that is linked to a detectable label. This type of ELISA is a simple "sandwich ELISA."
Detection may also be achieved by the addition of a second antibody specific for the desired antigen, followed by the addition of a third antibody that has binding affinity for the second antibody, with the third antibody being linked to a detectable label.
Variations of ELISA techniques are well known. In one such variation, the samples containing the desired antigen are immobilized onto the well surface and then contacted with the antibodies of the invention. After binding and appropriate washing, the bound immune complexes are detected. Where the initial antigen specific antibodies are linked to a detectable label, the immune complexes may be detected directly. Again, the immune complexes may be detected using a second antibody that has binding affinity for the first antigen specific antibody, with the second antibody being linked to a detectable label.
In embodiments of the invention in which gene expression is detected for determining AML prognosis or status, the use of gene expression portfolios is most preferred. A portfolio of genes is a set of genes grouped so that expression information obtained about them provides the basis for making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice. In this case, gene expression portfolios can be fashioned to help make therapeutic decisions regarding AML patients.
Diseased, in this context, refers to an alteration of the state of a body that interrupts or disturbs, or has the potential to disturb, proper performance of bodily functions as occurs with the uncontrolled proliferation of cells. Someone is diagnosed with a disease when some aspect of that person's genotype or phenotype is consistent with the presence of the disease. However, the act of conducting a diagnosis or prognosis may include the determination of disease/status issues such as determining the likelihood of relapse, type of therapy and therapy monitoring. In therapy monitoring, clinical judgments are made regarding the effect of a given 5 course of therapy by comparing the expression of genes over time to determine whether the gene expression profiles have changed or are changing to patterns more consistent with normal tissue.
Genes can be grouped so that information obtained about the set of genes in the group provides a sound basis for making a clinically relevant judgment such as a 10 diagnosis, prognosis, or treatment choice. These sets of genes make up the portfolios of the invention. As with most diagnostic markers, it is often desirable to use the fewest number of markers sufficient to make a correct medical judgment.
This prevents a delay in treatment pending further analysis as well unproductive use of time and resources.
15 One method of establishing gene expression portfolios is through the use of optimization algorithms such as the mean variance algorithm widely used in establishing stock portfolios. This method is described in detail in US patent publication number 20030194734. Essentially, the method calls for the establishment of a set of inputs (stocks in financial applications, expression as 20 measured by intensity here) that will optimize the return (e.g., signal that is generated) one receives for using it while minimizing the variability of the return.
Many commercial software programs are available to conduct such operations.
"Wagner Associates Mean-Variance Optimization Application," referred to as "Wagner Software" throughout this specification, is preferred. This software uses functions from the "Wagner Associates Mean-Variance Optimization Library" to determine an efficient frontier and optimal portfolios in the Markowitz sense is one option. Use of this type of software requires that microarray data be transformed so that it can be treated as an input in the way stock return and risk measurements are used when the software is used for its intended financial analysis purposes.
The process of selecting a portfolio can also include the application of heuristic rules. Preferably, such rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to output from the optimization method. For example, the mean variance method of portfolio selection can be applied to microarray data for a number of genes differentially expressed in subjects with cancer. Output from the method would be an optimized set of genes that could include some genes that are expressed in peripheral blood as well as in diseased tissue. If samples used in the testing method are obtained from peripheral blood and certain genes differentially expressed in instances of cancer could also be differentially expressed in peripheral blood, then a heuristic rule can be applied in which a portfolio is selected from the efficient frontier excluding those that are differentially expressed in peripheral blood. Of course, the rule can be applied prior to the formation of the efficient frontier by, for example, applying the rule during data pre-selection.
Other heuristic rules can be applied that are not necessarily related to the biology in question. For example, one can apply a rule that only a prescribed percentage of the portfolio can be represented by a particular gene or group of genes.
Commercially available software such as the Wagner Software readily accommodates these types of heuristics. This can be useful, for example, when factors other than accuracy and precision (e.g., anticipated licensing fees) have an impact on the desirability of including one or more genes.
The gene expression profiles of this invention can also be used in conjunction with other non-genetic diagnostic methods useful in cancer diagnosis, prognosis, or treatment monitoring. For example, in some circumstances it is beneficial to combine the diagnostic power of the gene expression based methods described above with data from conventional markers such as serum protein markers (e.g., Cancer Antigen 27.29 ("CA 27.29")). A range of such markers exists including such analytes as CA 27.29. In one such method, blood is periodically taken from a treated patient and then subjected to an enzyme immunoassay for one of the serum markers described above. When the concentration of the marker suggests the return of tumors or failure of therapy, a sample source amenable to gene expression analysis is taken. Where a suspicious mass exists, a fine needle aspirate (FNA) is taken and gene expression profiles of cells taken from the mass are then analyzed as described above. Alternatively, tissue samples may be taken from areas adjacent to the tissue from which a tumor was previously removed. This approach can be particularly useful when other testing produces ambiguous results.
Kits made according to the invention include formatted assays for determining the gene expression profiles. These can include all or some of the materials needed to conduct the assays such as reagents and instructions and a medium through which Biomarkers are assayed.
Preferred methods for establishing gene expression profiles (including those used to arrive at the explication of the relevant biological pathways) include determining the amount of RNA that is produced by a gene that can code for a protein or peptide or transcribe RNA. This is best accomplished by reverse transcription PCR (RT-PCR), competitive RT-PCR, real time RT-PCR, differential display RT-PCR, Northern Blot analysis and other related tests. While it is possible to conduct these techniques using individual PCR reactions, it is often desirable to amplify copy DNA
(cDNA) or copy RNA (cRNA) produced from mRNA and analyze it via microarray.
A number of different array configurations and production methods are known to those of skill in the art and are described in US Patents such as: 5,445,934;
5,532,128;
5,556,752; 5,242,974; 5,384,261; 5,405,783; 5,412,087; 5,424,186; 5,429,807;
5,436,327; 5,472,672; 5,527,681; 5,529,756; 5,545,531; 5,554,501; 5,561,071;
5,571,639; 5,593,839; 5,599,695; 5,624,711; 5,658,734; and 5,700,637.
Microarray technology measures steady-state mRNA levels of thousands of genes "
simultaneously thereby presenting a powerful tool for identifying AML patient gene expression profiles. Two microarray technologies are currently in wide use.
The first are cDNA arrays and the second are oligonucleotide arrays. Although differences exist in the construction of these chips, essentially all downstream data analysis and output are the same. The products of these analyses are typically measurements of the intensity of the signal received from a labeled probe used to detect a cDNA
sequence from the sample that hybridizes to a nucleic acid sequence at a known location on the microarray. Typically, the signal intensity is proportional to the cDNA
quantity, and thus mRNA, expressed in the sample cells. A large number of such techniques are available and useful. Preferred methods can be found in US Patents 6,271,002;
6,218,122; 6,218,114; and 6,004,755.
Analysis of the expression levels is conducted by comparing such intensities.
This is best done by generating a ratio matrix of the expression intensities of genes in a test sample versus those in a control sample. For instance, the gene expression intensities from a tissue that has been treated with a drug can be compared with the expression intensities generated from the same tissue that has not been treated with the drug. A ratio of these expression intensities indicates the fold-change in gene expression between the test and control samples.

Gene expression profiles can be displayed in a number of ways. A common method is to arrange a ratio matrix into a graphical dendogram where columns indicate test samples and rows indicate genes. The data are arranged so genes that have similar expression profiles are proximal to each other. The expression ratio for each gene is visualized as a color. For example, a ratio less than one (indicating down-regulation) may appear in the blue portion of the spectrum while a ratio greater than one (indicating up-regulation) may appear as a color in the red portion of the spectrum. Commercially available computer software programs are available to display such data including "GENESPRINT" from Silicon Genetics, Inc. and "DISCOVERY" and "INFER" software from Partek, Inc.
The differentially expressed genes are either up regulated or down regulated in diseased cells, as deduced by an assessment of gene expression as described above.
Up regulation and down regulation are relative terms meaning that a detectable difference (beyond the contribution of noise in the system used to measure it) is found in the amount of expression of the genes relative to some baseline. In this case, the baseline is the measured gene expression of a normal cell. The genes of interest in the diseased cells are then either up regulated or down regulated relative to the baseline level using the same measurement method. Preferably, levels of up and down regulation are distinguished based on fold changes of the intensity measurements of hybridized microarray probes. A 1.5 fold difference is preferred for making such distinctions. That is, before a gene is said to be differentially expressed in treated versus untreated diseased cells, the treated cell is found to yield at least 1.5 times more, or 1.5 times less intensity than the untreated cells. A 1.7 fold difference is more preferred and a 2 or more fold difference in gene expression measurement is most preferred.
One method of the invention involves comparing gene expression profiles for various genes to determine whether a person is likely to respond to the use of a therapeutic agent. Having established the gene expression profiles that distinguish responder from non-responder, the gene expression profiles of each are fixed in a medium such as a computer readable medium as described below. A patient sample is obtained that contains diseased cells (such as hematopoietic blast cells in the case of AML) is then obtained. Most preferably, the samples are of bone marrow and are extracted from the patient's sternum or iliac crest according to routine methods.
Preferably the bone marrow aspirate is processed to enrich for leukemic blast cells using routine methods. Sample RNA is then obtained and amplified from the diseased patient cells and a gene expression profile is obtained, preferably (in the case of a large gene portfolio) via micro-array, for genes in the appropriate portfolios. The expression profiles of the samples are then compared to those previously analyzed for prognostic outcome. When a small number of genes are used in the portfolio such as when the three gene profile is used, a simple nucleic acid amplification and detection scheme is the most preferred method of ineasuring gene modulation. In such a case, PCR, NASBA, rolling circle, LCR, and other amplification schemes known to skilled artisans can be used with PCR being most preferred. Where the portfolios include a large number of genes or it is desirable to measure the expression of numerous other genes then it is preferred to assess the expression patterns based on intensity measurements of microarrays as described above.
In similar fashion, gene expression profile analysis can be conducted to monitor treatment response. In one aspect of this method, gene expression analysis as described above is conducted on a patient treated with any suitable treatment at various periods throughout the course of treatment. If the gene expression patterns are consistent with a positive outcome the patient's therapy is continued. If it is not, the patient's therapy is altered as with additional therapeutics, changes to the dosage, or elimination of the current treatment. Such analysis permits intervention and therapy adjustment prior to detectable clinical indicia or in the face of otherwise ambiguous clinical indicia.
With respect to the molecular markers of the invention, a number of other formats and approaches are available for diagnostic use. Methylation of genomic regions can affect gene expression levels. For example, hypermethylation of gene promoter regions can constitutively down-regulate gene expression whereas hypomethylation can lead to an increase in steady-state mRNA levels. As such, detection of methylated regions associated with genes predictive of drug response, prognosis or status can be used as an alternative method for diagnosing gene expression levels.
Such methods are known to those skilled in the art. Alternatively, single nucleotide polymorphisms (SNPs) that are present in promoter regions can also affect transcriptional activity of a gene. Therefore, detection of these SNPs by methods known to those skilled in the art can also be used as a diagnostic for detecting genes that are differentially expressed in different prognostic outcomes.

Articles of this invention are representations of the gene expression profiles useful for treating, diagnosing, prognosticating, staging, and otherwise assessing diseases.
Preferably they are reduced to a medium that can be automatically read such as computer readable media (magnetic, optical, and the like). The articles can also 5 include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a CD ROM having computer instructions for comparing gene expression profiles of the portfolios of genes described above.
The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the 10 profiles can be recorded in different representational format. Clustering algorithms such as those incorporated in "DISCOVERY" and "1NFER" software from Partek, Inc. mentioned above can best assist in the visualization of such data.
Additional articles according to the invention are kits for conducting the assays described above. Each such kit would preferably include instructions in human or 15 machine readable form as well as the reagents typical for the type of assay described.
These can include, for example, nucleic acid arrays (e.g. cDNA or oligonucleotide arrays), as described above, configured to discern the gene expression profiles of the invention. They can also contain reagents used to conduct nucleic acid amplification and detection including, for example, reverse transcriptase, reverse transcriptase 20 primer, a corresponding PCR primer set, a thermostable DNA polymerase, such as Taq polymerase, and a suitable detection reagent(s), such as, without limitation, a scorpion probe, a probe for a fluorescent probe assay, a molecular beacon probe, a single dye primer or a fluorescent dye specific to double-stranded DNA, such as ethidium bromide. Kits for detecting surface antigens contain staining materials or 25 are antibody based including components such as buffer, anti-antigenic antibody, detection enzyme and substrate such as Horse Radish Peroxidase or biotin-avidin based reagents. Kit components for detecting blast cells generally include reagents for conducting flow cytometry, blast cell adhesion assays, and other common blast cell assays.
Conventional anti-cancer agents include, without limitation, tyrosine kinase inhibitors, MEK kinase inhibitors, P 13K kinase inhibitors, MAP kinase inhibitors, apoptosis modulators and combinations thereof. Exemplary drugs that are most preferred among these are the "GLEEVEC" tyrosine kinase inhibitor of Novartis, U-0126 MAP kinase inhibitor, PD-098059 MAP kinase inhibitor, SB-203580 MAP

kinase inhibitor, and antisense, ribozyme, and DNAzyme, Bcl-XL, and anti-apoptotics. Examples of other useful drugs include, without limitation, the calanolides of US Patent 6,306,897; the.substituted bicyclics of US Patent 6,284,764; the indolines of US Patent 6,133,305; and the antisense oligonucleotides of US Patent 6,271,210; platinum coordination compounds for example cisplatin or carboplatin, taxane compounds for example paclitaxel or docetaxel, camptothecin compounds for example irinotecan or topotecan, anti-tumor vinca alkaloids for example vinblastine, vincristine or vinorelbine, anti-tumor nucleoside derivatives for example 5-fluorouracil, gemcitabine or capecitabine, nitrogen mustard or nitrosourea alkylating agents for example cyclophosphamide, chlorambucil, carmustine or lomustine, anti-tumor anthracycline derivatives for example daunorubicin, doxorubicin or idarubicin; HER2 antibodies for example trastzumab;
and anti-tumor podophyllotoxin derivatives for example etoposide or teniposide; and antiestrogen agents including estrogen receptor antagonists or selective estrogen receptor modulators preferably tamoxifen, or alternatively toremifene, droloxifene, faslodex and raloxifene, or aromatase inhibitors such as exemestane, anastrozole, letrazole and vorozole.
Anti-cancer agents can also include therapeutics directed to gene therapy or antisense therapy or RNA interference. These include, without limitation, oligonucleotides with sequences complementary to an mRNA sequence can be introduced into cells to block the translation of the mRNA, thus blocking the function of the gene encoding the mRNA. The use of oligonucleotides to block gene expression is described, for example, in, Strachan and Read, Human Molecular Genetics, 1996. These antisense molecules may be DNA, stable derivatives of DNA
such as phosphorothioates or methylphosphonates, RNA, stable derivatives of RNA
such as 2'-O-alkylRNA, or other antisense oligonucleotide mimetics. Antisense molecules may be introduced into cells by microinjection, liposome encapsulation or by expression from vectors harboring the antisense sequence.
In gene therapy, the gene of interest can be ligated into viral vectors that mediate transfer of the therapeutic DNA by infection of recipient host cells. Suitable viral vectors include retrovirus, adenovirus, adeno-associated virus, herpes virus, vaccinia virus, polio virus and the like. Alternatively, therapeutic DNA can be transferred into cells for gene therapy by non-viral techniques including receptor-mediated targeted DNA transfer using ligand-DNA conjugates or adenovirus-ligand-DNA

conjugates, lipofection membrane fusion or direct microinjection. These procedures and variations thereof are suitable for ex vivo as well as in vivo gene therapy.
Protocols for molecular methodology of gene therapy suitable for use with the gene is described in Gene Therapy Protocols, edited by Paul D. Robbins, Human press, Totowa NJ, 1996.
Compounds identified according to the methods disclosed herein may be used alone at appropriate dosages defined by routine testing in order to obtain optimal inhibition or activity while minimizing any potential toxicity. In addition, co-administration or sequential administration of other agents may be desirable.
The invention is further illustrated by the following nonlimiting examples.
All references cited herein are hereby incorporated herein by reference.
Example 1 Clinical Evaluation and Response Definitions The current study was part of an open label, multicenter, non-comparative phase 2 clinical study in which patients with relapsed or refractory AML (Harousseau et al.
(2003)) were treated with tipifarnib at a starting oral dose of 600 mg bid for the first 21 consecutive days of each 28-day cycle. Patients were enrolled into 2 cohorts, those with relapsed AML and those with refractory AML. A total of 252 patients (135 relapsed and 117 refractory) were treated. Eighty patients chose to provide bone marrow samples for RNA microarray analysis, for which a separate informed consent was required. The overall response rate was relatively low in this study.
Therefore, for the purposes of the gene expression profiling, response to tipifarnib was defined as patients who had an objective response (complete remission [CR], complete remission with incomplete platelet recovery [CRp] or partial remission [PR]), a hematological response (decrease of >50% of leukemic blast cells in bone marrow) as determined by either central review or by the clinical site, or stable disease (no hematological response but no progression of the disease) as determined by both central review and the clinical site. Complete remission with incomplete platelet recovery was defined similarly, except for a platelet count less than 100,000/ L sufficient to ensure transfusion independence. Partial remission was defined as at least a 50% decrease in bone marrow blasts with partial neutrophil (>500/ L) and platelet count (>50,000/ L) recovery. Response had to be confirmed at least 4 weeks after first documentation.

Sample Collection and Microarray Processing Bone marrow samples were collected from patients before treatment with tipifarnib, diluted with phosphate buffered saline (PBS) and centrifuged with Ficoll-diatrizoate (1.077 g/mL). White blood cells were washed twice with PBS, resuspended in fetal bovine serum (FBS) with 10% dimethyl sulfoxide (DMSO) and immediately stored at -80 C. Cells were thawed and total RNA was extracted from cell samples using the RNeasy Kit (Qiagen, Valencia, CA). RNA quality was checked using the Agilent Bioanalyzer. Synthesis of cDNA and cRNA was performed according to Affymetrix (Santa Clara, CA) protocols.

Microarray Processing Two rounds of linear amplification were performed because the RNA yield for several samples was too low to obtain enough labeled cRNA for chip hybridization using one round of amplification. For hybridization, 11 g of cRNA were fragmented randomly by incubation at 94 C for 35 minutes in 40 mM Tris-acetate, pH 8.1, 100 mM potassium acetate, and 30 mM magnesium acetate. Fragmented cRNA was hybridized to U133A arrays at 45 C for 16 hours in a rotisserie oven set at 60 rpm. Following hybridization, arrays were washed (with 6x SSPE and 0.5x SSPE containing Triton X-100 [0.005%]), and stained with streptavidin-phycoerythrin (SAPE; Molecular Probes, Eugene, OR). Quantification of bound labeled probe was conducted using the Agilent G2500A GeneArray scanner (Agilent Technologies, Palo Alto, CA).
The total fluorescence intensity for each array was scaled to the uniform value of 600. Chip performance was quantitated by calculating a signal to noise ratio (raw average signal/noise). Chips were removed from further analysis if their signal-to-noise ratio was less than 5. Genes were only included in further analysis if they were called "present" in at least 10% of the chips. Eleven thousand seven hundred twenty three Affymetrix probe sets remained following this cut-off. The quality of the gene expression data were further controlled by identifying outliers based on principal components analysis and by analyzing the normal distributions of the gene intensities (Partek Pro V5.1).
Statistical Analysis To identify genes that predict response with high sensitivity, a percentile analysis was employed. Genes that were up- or down-regulated in 100% of responders compared to at least 40% of non-responders were identified. The chi-squared test and Student's t-test were then used to test the significance of the correlations between patient response and patient co-variates, including ras mutation status, and gene expression. Unsupervised k-means and hierarchical clustering were performed in Omniviz. The predictive value of the selected genes was then analyzed by leave-one-out and leave-five-out cross validation methods. Here, one (or five) sample(s) was (were) removed from the data set and the marker was reselected from 11,723 genes. The predictive value of this gene was then tested on the left-out sample(s) using a linear discriminant analysis. Sensitivity was calculated as the number of true positives detected by the test divided by the sum of true positives plus false negatives. Specificity was calculated as the number of true negatives detected by the test divided by the sum of true negatives and false positives. Positive predictive value was calculated as the number of true positives divided by the number of true positives and false positives. Negative predictive value was calculated as the number of true negatives divided by the number of true negatives and false negatives. The positive likelihood ratio of a patient responding to treatment is sensitivity divided by 1 minus specificity. Receiver operator curves (ROC) were used to choose appropriate thresholds for each classifier, requiring a sensitivity of 100%. The ROC diagnostic calculates the sensitivity and specificity for each parameter.
Real-Time RT-PCR Validation TaqMan real-time RT-PCR was employed to verify the microarray results of the AHR and AKAP13 genes. For each 1 g sample of amplified RNA, cDNA was produced using T7 oligo(dT) primer and Superscript II reverse transcriptase according to the manufacturer's instructions (Invitrogen). Primers and MGB-probes for AKAP 13 gene and control gene PBGD were designed using Primer Express (Applied Biosystems), while those for AHR gene and control gene HPRT were available as Assays-on-Demand from ABI. Primer/probe sequences for AKAP 13 were as follows: AKAP13 forward, 5'ggtcagatgtttgccaaggaa3' (SEQ ID NO: 1);
AKAP 13 reverse, 5'tcttcagaaacacactcccatc-3' (SEQ ID NO: 2); AKAP 13 probe, 6FAM-tgaaacggaagaagcttgtA-3' (SEQ ID NO: 3).
All primers and probes were tested for optimal amplification efficiency above 90%. The relative standard curve was composed of 5 dilutions (10-fold each) of HeLa cDNA (in most cases, ranging from 25 ng to 2.5 pg). RT=PCR amplification mixtures (25 L) contained 100 ng template cDNA, 2x TaqMan universal PCR
master mix (12.5 L; Applied Biosystems), 500 nM forward and reverse primers, and 250 nM probe. Reactions were run on an ABI PRISM 7900HT Sequence Detector (Applied Biosystems). The cycling conditions were: 2 min of AmpErase 5 UNG activation at 50 C, 10 min of polymerase activation at 95 C and 50 cycles at 95 C for 15 sec and annealing temperature (59 C or 60 C) for 60 sec. In each assay, a standard curve and a no-template control along with template cDNA were included in triplicates for the gene of interest and control gene. The relative quantity of each gene was calculated based on the standard curve, and was normalized with the 10 quantity of the control gene. The median coefficient of variation (based on calculated quantities) of triplicate samples was 8%. The correlation between repeated runs using independently diluted templates from the stock was above 0.95.
Samples were only compared with microarray data if duplicate TaqMan experiments showed reproducible results.
15 Cell Line Culture and AKAP 13 Over-expression Assay The AKAP13 vectors, oncoLBC and protoLBC, and vector control (pSRalpha-neo) were obtained from Dr. Deniz Toksoz. Zheng et al. (1995). The HL60 cell line was obtained from the American Tissue Culture Collection and grown in RPMI
1640 with 10% FBS. Cells were transiently transfected with each vector using the 20 Effectene reagent (Qiagen) according to the manufacturer's instructions and kept under G418 (600 g/mL) for 7 days. Tipifamib was then added in various concentrations (0, 1.5, 3.1, 6.3, 13, 25, 50, 100, 200, 1000, and 10,000 nM) to duplicate cultures (1.5x105 cells/mL). Cells were counted at Day 6. Cell counts were normalized to cultures with no drug to give a percent of viable control cells.
25 Results Expression Profiling of Relapsed and Refractory AML
FTIs were originally designed to specifically inhibit FTase activity, thereby blocking the oncogenic ras pathways. Therefore, we initially analyzed DNA from the bone marrow of 80 patients with relapsed or refractory AML for activating ras 30 mutations and investigated the possible correlation between ras mutation and the response to tipifarnib. While 26% of the AML samples harbored N-ras mutations, mutation status did not correlate with objective response or overall survival.
Harousseau et al. (2003). We therefore performed gene expression profiling to identify novel signatures that could be used to predict response to the FTI
tipifamib.
Bone marrow samples were obtained for gene expression analysis from 80 patients prior to treatment with tipifarnib. Table 1 shows the patient information.
Table 1. Patient Information Patient ID AML CLASS SEX AGE Best Response* Survival Time Response IA30060 REFRACTC)IZI \1ALL 7Q CR 355 A300i ] RELAPSED V'[ALL 70 PR 154 A30059 RCFRAC'T( K f EMALL r;? SI) 119 es A30095 REFRACT(KY \I.ALE r;l ( R i) vc 130I77 RELAPSEL) I EMALE 03 SE) 7 vrs \3(11'>_' RLL,AI'SI U \i:\I F. 67 tiL) ?60 vcs A3O73~ RFI.IFSE:E) FFRtAE_F. ~1~ (R 270 vcs A;0246 Kf,:FEZ:AC[( iKY NfALI-, 7 4 II I 2 13 A~U353 ftLLAf'S11_) 1I7T'1ALL 3o III l~) vc, \30-"5-; KEE_aPSEI-) 1+A111 E 44 IIl T7 ~c5 130360 UFEt,ACTUIZI" \1~AI F w0 llI 3~ vc~ A',(i3h4 KI~IAPSEG Ki.ti1 AL1=- ~4 HI 6 7 -A_')(371) KE-:I_:AP~HL) IE:%lr\LL (1 3 SE) \;03h0 KLFK:ACl(>KY 1, E.:%1-ALF
71 Ill 71 A30007 REFRACTORY FEMALE 54 NR 106 no A30008 REFRACTORY MALE 52 NR 27 no A30053 RELAPSED MALE 51 NR 48 no A30057 REFRACTORY MALE 74 NR 102 no A30060 REFRACTORY FEMALE 33 NR 175 no A30096 REFRACTORY MALE 69 NR 182 no A30179 REFRACTORY FEMALE 70 NR 148 no A30182 REFRACTORY MALE 70 NR 92 no A30190 RELAPSED FEMALE 54 NR 51 no A30191 RELAPSED FEMALE 67 NR 78 no A30245 RELAPSED MALE 63 NR 366 no A30300 RELAPSED MALE 47 NR 414 no A30302 RELAPSED MALE 62 NR 234 no A30308 RELAPSED MALE 66 NR 71 no A30311 RELAPSED FEMALE 61 NR 115 no A30377 RELAPSED MALE 68 NR 364 no A30047 RELAPSED FEMALE 63 NR 94 no A30055 RELAPSED FEMALE 71 NR 56 no A30063 RELAPSED MALE 46 NR 220 no A30090 REFRACTORY FEMALE 85 NR 56 no A30091 REFRACTORY FEMALE 67 NR 56 no A30092 REFRACTORY FEMALE 54 NR 40 no A301 11 REFRACTORY FEMALE 71 NR 38 no A30112 RELAPSED FEMALE 61 NR 12 no A30113 REFRACTORY MALE 75 NR 177 no A30119 REFRACTORY MALE 19 NR 36 no A30153 RELAPSED FEMALE 68 NR 105 no A30176 REFRACTORY MALE 75 NR 54 no A30178 RELAPSED FEMALE 70 NR 39 no A30180 REFRACTORY MALE 62 NR 72 no A30183 REFRACTORY MALE 63 NR 64 no A30244 RELAPSED FEMALE 34 NR 35 no A30247 REFRACTORY FEMALE 72 NR 35 no A30248 RELAPSED MALE 46 NR 61 no A30304 RELAPSED MALE 65 NR 44 no A30306 RELAPSED FEMALE 28 NR 74 no A30349 REFRACTORY MALE 58 NR 22 no A30354 REFRACTORY FEMALE 31 NR 103 no A30359 RELAPSED MALE 65 NR 8 no A30363 RELAPSED MALE 64 NR 37 no A30376 RELAPSED FEMALE 24 NR 383 no A30378 RELAPSED FEMALE 76 NR 184 no A30381 REFRACTORY FEMALE 70 NR 128 no A30395 REFRACTORY MALE 61 NR 83 no * Stable disease (SD) only included if confirmed by independent investigators HI = hematological improvement CR = complete response PR = partial response NR = no response Fifty-eight of the 80 samples passed quality control measures including RNA
quality and chip performance. There were no significant differences in age, sex, AML class (relapsed or refractory), cytogenic risk factors, baseline blast counts, response, and overall survival between these 58 patients and the remainder of the clinical study population (N = 194; Table 2).
Table 2 co-variate 58 subset 194 remainder p-value response 14 28 0.1237 male 28 119 0.1055 average age 60 56 0.1046 relapsed 31 104 0.8977 cytogenetic risk 34 5 0.1503 average blasts 55% 50% 0.1629 The gene expression data were integrated with the clinical information and retrospective analyses were performed to identify genes that could separate responders from non-responders with a high level of sensitivity. The data went through several filtering steps before identification of differentially expressed genes.
First, genes that were not expressed in at least 10% of the samples were removed.
This reduced the number of genes from approximately 22,000 to 11,723 genes.
For unsupervised analyses genes that showed little variation in expression across the dataset (coefficient of variance of <45% across all the samples) were also excluded and quantile normalization was applied to the remaining 5,728 genes. At this stage an unsupervised k-means clustering analysis was performed to identify any differences between patients based on their global gene expression profiles.
Six main clusters of patients were identified using this technique. No separation between responders and non-responders was observed (Figure 1). Only a handful of genes may be associated with the anti-tumor effect of FTIs, for example, it is possible that the differential expression of a single gene that is involved in FTI
biology impacts clinical response and this would be masked by the noise introduced from the other 11,722 genes.
Example 2 Identification of Genes that Are Differentially Expressed Between Responders and Non-responders We next performed supervised analysis using the gene expression data to identify genes that were differentially expressed between all responders and at least 40% of non-responders. These criteria were chosen to identify genes that could predict response to tipifarnib with the highest level of sensitivity possible.
From 11,723 genes, a total of 19 genes were identified that could stratify responders and non-responders (Table 3 and Table 10 for more detail) and that gave significant P
values in a t-test (P <0.05). The genes included those involved in signal transduction, apoptosis, cell proliferation, oncogenesis, and potentially, FTI
biology (ARHH, AKAP13, IL3RA).
Table 3 Top 19 Genes that Predict Response to tipifarnib and Results of Analysis SEQ ID
NO Symbol Specificity P value Functional description 151 AHR 0.52 0.00000255 Apoptosis, cell cycle, signal transduction 309 AKAP13 0.63 0.00006133 Small GTPase mediated signal transduction, oncogenesis 488 MINA53 0.50 0.00006934 Cell proliferation 411 IDS 0.50 0.00023964 Glycosaminoglycan degradation 632 OPN3 0.40 0.00064297 G-protein coupled receptor protein signaling 280 GPR105 0.43 0.00087608 G-protein coupled receptor protein signaling 582 TENCI 0.43 0.0010309 Signal transduction 376 TNFSF13 0.40 0.00104219 Cell proliferation 134 SVIL 0.45 0.00145723 Cytoskeletal anchoring 272 IL3RA 0.40 0.00198392 Receptor signaling 209 C6orf56 0.40 0.00261553 -697 FRAG1 0.45 0.00298989 Tumor suppressor 476 GOSRI 0.45 0.01201057 Intra-Golgi transport 204 KIAA1036 0.43 0.01262079 -483 BTG3 0.47 0.01659402 Regulation of cell cycle 487 MAPK8IP3 0.40 0.01817428 Regulation of JNK cascade 419 LILRB3 0.41 0.02374898 Immune response 242 ARHH 0.40 0.02721922 Small GTPase mediated signal transduction 496 NPTX2 0.45 0.03346833 Heterophilic cell adhesion Real Time RT-PCR Validation of Gene Markers To verify the microarray gene expression data, TaqMan real time RT-PCR was performed on cDNA that was used for generating the labeled target cRNA for microarray hybridization. Two genes were selected to verify the gene expression data. The AHR and AKAP 13 genes were chosen because the use of these genes resulted in the highest level of specificity for responders. The correlation coefficient was 0.74 for AHR and 0.94 for AKAP 13 indicating that the microarray gene expression data could be validated by PCR (Figure 2).
Identification of the AKAP 13 Gene as a Marker of Resistance AKAP13 was over-expressed in patients who were resistant to tipifarnib. The predictive value of this gene was calculated for the 58 samples using a leave-one-out cross validation (LOOCV; Figure 3A). AKAP13 gene expression predicted non-response with a negative predictive value (NPV) of 96%, and low expression levels mediated response with a positive predictive value (PPV) of 43% (x2 = 13.7; P
=
0.0022). The overall diagnostic accuracy was 69% and positive likelihood ratio of responding was 2.4. Therefore, stratification of this patient population based on AKAP13 gene expression increased the response rate from 24% (14/5 8) in the entire group to 43% (13/30) among those patients with low expression of the gene.
Expression values for the AKAP13 gene in each patient are shown in Figure 3B.
When survival was analyzed by Kaplan-Meier analysis, the median survival of patients with low expression of this gene was 90 days longer than those patients who had high expression levels (P = 0.008; Figure 3C).
Identification of a Minimal Set of 3 Gene Markers LOOCV was used to identify a candidate set of gene markers that could predict response to tipifarnib with an improved accuracy compared to AKAP 13 alone.
Classifiers were built with an increasing number of genes based on t-test P
values, and the error rate of these classifiers was calculated using LOOCV while keeping the sensitivity of predicting response at 100% (Figure 4A).

The 3-gene classifier could predict response with the lowest error rate (Figure 4A). This was also observed when a leave-five-out cross validation was performed.
When more genes were added the error rate increased, indicating that additional genes were introducing noise to the classifier. For the 3-gene classifier, the LOOCV
5 demonstrated a NPV of 94% and a PPV of 48%, with an overall diagnostic accuracy of 74% and positive likelihood ratio of 2.9 (Figure 4B). The combined expression values for the 3 genes in each patient are shown in Figure 4C. Therefore, for the group of patients with this gene signature, the response rate to tipifamib was 48%
(12/25) compared to 24% (14/58) in this patient population.
10 Using the 3-gene signature (AHR, AKAP 13 and MINA53), Kaplan-Meier analysis again showed a significant difference in survival between the predicted responder group and the non-responder group (Figure 4D). The 13 patients who were incorrectly classified as responders had a better overall survival compared to the 31 patients correctly classified as non-responders (Figure 5).
Interestingly, the 2 15 patients that were misclassified as non-responders only demonstrated hematological improvements with relatively short overall survival times (71, 87 days).
Over-expression of AKAP13 Increases Resistance to tipifarnib in AML

The AKAP 13 gene was the most robust marker of resistance to tipifamib. We therefore investigated its involvement in FTI biology by over-expressing the 20 oncoLBC and protoLBC variants of this gene in the HL60 cell line. Transient transfectants were then tested for sensitivity to tipifarnib. Over-expression of both AKAP 13 variants in this AML cell line model led to an approximate 20-fold increase in resistance to tipifarnib compared to control cells (Figure 3).
Both the LBC oncogene and proto-oncogene increased the resistance to tipifarnib to the same 25 extent, as seen by a parallel rightward shift of the kill curves by more than one log-unit compared to control.
Discussion Two groups recently identified gene expression profiles of newly diagnosed adult AML patients that are useful for predicting clinical outcome. Bullinger et al.
30 (2004); and Valk et al. (2004). These profiles seem to be more powerful than currently used prognostic markers such as karyotyping. Moreover, expression profiles have been found that predict response to anticancer compounds including standard chemotherapeutics (Chang et al. (2003); Okutsu et al. (2002); and Cheok et al. (2003)) and novel selective anticancer agents. Hofmann et al. (2002); and McLean et al. (2004). Similarly, pharmacogenetic profiles have recently been found that correlate with patient response to the tyrosine kinase inhibitor gefitinib. Paez et al. (2004) and Lynch et al. (2004). In that study, a subgroup of non-small cell lung cancer patients had activating mutations within the target epidermal growth factor receptor that correlated with clinical response to the targeted therapy.
In a phase 2 study of relapsed and refractory AML patients, we have identified gene expression profiles that predict response to tipifamib, a novel farnesyl transferase inhibitor. This class of compounds is showing promise in the treatment of hematological malignancies (Karp et al. (2001); Kurzrock et al. (2004);
Alsina et al. (2003); Cortes et al. (2003); and Thomas et al. (2001)) and solid tumors such as breast cancer (Johnston et al. (2003)) and recurrent glioma. Brunner et al.
(2003).
However, while clinical responses are being demonstrated, there is a growing need to tailor therapy by identifying patients who are most likely to respond to the drug and are, therefore, the best candidates for treatment. Furthermore, while ras was considered to be a primary target of this class of drugs, several clinical studies have shown that they are not necessarily effective in populations with a high frequency of ras mutations. Van Cutsem et al. (2004); and Rao et al. (2004).
Several gene markers were identified that have the potential to predict response to tipifarnib. A subset of these markers was both predictive of drug response and also thought to have the potential to be involved in FTI biology. One of the top candidates discovered from the microarray studies was the lymphoid blast crisis oncogene (oncoLBC or AKAP13). This gene functions as a guanine nucleotide exchange factor for the Rho proteins (Zheng et al. (1995)) and as a protein kinase A
anchoring protein. Carr et al. (1991). AKAP13 contains a region that is homologous to an a-helical domain that is known to interact with lamin B.
Foisner et al. (1991). This association could lead to lamin B activation via protein kinase A, consequently increasing mitotic activity. Both RhoB and lamin B are farnesylated and are candidate targets of FTIs. AKAP 13 is also a proto-oncogene, because loss of its 3-prime end causes cellular transformation. Sterpetti et al. (1999).
While it was originally identified from a patient with chronic myeloid leukemia, its expression has not been documented in AML.
The identification of several genes that are potentially involved in FTI
biology (ARHH, AKAP13, IL3RA) support the idea that the interaction of multiple pathways can affect how FTIs function in this population of AML patients (Figure 7). These genes interact with several farnesylated proteins including ras, rho, and potentially lamin B. Rho proteins are potentially important antitumorigenic targets for FTIs. Sahai et al. (2002); and Lancet et al. (2003). RhoB, RhoA, and RhoC
have been found to be over-expressed in multiple cancer types. Sahai et al.
(2002).
In addition, RhoH (ARHH) is frequently re-arranged in tumors of myeloid origin, and this may lead to its over-expression. Pasqualucci et al. (2001). While most of these Rho proteins are geranygeranylated, they interact closely with each other and the farnesylated ras, RhoE, and RhoB small GTPases. Sahai et al. (2002); and Li et al. (2002). Furthermore, it has been shown that RhoH, RhoB, and RhoE can act in an antagonistic fashion to the transforming abilities of RhoA and RhoG. Li et al.
(2002). The activity of RhoA, and possibly other related small GTPases, is increased by the guanine nucleotide exchange factor lymphoid blast crisis oncogene (AKAP13). Sterpetti et al. (1999); and Toksoz et al. (1994). In addition, may increase mitotic activity by activating lamin B via protein kinase A.
Foisner et al. (1991). It is also well known that the IL3 receptor activates ras pathways. Testa et al. (2004). Therefore, as indicated in Figure 7, the increased activity of and AKAP13, and the decrease in RhoH expression could lead to an increased cellular profile of transformation. This might allow for the leukemic blast cell to overcome the anti-tumorigenic effects of FTIs through compensatory pathways.
In contrast, when IL3RA and AKAP13 are under-expressed and there is an increase in RhoH activity, FTIs may be more effective in blocking these pathways.
Finally, we demonstrated that over-expression of AKAP 13 (both oncoLBC and protoLBC variants) increased the IC50 of the HL60 AML cell line by approximately 20-fold. This indicates that over-expression of AKAP 13 is a relevant marker of resistance and that it may also be a useful alternative drug target for patients who are resistant to tipifarnib.
Overall, our findings allow development of a gene expression based diagnostic assay to identify patients likely respond to tipifarnib. This information could be used to better direct treatment to the appropriate patient population. Using survival as the gold standard, the gene signature predicts a level of response to therapy that cannot be predicted by using conventional clinical response criteria.
Alternatively, this raises the question of whether the gene signature for predicting response to FTI
treatment also has prognostic value irrespective of FTI therapy. We thus evaluated a prognostic signature previously identified in newly diagnosed AML patients who were treated with conventional chemotherapy. Bullinger et al. (2004). While this signature showed utility in the current patient population our 3-gene signature further stratified these poor and good prognostic groups showing that it is a predictor of response to FTIs.
Example 3 Analysis of an AML prognostic gene signature The 3-gene signature can predict prognosis irrespective of the type of drug treatment. To determine this, we first evaluated a gene-expression signature recently identified in newly diagnosed AML patients who were treated with conventional chemotherapy. Bullinger et al. (2004). This signature was defined using a cDNA
array and therefore we first matched these genes with the probes present on the Affymetrix gene chip. Of the 133 predictive genes identified by Bullinger et al., 167 probe sets (corresponding to 103 unique genes) were matched to the Affymetrix U133A chip. The 3 genes identified in our present analysis are not present in the Bullinger et al. 133 gene list. SEQ ID NOs: shown in Table 4. Two main groups of patients were defined by hierarchical clustering using these 167 probe sets (Fig 8A).
Kaplan-Meier analysis showed a clear stratification of these clusters into patients with good and poor prognosis (Fig 8B, p = 0.000003). Our data therefore show that a subset of the 133-gene prognostic signature identified by Bullinger et al.
(2004) can also be used in a relapsed and refractory cohort of patients.
Consequently, this indicates that the prognostic gene profile is surprisingly robust across different microarray platforms, and different classes of AML.
Neither of the clusters defined by the prognostic gene signature had significantly more responders. However, when the tipifarnib 3-gene signature was applied to the good and poor prognostic groups, patients who responded to tipifarnib were further stratified from both prognostic groups (Fig 8C). Therefore, the 3-gene signature has independent utility to the prognostic signature and that it is specific for FTI
treatment in this population of patients.

Example 4 Clinical Evaluation and Response Definitions The current study was an open label, multicenter, non-comparative Phase 2 study investigating the efficacy and safety of farnesyl transferase inhibition with tipifarnib administered as a single agent, at a starting oral dose of 600 mg b.i.d. for the first 21 days of each 28 day cycle in AML. Subjects were enrolled into two cohorts, those with relapsed AML and those with refractory AML. A total of 252 patients (135 relapsed and 117 refractory) were treated. For the purposes of the gene expression profiling response to tipifarnib was defined as patients who had an objective response (CR, CRp, or PR) (as described above), or patients who demonstrated confirmed stable disease, or a hematological response (decrease of >50% of leukemic blast cells) as determined by either central review or by the clinical site at any time during follow up.
Sample Collection and Microarray Processing All samples were obtained from patients who had consented to the described processing and analyses. Bone marrow samples were collected from patients before treatment with tipifarnib, diluted with PBS and centrifuged with Ficoll-diatrizoate (1.077g/ml). White blood cells were washed twice with PBS, resuspended in FBS
with 10% DMSO and immediately stored at -80 C. Cells were thawed and total RNA was extracted from cell samples using the RNeasy Kit (Qiagen, Valencia, CA).
RNA quality was checked using the Agilent Bioanalyzer. Synthesis of cDNA and eRNA were performed according to Affymetrix (Santa Clara, CA) protocols. Two rounds of linear amplification were performed because the RNA yield for several samples was too low to obtain enough labeled cRNA for chip hybridization using one round of amplification.
For hybridization, 11 g of cRNA were fragmented randomly by incubation at 94 C for 35 min in 40 mM Tris-acetate, pH 8.1, 100 mM potassium acetate, and mM magnesium acetate. Fragmented cRNA was hybridized to U133A arrays at 45 C for 16 h in a rotisserie oven set at 60 rpm. Following hybridization, arrays were washed (with 6x SSPE and 0.5x SSPE containing Triton X-100 (0.005%)), and stained with streptavidin-phycoerythrin (SAPE; Molecular Probes, Eugene, OR).
Quantification of bound labeled probe was conducted using the Agilent G2500A
GeneArray scanner (Agilent Technologies, Palo Alto, CA).

The total fluorescence intensity for each array was scaled to the uniform value of 600. Chip performance was quantitated by calculating a signal to noise ratio (raw average signal/noise). Chips were removed from further analysis if their signal-to-noise ratio was less than 5. Genes were only included in further analysis if they 5 were called "present" in at least 10% of the chips. Approximately 12,000 Affymetrix probe sets remained following this cut-off. Gene expression data quality was further controlled by identifying outliers based on principal components analysis and by analyzing the normal distributions of the gene intensities (Partek Pro V5.1).
10 Statistical Analysis Unsupervised hierarchical clustering and clustering was performed in Omniviz.
Kaplan-Meier analysis was performed using S-Plus.

Example 5 A prognostic signature identified in de novo AML has utility in relapsed and 15 refractory_patients Two papers were recently published describing gene-expression profiling of newly diagnosed adult AML patients and its use in predicting clinical outcome.
Bullinger et al. (2004); and Valk et al. (2004). We have profiled 58 patients with relapsed and refractory AML using the Affymetrix U133A gene chip. Of the 133 20 predictive genes identified by Bullinger et al. 167 probe sets (corresponding to 103 unique genes) were identified on the U133A chip (Fig 9). Bullinger et al.
(2004).
The 167 probe sets are listed in the Sequence Listing Table and designated the SEQ
ID NOs: shown in Table 4.
Two main groups of patients were defined by hierarchical clustering using these 25 167 probe sets (Fig l OA). Kaplan-Meier analysis showed a clear stratification of these clusters into patients with good and poor prognosis (Fig. l OB, p =
0.0000219).
Our data therefore shows that a 103 gene subset of the 133-gene prognostic signature identified by Bullinger et al. (2004) can also be used in a relapsed and refractory cohort of patients. Table 4. Consequently, this indicates that the 30 prognostic gene profile is surprisingly robust across different microarray platforms, different classes of AML, and for different treatment algorithms.
Table 4 SEQ ID NO clusterl mean J cluster2 mean ratio good rognostic group 44 2.101613636 0.980102944 2.144278466 u 51 1.574545244 0.899302214 1.750852182 u 52 2.459568465 1.161727897 2.117163987 up 55 1.752902097 0.968494897 1.809923937 up 56 2.395730325 0.72630844 3.298502667 up 68 1.319861385 1.36567127 0.96645614 down 76 1.269874887 1.430330507. 0.8878192 down 90 0.986377684 1.432950683 0.688354244 down 91 1.177005842 1.428309412 0.824055231 down 93 1.095239589 0.99028255 1.105986962 up 104 1.798024918 0.806182842 2.230294202 up 108 5.041411016 0.60238936 8.369024013 up 111 1.159807609 1.639962342 0.707216001 down 114 1.300310388 1.324114868 0.982022345 down 115 1.342190037 2.306688065 0.581868896 down 116 1.320735142 1.367135334 0.966060279 down 118 3.891659096 1.14773151 3.390739962 up 119 1.519690498 1.219697115 1.245957278 up 120 1.328167684 1.250925424 1.061748094 up 124 4.052024058 0.844109372 4.800354307 up 128 2.091653255 0.964077201 2.169591038 up 129 1.991638259 1.045332875 1.905267027 up 130 2.231280889 0.889145566 2.509466362 up 131 1.815199475 0.992602441 1.828727596 up 142 1.3198052 1.283492456 1.028292136 up 143 2.222738653 0.801693589 2.772553857 up 144 1.591280353 1.037860182 1.533231913 up 147 1.050088807 1.267434876 0.828515 down 148 1.07046517 1.621321185 0.660242511 down 150 1.768390061 0.83169323 2.126252803 up 153 1.253048826 1.17309932 1.068152376 up 157 1.24240959 1.057597528 1.174747063 up 158 1.039468561 1.565715718 0.663893546 down 159 0.897290104 2.462204436 0.364425509 down 160 2.042794407 1.039120916 1.965887103 up 161 2.935364557 1.109613717 2.645393178 up 167 0.771931017 1.585630652 0.486829021 down 174 1.206470925 1.279664724 0.942802363 down 200 1.099482264 1.491611294 0.737110445 down 207 4.901329448 0.871582063 5.623485909 up 208 2.346190152 0.930016604 2.522740069 up 211 0.774893647 2.117870914 0.365883323 down 212 8.626480573 0.518118436 16.64963061 up 220 2.002466652 2.360710711 0.84824737 down 221 1.031573096 1.410790966 0.731201943 down 222 1.236611762 1.579356606 0.782984512 down 224 4.252841813 1.038446727 4.095387566 up 227 2.330630017 0.740455045 3.147564506 u 229 1.271065192 1.275443314 0.996567372 down 230 4.079291876 0.770236845 5.296152606 up 231 1.613398862 1.053542946 1.531403032 up 234 2.462533188 0.694618451 3.54515948 up 235 1.736866874 1.312450625 1.323376926 up 236 0.973964566 1.442647881 0.675122862 down 237 2.146894817 1.117648799 1.920902898 up 238 1.121767102 1.496868758 0.749409122 down 241 1.09122088 1.120796083 0.973612325 down 243 1.563910635 1.102945516 1.417940063 u 244 1.473673649 1.474149259 0.999677367 down 245 1.148794849 1.399237842 0.821014709 down 246 1.350310245 1.327639719 1.017075812 u~
249 1.44906906 1.424150806 1.017496921 up 256 1.790625889 1.255091645 1.426689354 up 260 1.423005429 1.45961841 0.97491606 down 262 1.09603256 1.181485007 0.927673693 down 263 1.125580943 1.178287994 0.955268108 down 264 0.891946685 1.753005455 0.508809988 down 265 1.2942793 2.776461422 0.466161456 down 277 1.102290438 1.391074208 0.792402326 down 278 1.186998217 2.302906551 0.515434817 down 281 0.79821098 1.919103099 0.415929181 down 288 1.236023874 1.187331666 1.041009778 up 301 1.087313678 1.212772631 0.896551959 down 303 1.820970134 1.219911387 1.492706891 up 306 0.893432913 1.529980396 0.583950563 down 310 1.076214323 1.702252048 0.632229713 down 314 1.407137316 1.229815501 1.144185705 up 315 2.943883289 0.906317573 3.248180746 up 316 1.666394606 0.87032571 1.914679281 up 340 1.499937462 0.921725287 1.627315083 up 346 1.035229803 1.508152041 0.686422705 down 347 1.317050838 1.350558887 0.975189495 down 348 1.481608791 1.161107338 1.276030856 up 351 1.177285398 1.293627618 0.910065139 down 352 1.328716012 1.354505535 0.980960194 down 353 1.245636653 1.388354047 0.897203891 down 354 1.057630522 1.407147783 0.751612968 down 355 1.394737541 1.22633541 1.137321429 up 356 1.194410314 1.007530697 1.185482803 up 359 1.653993358 1.201194991 1.37695659 up 361 1.318561503 1.233384342 1.069059707 up 364 1.045812959 1.463795973 0.714452682 down 365 1.721871636 1.213128563 1.419364517 up 366 1.437978693 1.211203408 1.18723138 up 369 1.480402866 1.289306967 1.148215983 up 373 1.750186104 1.199777316 1.458759122 up 379 1.987020453 2.331667877 0.852188458 down 383 1.419274906 1.587359923 0.894110331 down 385 1.412275401 1.410313065 1.001391419 up 387 1.339345508 1.331753924 1.005700441 up 395 1.747595879 1.473302004 1.186176272 up 401 1.237925661 1.240617377 0.997830341 down 404 1.176676928 1.14697739 1.025893743 up 407 1.256304659 1.357899094 0.925182633 down 415 2.013680769 0.801461983 2.512509405 up 418 1.405 740421 1.158990553 1.212900673 up 423 1.346444485 1.136343796 1.184891835 up 426 9.592759013 1.560596128 6.146855576 up 430 1.768393033 1.23257422 1.434715252 up 437 1.40453464 1.860231401 0.755032218 down 438 1.673447728 3.930927905 0.425713157 down 445 1.283792446 1.501667999 0.85491097 down 446 1.214492279 1.091382628 1.11280155 up 448 1.561740275 1.228333636 1.271430033 up 450 1.734251715 1.844252606 0.940354759 down 452 1.408580649 1.582858413 0.889896807 down 453 1.1383915 1.741970797 0.653507798 down 454 1.079716972 1.450127515 0.744566917 down 457 1.783529874 1.314108612 1.357216487 up 460 1.136773942 1.003606776 1.132688588 up 462 1.220645614 1.113413039 1.096309789 up 466 1.183017496 1.211183566 0.976745003 down 467 1.113306085 1.0674171.58 1.042990621 up 469 1.211346488 1.1871952 1.020343148 up 484 0.998901987 1.654269584 0.60383265 down 485 1.133736575 1.914231031 0.592267368 down 489 1.412382921 1.384849519 1.019881873 up 493 3.988440713 0.965026485 4.132985753 up 495 1.187128423 1.462785203 0.81155348 down 498 3.691405839 0.765303503 4.823453473 up 499 2.216130138 0.848821568 2.610831558 up 507 2.199861346 0.773086467 2.845556663 up 520 1.67633492 1.194585675 1.403277265 up 524 7.743976211 0.841225224 9.205592025 up 529 2.591731743 1.08342825 2.392158173 up 530 1.273363236 1.097292728 1.160459013 up 535 1.095 8 79149 1.302490014 0.8413724 down 546 0.987046127 2.124625445 0.464574181 down 550 2.103990301 0.929475745 2.263631205 up 555 1.439273076 1.211738853 1.187774967 up 557 1.121983318 1.251788385 0.896304305 down 559 1.197910138 1.34693432 0.889360468 down 560 1.144102989 1.261720239 0.906780246 down 565 1.078544278 1.162115183 0.928087244 down 568 1.46401688 0.960152377 1.524775561 up 569 1.262437604 1.105379077 1.142085669 u 585 1.349621283 1.10729713 1.218842935 up 590 1.203037349 1.068047506 1.126389361 up 609 1.395352279 1.172354292 1.190213819 up 624 1.131210862 1.399636303 0.80821772 down 630 0.859929427 1.37477642 0.625504929 down 637 0.72293765 2.343345902 0.308506588 down 641 1.340847351 1.601484153 0.837252962 down 656 1.259818484 1.213892186 1.037833918 up 658 0.60640428 2.092078796 0.289857285 down 659 1.099549227 1.166865002 0.942310572 down 665 1.615399946 1.733324276 0.931966378 down 684 0.734401434 2.496195783 0.294208267 down 685 2.158835355 0.765517273 2.82010012 up 686 0.957971846 1.386779118 0.690789061 down 687 0.982131268 1.162818279 0.844612856 down 690 2.421692274 0.791473723 3.059725425 up 691 1.268020946 0.973474927 1.302571757 up 692 1.054213523 1.147045848 0.91906834 down 693 1.251020003 1.271391137 0.983977288 down 694 1.122443987 1.153661375 0.972940597 down 696 1.301034813 1.029185966 1.264139675 up The prognostic signature is independent of a 3-gene signature that predicts response to tipifamib.
We identified a 3-gene signature (AHR, AKAP13, MINA53) that predicts response to tipifarnib in relapsed and refractory AML patients. These genes can stratify patients into good and poor prognostic outcome groups (Fig 11 B, p =
0.002).
The question arises as to whether this gene signature is predicting response to FTI
treatment or merely identifying patients who have a generally good prognosis.
When the 3-gene signature was applied to the good and poor prognostic groups, responders were further stratified from the prognostic groups (Fig 11C, p =
0.000003). Following the application of both gene signatures there is clear stratification of a group of patients that do not respond to tipifamib and have a poor prognosis irrespective of treatment type (Fig 11 D, p = 0.0000005). Therefore, the 3-gene signature seems to be independent of the prognostic signature that has been identified and it is specific for FTI treatment in this population of patients. As a result we suggest that the prognostic signature maybe used in conjunction with drug-specific signatures (such as the tipifarnib predictive profile) to better manage patient therapy.

Example 6 Identification of genes that are differentially expressed between responders and non-responders (not including stable disease patients) Four patients were removed from the analysis since they were classified as 5 having stable disease and these patients cannot be clearly defined as either responders or non-responders. Inclusion of stable disease patients may bias the analysis for selecting genes associated with prognosis irrespective of drug treatment.
This resulted in comparing 10 responders with 44 non-responders. Selected genes were required to show a specificity of 40% and a minimum mean fold-change of 2Ø
10 These criteria were chosen to identify genes that could predict response to tipifarnib with the highest level of sensitivity possible. From 11,723 genes, a total of 8 genes were identified that could stratify responders and non-responders (Table 5) and that gave significant P values in a t-test (P <0.05). The genes included those involved in signal transduction, apoptosis, cell proliferation, oncogenesis, and potentially, FTI
15 biology. AKAP 13 is the most robust marker We next aimed at identifying a minimal set of genes that would provide the best diagnostic accuracy from the 8 selected genes. Classifiers were built with an increasing number of genes based on the AUC values from receiver operator characteristic analysis, and the error rate of these classifiers was calculated using 20 LOOCV while keeping the sensitivity of predicting response at 100% (Fig.
12a).
The AKAP 13 gene could predict response with the lowest error rate of less than 40% (Fig. 12a). The error rate increased to more than 50% when more than 2 genes were used in the classifier. For the AKAP 13 the LOOCV demonstrated a NPV of 93% and a PPV of 31%, with an overall diagnostic accuracy of 63% and positive 25 likelihood ratio of 2.0 (Fig. 12b). The expression value for AKAP 13 in each patient is shown in Fig. 12c. Therefore, for the group of patients with low expression of AKAP 13, the response rate to tipifarnib was 31 %(8/26) compared to 18%
(10/54) in the current patient population. Using the AKAP13 gene, Kaplan-Meier analysis showed a significant difference in survival between the predicted responder group 30 and the non-responder group (Fig. 12d).
Table 5. List of Top 8 Genes that Predict Response to Tipifarnib SEQ ID Symbol AUC fold P value Functional Description NO: change 309 AKAP13 0.830 0.491 0.00007 intracellular signaling, oncogenesis 151 AHR 0.807 0.446 0.00019 signal transduction, a o tosis 222 SCAP2 0.777 0.431 0.00007 signal transduction 496 NPTX2 0.738 0.115 0.02934 cell adhesion 451 BAT1 0.725 0.458 0.00097 cellular biosynthesis 272 IL3RA 0.705 0.375 0.00226 receptor signalling 411 IDS 0.645 0.395 0.00069 metabolism 280 P2RY14 0.627 0.369 0.00145 signal transduction AUC = area under the curve from receiver operator characteristic analysis.
This is an indication of the overall diagnostic accuracy.
Example 7 Gene Expression Profiling Predictive of Tipifarnib (ZARNESTRA , RI 15777) Response in Patients With Newly Diagnosed Acute Myeloid Leukemia Tipifarnib (ZARNESTRA , R115777), has demonstrated clinical response in patients with hematological disease. While the inhibition of protein farnesylation is the primary mechanism of action (MOA), the level of farnesyl inhibition is not a reliable pharmacodynamic marker of response, nor is it clear what genetic markers can be employed to predict response. This prospectively designed study was conducted to identify potential genetic markers and expression signatures that may be surrogate predictors of response for tipifarnib in patients with acute myeloid leukemia (AML). Bone marrow samples were collected and gene expression profiles analyzed from a single arm phase 2 clinical study of tipifarnib in poor-risk patients with newly diagnosed AML. Lancet et al. (2004). In total, 79 samples were profiled before (n = 25), during (n = 30), and after (n = 24) tipifarnib treatment.
Bone marrow samples were analyzed using the Affymetrix U133A GeneChip array. Global gene expression signatures revealed that tipifarnib treatment resulted in gene expression changes that were maintained for up to 120 days following treatment termination. Pretreatment vs post-treatment samples identified approximately 500 genes that had significant changes (False Discovery Rate [FDR]
<0.005) in gene expression following farnesyl transferase inhibition, including several genes associated with famesylation (eg K-ras, FNTA). Many of the modulated genes were identified as those significantly involved in protein biosynthesis, DNA replication, intracellular signaling, and cell cycle pathways, thus, reflective of inhibition of cellular proliferation. A subset of 27 genes (including genes associated with signal transduction and cell cycle) was also identified as being differentially modulated between responders and non-responders (P < 0.01).
Gene expression signatures previously identified from a phase 2 clinical trial in relapsed and refractory AML were also tested in pretreatment samples to examine their ability to predict response. Raponi et al. (2004). A combination of 6 genes was found to have significant predictive accuracy in this independent set of samples (P =
0.05). The genes identified from these studies might be used as surrogate biomarkers of tipifarnib activity.
Patient Samples In a phase 2 study, patients with newly diagnosed AML received ZARNESTRA , 600 mg bid21dQ4wks. Bone marrow samples were obtained before, during, and after treatment with ZARNESTRA . Mononuclear cells isolated by Ficoll-Hypaque density centrifugation and viably frozen.
Microarray Analysis Message RNA was amplified from patient blast cells and hybridized to the Affymetrix U133A chip, which can probe for approximately 22,000 genes (Fig.
13).
Chip data were pre-filtered to remove poor quality data and genes that were not expressed in at least 10% of the patient samples. In addition genes that did not vary across the dataset were removed (CV < 40%). Approximately, 8000 genes remained for further analysis. A total of 79 chips passed quality control measures and also had associated clinical response data.

Statistical Analysis Analysis of variance (ANOVA) and t-tests were used to investigate the effect of drug treatment and time and their interactions for each gene. Multiple hypotheses testing was controlled by applying the false discovery rate (FDR) algorithm.
All statistical analyses were performed in S-Plus 6.1 (Insightful Corporation).
Principal components analysis was performed in Partek Pro. Hierarchical clustering was performed using a correlation metric and complete linkage (OmniViz ProTM, OmniViz, Maynard, MA). Pathway analysis was performed using Gene Ontology functional classifications. Table 6 shows the results of the analysis.
Table 6. Functional Gene Classes significantly modulated by tipifarnib GO.ID GO.Class p.value 6886 Intracellular protein transport 5.75E-05 6951 Heatshock response 8.32E-05 6913 Nucleocytoplasmic transport 1.18E-04 6207 De novo pyrimidine base biosynthesis 1.58E-04 6809 Nitric oxide biosynthesis 1.58E-04 6376 mRNA splice site selection 2.05E-04 245 Spliceosome assembly 2.05E-04 6259 DNA metabolism 4.86E-04 6371 mRNA splicing 4.86E-04 6607 NLS-bearing substrate-nucleus import 5.64E-04 15031 Protein transport 6.05E-04 6338 Chromatin remodeling 9.87E-04 6397 mRNA processing 0.003201 7050 Cell cycle arrest 0.003685 8380 RNA splicing 0.003992 6512 Ubi uitin cycle 0.004639 6396 RNA processing 0.005096 398 Nuclear mRNA splicing, via spliceosome 0.007120 6916 Anti-a o tosis 0.007395 6118 Electron transport 0.010004 7049 Cell cycle 0.019631 6457 Protein folding 0.021817 7264 Small GTPase mediated signal 0.025281 transduction 8285 Negative regulation of cell proliferation 0.026482 6366 Transcription from Poll promoter 0.048613 Approximately 8000 genes were used for global unsupervised clustering.
Pretreatment 'samples clustered distally from during- and after-treatment samples.
After-treatment samples included those that were from patients up to 120 days following treatment termination. Figure 14 shows that AML samples maintain FTI-mediated global gene expression changes following termination of tipifarnib treatment.
502 genes were found to be differentially expressed after treatment with tipifarnib (p<0.005). These genes are listed in Tables 7A-7C and in more detail in Table 9.
Table 7A
SEQ ID NO psid SEQ ID NO psid SEQ ID NO psld 1SEQ ID NO pgld SEQ ID NO psld ZZOL6Z L95 bSL~2 505 LZOZ6Z 6~b 6L960Z ~8~ 6L980Z ~ZE
LL696Z 855 LZL~LZ 'VOS LOOZI.Z 9~t' Z9960Z Z8~ ZL980Z ZZ~
~~896Z 999 OOLE1- Z E05 98646Z 5~1 8Z960Z 1-8~ L9980Z 1-3~
t,8~91- Z t755 L89~6Z 305 9L61-6Z 17~17 589603 08~ 9S9803 03~
8b~92 ~S5 669E 2 605 8E6~2 ~~t, OZ960Z 8L~ ~V980Z 66~
LOZ96Z Z5S L99~6Z 009 Z~666Z Z~t' L0560Z LLC ~E980Z 8L~
1.699~Z 8bS Z09~6Z L6t, 6Z6~2 6Et7 Z6b60Z 9LC 6Z980Z LL~
~6756Z L'V5 V6b~6Z t76t, 85862 6Zb 4Lt,60Z tL~ 86580Z ~L~
66E56Z 5t,9 t.~~~6Z Z6t, LbL62 LZV 66760Z ZL~ 6ti580Z Z6~
08~52 t'ti5 L8Z~LZ G6t, 0~L1.6Z 5Zb 58~60Z LLE OZV80Z LL~
6L~9~Z ~tI5 ~SZ~6Z 0617 5b912 tZV LLfi60Z OL~ 06~80Z 80~
LZZ92 ZtlS 656~6Z 98t, L8b66Z ZZJF 69860Z 89E OLZ80Z LOC
6L694Z L b5 6Z6~6Z Z8t 517tl~ I.Z 6Zt, Z9~60Z L9C L6080Z 50E
Lb696Z 0tl5 bL08l.Z 68b OEv~6Z 0Zt' ~0~60Z C9C bL6LOZ b0E
9Eb92 6C9 Z90~6Z 08tl Z8602 LLt, 96Z60Z Z9~ St8LOZ ZO~
LZ~SIZ 8~5 690E6Z 6LV 69806Z 96t, Z8Z60Z 09C LLLLOZ 00~
LZ694Z L~5 Lti0~LZ BLt, Ob806Z KV 89Z60Z 85E ~LSLOZ 66Z
96096Z 9~5 8ZOE6Z LLt, 98LOLZ ELt, L5Z60Z L5~ 4L5LOZ 86Z
8EO9~Z bES b96Z2 5Lt, 98L06Z Z6b ZL~60Z 09C 899LOZ L6Z
OZ8tl6Z Z~5 LZ6Z2 tLt, 6b902 06V 8E1.60Z 6K 699LOZ 96Z
008tII.Z LE5 L06Z2 ELt' 6E902 60t, 68060Z 5'VE Ob9L0Z 56Z
699V2 8Z5 8L8Zt.Z ZLt, ~~901.Z 80t, 99060Z tltt'~ SE~LOZ t,6Z
L69b6Z LZ5 098Z2 LLt, 86902 90t, ~9060Z EtIE L8ZLOZ ~6Z
661717 6Z 9Z5 ~E8Z6Z OLt, ~95NZ 50t, ZZ060Z Zt'~ 99ZLOZ Z6Z
V6bb~Z 5Z5 L9LZ6Z 89b' ~5b06Z ~0t, 68680Z 6bE 59~LOZ WZ
LL~V6Z ~Z9 0~9UZ 99t, 8bbUZ Z0'V 9L680Z 6CC ~96LOZ 06Z
Z5~V2 ZZ5 E8SZ6Z t9t' b8Z0~Z 00t' 99680Z 8CC LZ4LOZ 68Z
88Zb~Z 6L5 8LSZ2 E9t, ~8Z06Z 66C 99680Z LEE ZLOLOZ L8Z
17931743 81.5 6L9321-917 5~3063 86~ 61.6803 5EE 8S6903 983 plsd ON a1 O3S P!sd ON aI 03s plsd ON ai 03s p!sd ION 01036Plsd ON al 03S
8L alqel bL890Z 58Z 9ti6bOZ tlZ ~50~OZ ~96 600ZOZ 96 b601.OZ 9t, 89890Z tl8Z 5b6bOZ ELZ bb0~OZ ~9L L8660Z 96 6t060Z 5t, 06L90Z E8Z L90VOZ 0LZ L56ZOZ Z9L 8~660Z t,6 LZ060Z ~t, ~ZL90Z Z8Z 996~OZ 90Z bSBZOZ 95L IZ640Z Z6 66060Z Ztl 869903 6LZ 096~03 503 8178303 551- 068WZ 68 666003 617 SbZ90Z 9LZ 9~6~OZ ~0Z ~tBZOZ t,56 M60Z 88 W600Z 0t, 66Z90Z 5LZ ZZ6~OZ Z0Z vZBZOZ Z51. Z1860Z L8 68600Z 6C
LL6903 tLZ ~68~03 1-03 69LZOZ 6171- 508603 98 6L6003 8~
09L90Z ~LZ Z88EOZ 666 bSLZOZ 9t, L bSL60Z V8 b9600Z L~
99090Z ~LZ LZ8~OZ L6L SELZOZ 5tl 6 8~L60Z E8 6b600Z 9~
L969OZ 0LZ 69L~OZ 966 Z69ZOZ 6tL 96L60Z Z8 V~600Z 5~
6178903 69Z 817L~03 566 ~L9303 0176 51L60Z 68 936003 v~
L089OZ 89Z EtiL~OZ b6L 6b9ZOZ 6~L 6691OZ 08 Z0600Z ~~
1L950Z L9Z bL9~OZ ~66 Zt79ZOZ 8~L 9L960Z 6L 89800Z Z~
bb990Z 99Z L99~OZ Z6l ZZ9ZOZ L~l 599WZ 8L L5800Z L~
L9b90Z 69Z 999~OZ (-66 665ZOZ 9~L 5~9LOZ LL C9800Z 0~
~0tl50Z 65Z 6Z9EOZ 06L Z85ZOZ 5EL OZ960Z 5L 9ti800Z 6Z
L9~SOZ 85Z u9~OZ 686 bbSZOZ ~U L6540Z t7L V~800Z 8Z
6~~50Z L5Z 069~OZ 886 cO9ZOZ Z~L 88960Z CL 10800Z LZ
SE~SOZ 55Z 685EOZ L8~ ZtbZOZ LZL 895LOZ ZL 08LOOZ 9Z
~Z~90Z b5Z l~S~OZ 986 ZEbZOZ 9ZL ~BbLOZ lL ZLLOOZ 5Z
6t OO19b0/SOOZSII/13d ObZ990/900Z OAd 0~-SO-LOOZ 99068930 FIO

tIEKZZ 699 9L58~Z 81.9 Z~b6ZZ 899 E9986Z L1.9 ESZI.ZZ L99 E1758LZ 969 ~tiL 4ZZ 999 Z8782 5l9 ZV60ZZ V99 L9b84Z b19 t,980ZZ ~99 Ltlb86Z E~9 6bLOZZ 199 56~82 1~69 bOibOZZ L99 EL~86Z Ol-9 6660ZZ 999 L9~81.Z 809 951OZZ t,59 O9~82 L09 0900ZZ E59 6~~82 909 Z500ZZ Z59 vE~8LZ 909 EZOOZZ L59 88Z8t.Z t709 LOOOZZ 099 ~8Z86Z ~09 90664Z 6t,9 08Z86Z Z09 66862 8t,9 9LZ8lZ 609 91861Z Lt,9 t~LZ82 009 59L66Z 9t,9 69Z86Z 669 65L62 9b9 95Z8bZ 869 9b561Z ~t,9 ~tiZ86Z L69 L0966Z ~V9 ~6Z86Z 969 90966Z Zt,9 886W 569 ~-8o!q-03-U-X=I=Id 90L LtE66Z 0t,9 5L1.86Z t,65 8W 9~6L6W/'d~-~JSIWfIH-X=I=Ib' V0L E6Z62 6C9 L6682 ~65 W 65EOOX/L0O'dSH-X=IJ'd EOL 98Z66Z 8~9 961.82 Z65 ~ 6S~OOX/LOOt/SH-X=I=Ib' Z0L ~9462 9C9 ~0686Z L69 bb969 WL 06664Z 5E9 85086Z 685 Z695S OOL 90662 17~9 6E086Z 885 60ZL~ S69 99062 ~~9 ~0082 L85 08ZZZZ 689 0~066Z 6E9 LL8L2 98S
~OZZZZ 889 9t,682 6Z9 998L2 1785 6996ZZ LL9 LE684Z 8Z9 098L6Z ~85 OZ92Z 9L9 9E6W LZ9 Et,8L2 L85 E692Z VL9 0~882 SZ9 ~EBL2 6L9 LL52Z ~L9 6L984Z CZ9 SZ8L2 8L5 6092Z 6L9 Ev98tZ LZ9 t,08L29L9 plSd :ON QI 03S p!Sd :ON dl 03S plSd :ON 41 b3S
OL alqel ELLL<-Z 5LS bZZKZ L65 499Z2 65t, ~EZ06Z L6E 50680Z b~E
69LL~Z tLS Eb6b6Z 965 6b5Z2 8517 08606Z 96C 170680Z ~E~
VSLL6Z ELS 6Z6ti6Z S65 45bZ2 95t, L~106Z V6~ 00680Z ZEE
05LL6Z ZL5 bZ6t,6Z bl5 9ZtZ6Z S5t, L6002 ~6~ b6880Z LEE
6t,LL2 6L5 L60ti6Z ~65 46~Z2 6tt, E6006Z Z6~ 4E880Z OE E
~6bLI.Z 0L5 6S0t,6Z Zl-5 L8ZZ6Z Lbtl L0660Z 66~ 61880Z 6Z~
9E~LI.Z L99 ~00KZ 6l5 LLZZ6Z tltltl 89860Z 06C 80880Z 8ZE
6ZEL2 999 6b6EtZ 0L5 69ZZ6Z E~tl 8E860Z 688 V9L80Z LZC
6tZLLZ V95 t-66E6Z 609 8tZZ6Z Zt7t7 LZ860Z 888 9VL80Z 9ZE
ZZ6LI.Z ~95 L98~6Z 805 40ZZ2 L tl'V t~L60Z 988 SbL80Z 5Z~
906LLZ Z99 1-68uZ 909 6822 01717 L8960Z tl8E 56980Z tlZE
OS
OO19b0/SOOZSII/13d ObZ990/900Z OAd 0~-SO-LOOZ 99068930 FIO

Included in this list were many genes involved in FTI biology including AKT1, CENPF, KRAS, RAF1, STATs and farnesyltransferase. Several gene functional categories were found to be significantly enriched in this gene list (Table 8).
Table 8.
Genes differentially expressed between responders and non-responders after treatment with ti ifarnib SEQ Probe set ID Gene function (GO) RE pre v PD pre v PD post ID NO: RE post 13 200044 RNA splicing U No difference 23 200661 Protein U No difference 85 201804 Protein folding Up No difference 123 202349 Protein folding Up No difference 137 202622 Nuclear Up No difference 149 202761 Cytoskeleton Up No difference 198 203845 transcription Down No difference 210 204067 Electron transport U No difference 218 204215 Membrane Down No difference 257 205339 Cell proliferation Down No difference 266 205644 RNA splicing Down No difference 268 205807 Bone Down No difference mineralization 290 207163 Signal transduction Up No difference 329 208819 Signal transduction Up No difference 336 208927 RNA U No difference 362 209295 Signal transduction Down No difference 428 211762 Cell cycle Down No difference 470 212833 Transport Down Down (more in PD) 521 214298 Cell cycle U U(more in PC) 549 215764 Transport Up Up (more in PD) 551 215905 RNA splicing Up No difference 598 218256 Transport Down No difference 610 218373 A o tosis Up No difference 619 218603 Cell cycle Down No difference 660 220671 transcription No down difference 698 44696 Signal transduction No Up difference 699 49485 Transcription U No difference We have previously identified 8 genes that were predictive of resistance to tipifarnib in relapsed and refractory AML. The most predictive gene in that dataset was AKAP-13 (AUC = 0.83).

The predictive value of these genes was tested in the current set of newly diagnosed AML samples (CTEP20). Samples from patients with complete response or progressive disease were used. The results are shown in Figure 15.
A predictive classifier was built from the top 6 genes since these gave less than a 50% error rate in the training set. This 6 gene classifier is shown in Table 9 and its ability to stratify newly diagnosed AML is shown in Figure 16.
Table 9 SEQ ID NO: PSID Symbol NTI7AUC CTEP20AUC
309 208325 AKAP13 0.830 0.464 151 202820 AHR 0.807 0.750 222 204362 SCAP2 0.777 0.893 496 213479 NPTX2 0.738 0.766 451 212384 BAT1 0.725 0.288 272 206148 IL3RA 0.705 0.714 411 210666 IDS 0.645 0.446 280 206637 P2RY14 0.627 0.589 Conclusions = 27% of the clinical bone marrow samples could be used indicating optimization of sample collection is required.
= Global gene expression signatures of AML cells indicated FTI-treatment resulted in stable gene expression changes following treatment termination.
= Approximately 500 genes (p < 0.005) were affected by tipifarnib reflecting the multiple pathways that are targeted by FTIs. These included many pathways previously associated with FTI biology.
= Differential expression of 27 genes was found between responders and non-responders following treatment with tipifarnib (Table 8). These are candidate PD
markers.
= A 6-gene classifier identified in relapsed and refractory AML was found to have predictive value in newly diagnosed AML

Example 8 Antibodies (Pro hn etic) An LBC oncogene-derived peptide is synthesized, coupled to keyhole limpet hemocyanin, and used to immunize rabbits for production of polyclonal antibodies.
The sera are tested for reactivity against the corresponding peptide with ELISA, and the positive batches are affinity-purified. The purified antibody specifically detects the peptide that has the epitope in tissue sections. This is verified by complete abolishment of the signal if the corresponding peptide is added simultaneously with the antibody. In addition to this polyclonal antibody, which works well in IHC, monoclonal antibodies able to detect the protein in its natural fold are produced. To produce monoclonal antibodies, a purified antigen, produced in mammalian cells to ensure natural fold and posttranslational modifications, is generated. The antigen, LBC onco protein-IgG constant part fusion protein, is expressed in mouse myeloma cells, and the protein is purified using the Fc part as bait. This purified antigen is recognized in Western blot by the C-terminal polyclonal antibody. The antigen is used to generate mouse monoclonal antibodies against LBC peptides by selecting out of the positive clones those that produce antibodies that react against LBC
peptide instead of the IgG constant part. Kits for the clinical identification of LBC
oncogene can be readily fashioned employing these and similar antibodies. Such kits would include antibodies directed to LBC peptide identification (and hence, LBC oncogene), appropriate indicator reagents (e.g., enzymes, labels, and the like), and (optionally) other reagents useful in the clinical application of such a kit such as dilution buffers, stabilizers, and other materials typically used in such assays.

Example 9 Immunohistochemistry (Prophetic) An affinity-purified polyclonal antibody against the C-terminal peptide of LBC
oncogene is used for the IHC detection and localization of LBC oncogene. Four m sections from formalin-fixed and paraffin embedded normal and tumor tissue is mounted on 3-aminopropyl-triethoxy-silane (APES, Sigma, St. Louis, MO) coated slides. The sections are deparaffinized and rehydrated in graded concentrations of ethanol and treated with methanolic peroxide (0.5% hydrogen peroxide in absolute methanol) for 30 minutes at room temperature to block the endogenous peroxidase activity. Antigen retrieval is done in a microwave oven twice for 5 minutes (650W).
An Elite ABC Kit (Vectastain, Vector Laboratories, Burlingame, CA) is used for immunoperoxidase staining. The LBC peptide antibody is used at an optimal dilution of 1:2000. Both the biotinylated second antibody and the peroxidase-labeled avidin-biotin complex are incubated on the sections for 30 minutes.
The dilutions are made in PBS (pH 7.2), and all incubations are carried out in a moist chamber at room temperature. Between the different staining steps the slides are rinsed three times with PBS. The peroxidase staining is visualized with a 3-amino-9-ethylcarbazole (Sigma) solution (0.2 mg/ml in 0.05 M acetate buffer containing 0.03% hydrogen peroxide, pH 5.0) at room temperature for 15 minutes. Finally, the sections are lightly counterstained with Mayer's haematoxylin and mounted with aqueous mounting media (Aquamount, BDH). In control experiments the primary antibodies are replaced with the IgG fraction of normal rabbit serum or the primary antibody was preabsorbed with the LBC peptide. These stainings indicate the presence of the LBC oncogene in a subset of cells.
Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, the descriptions and examples should not be construed as limiting the scope of the invention.

Table 10 Sequence Listing Description sEQ psid Description Name Accession ID
NO
1 AKAP13 forward primer NM 007209 2 AKAP13 reverse primer NM 001494 3 AKAP13 probe NM 000975 4 200002 ribosomal protein L35 RPL35 NM 007209 5 200008 GDP dissociation inhibitor 2 NM 001494 6 200010 ribosomal protein L11 NM 000975 7 200013 ribosomal protein L24 RPL24 NM 000986 8 200017 ribosomal protein S27a RPS27A NM 002954 9 200018 ribosomal protein S13 RPS13 NM 001017 10 200025 ribosomal protein L27 RPL27 NM 000988 11 200026 ribosomal protein L34 RPL34 NM 000995 12 200041 HLA-B associated transcri t-1 D6S81 E NM 004640 13 200044 s licin factor, arginineserine-rich 9 SFRS9 NM 003769 14 200056 hi hl similar to integrin alpha-7 FLJ12486 AK022548 15 200061 Similar to ribosomal protein S24 BC000523 16 200073 hnRNP-C like protein M94630 17 200086 cytochrome c oxidase subunit IV AA854966 19 200091 ribosomal protein S25 AA888388 20 200634 profilin 1 PFN1 NM 005022 21 200640 tyrosine 3-monooxygenasetryptophan 5- YWHAZ NM_003406 monooxygenase activation protein, zeta polypeptide 22 200643 high density li o rotein binding protein HDLBP NM 005336 23 200661 protective protein for beta-galactosidase PPGB NM 000308 24 200718 transcri tion elongation factor B SIII , polypep 1-like NM 003197 25 200772 prothymosin, alpha (gene sequence 28) NM 002823 26 200780 guanine nucleotide binding protein (G protein), alpha GNAS1 NM_000516 stimulating activity ol e tide 1 27 200801 actin, beta ACTB NM 001101 28 200834 ribosomal protein S21 RPS21 NM 001024 29 200846 protein phosphatase 1, catalytic subunit, a isoform PPP1 CA NM

30 200853 H2A histone family, member Z H2AFZ NM 002106 31 200857 nuclear receptor co-repressor 1 NCOR1 NM 006311 32 200858 ribosomal protein S8 RPS8 NM 001012 33 200902 15 kDa selenoprotein SEP15 NM 004261 34 200925 cytochrome c oxidase subunit Via polypeptide 1 COX6A1 NM 004373 35 200934 DEK oncogene (DNA binding) DEK NM 003472 36 200949 ribosomal protein S20 RPS20 NM 001023 37 200964 ubi uitin-activatin enzyme El UBE1 NM 003334 38 200971 stress-associated endoplasmic reticulum protein 1 SERP1 NM 014445 39 200981 neuroendocrine secretory protein 55 NESP55 NM 016592 40 200991 KIAA0064 gene product KIAA0064 NM 014748 41 200999 transmembrane protein (63kD), endoplasmic P63 NM_006825 reticulum Golgi intermediate compartment 42 201019 eukaryotic translation initiation factor 1A EIF1A NM 001412 43 201027 KIAA0741 gene product IF2 NM 015904 44 201042 trans lutaminase 2 AL031651 45 201049 ribosomal protein S18 RPS18 NM 022551 46 201094 ribosomal protein S29 RPS29 NM 001032 47 201104 DJ328E19.C1.1 NM 015383 48 201118 hos ho luconate deh dro enase PGD NM 002631 49 201134 c ochrome c oxidase subunit Vllc COX7C NM 001867 50 201163 insulin-like growth factor binding protein 7 IGFBP7 NM 001553 51 201195 L-t e amino acid trans orter 1 NM 003486 52 201212 protease, cysteine, 1 le umain NM 005606 53 201227 NADH deh dro enase 1 beta subcomplex, 8 NDUFB8 NM 005004 54 201244 v-raf-1 murine leukemia viral oncogene hom 1 RAF1 NM 002880 55 201249 solute carrier family 2 member 1 SLC2A1 NM 006516 56 201250 solute carrier family 2 member 1 SLC2A1 NM 006516 57 201273 signal recognition particle 9kD SRP9 NM 003133 58 201277 heterogeneous nuclear ribonucleoprotein AB HNRPAB NM 004499 59 201300 prion protein (p27-30) PRNP NM 000311 60 201305 acidic protein rich in leucines NM 006401 61 201317 roteasome (prosome, macro ain sub a type 2 PSMA2 NM 002787 62 201324 epithelial membrane protein 1 EMP1 NM 001423 63 201352 YME1 S. cerevisiae)-like 1 YME1 L1 NM 014263 64 201381 calcyclin binding protein NM 014412 65 201393 insulin-like growth factor 2 receptor IGF2R NM 000876 66 201403 microsomal glutathione S-transferase 3 MGST3 NM 004528 67 201429 ribosomal protein L37a RPL37A NM 000998 68 201445 calponin 3, acidic CNN3 NM 001839 69 201455 puromycin sensitive amino e tidase NM 006310 70 201472 von Hippel-Lindau binding protein 1 VBP1 NM 003372 71 201483 su ressor of Ty S. cerevisiae) 4 homolog I NM 003168 72 201568 low molecular mass ubiquinone-binding protein QP-C NM 014402 73 201588 thioredoxin-like, 32kD TXNL NM 004786 74 201597 c tochrome c oxidase subunit Vlla ol e 2 COX7A2 NM 001865 75 201620 membrane-bound transcription factor protease, site 1 MBTPS1 NM

76 201621 neuroblastoma, suppression of tumorigenicity 1 NBL1 NM 005380 77 201635 fra ile X mental retardation, autosomal hom 1 U25165 78 201665 ribosomal protein S17 RPS17 NM 001021 79 201675 A kinase PRKA anchor protein 1 AKAP1 NM 003488 80 201699 proteasome 26S sub ATPase 6 PSMC6 NM 002806 82 201716 sortin nexin 1 SNX1 NM 003099 83 201738 translation factor suil homolog GC20 NM 005875 84 201754 cytochrome c oxidase subunit Vlc COX6C NM 004374 85 201804 cytoskeleton-associated protein 1 CKAP1 NM 001281 86 201805 protein kinase, AMP-activated, y 1 non-catalytic sub PRKAG1 NM

87 201812 6.2 kd pro LOC54543 NM 019059 88 201818 hypothetical protein FLJ12443 FLJ12443 NM 024830 89 201890 ribonucleotide reductase M2 polypeptide NM 001034 90 201910 RhoGEF (ARHGEF) and pleckstrin domain protein 1 FARP1 NM 005766 91 201911 RhoGEF ARHGEF and pleckstrin domain protein 1 FARP1 NM 005766 92 201921 guanine nucleotide binding protein 10 GNG10 NM 004125 93 201934 hypothetical protein PR02730 NM 025222 94 201938 deleted in oral cancer 1 DOC1 NM 004642 95 201987 thyroid hormone receptor-assoc protein, 240 kD sub NM 005121 96 202001 NADH deh dro enase 1 a subcom lex, 6 NDUFA6 NM 002490 97 202029 ribosomal protein L38 RPL38 NM_000999 98 202077 NADH deh dro enase 1, alphabeta subcomplex, 1 NDUFAB1 NM 005003 99 202078 COP9 subunit 3 COPS3 NM 003653 100 202090 ubi uinol-c tochrome c reductase (6.4kD) sub UQCR NM 006830 101 202110 cytochrome c oxidase subunit Vllb COX7B NM 001866 102 202114 sorting nexin 2 SNX2 NM 003100 103 202141 COP9 homolog NM 006710 104 202154 tubulin, beta, 4 TUBB4 NM 006086 105 202163 CCR4-NOT transcription complex, subunit 8 CNOT8 NM 004779 106 202187 rotein hos hatase 2 re ulato subunit B a isoform PPP2R5A NM 006243 107 202197 m otubularin related protein 3 MTMR3 NM 021090 108 202219 solute carrier family 6, member 8 SLC6A8 NM 005629 109 202231 dendritic cell protein GA17 NM 006360 110 202233 ubi uinol-c ochrome c reductase hinge protein UQCRH NM 006004 111 202242 transmembrane 4 su erfamily member 2 TM4SF2 NM 004615 112 202275 lucose-6- hos hate deh dro enase G6PD NM 000402 113 202279 chromosome 14 open reading frame 2 C14ORF2 NM 004894 114 202285 tumor-associated calcium signal transducer 2 NM 002353 115 202286 tumor-associated calcium signal transducer 2 NM 002353 116 202287 tumor-associated calcium signal transducer 2 NM 002353 117 202298 NADH deh dro enase 1 alpha subcomplex, 1 NDUFAI NM 004541 118 202310 proalpha 1 I chain of type I procollagen NM 000088 119 202311 proalpha 1 I chain of type I procoliagen NM 000088 120 202312 colla en, type I, alpha 1 COL1A1 NM 000088 121 202324 ol i resident protein GCP60 GCP60 NM 022735 122 202325 ATP synthase, H+ transporting, mitochondrial FO ATP5J NM_001685 complex, subunit F6 123 202349 d stonia 1, torsion DYT1 NM 000113 124 202411 interferon, alpha-inducible protein 27 IF127 NM 005532 125 202423 zinc finger protein 220 ZNF220 NM 006766 126 202432 protein phosphatase 3 catalytic subunit, beta isoform PPP3CB NM

127 202442 adaptor-related protein complex 3, sigma 1 sub AP3S1 NM 001284 128 202458 protease, serine, 23 SPUVE NM 007173 129 202468 catenin a-like 1 CTNNALI NM 003798 130 202478 GS3955 protein GS3955 NM 021643 131 202481 short-chain deh dro enasereductase 1 SDR1 NM 004753 133 202544 glia maturation factor, beta GMFB NM 004124 134 202565 supervillin, transcript variant 1 SVIL NM 003174 135 202582 RAN binding protein 9 RANBPM NM 005493 136 202591 sin le-stranded DNA-binding protein SSBP NM 003143 137 202622 spinocerebellar ataxia 2 SCA2 NM 002973 138 202642 transformationtranscription domain-assoc protein TRRAP NM 003496 139 202649 ribosomal protein S19 RPS19 NM 001022 140 202673 dolichyl-phosphate mannosyltransferase polypeptide DPM1 NM003859 1, catal tic subunit 141 202692 upstream binding transcription factor, RNA UBTF NM_014233 polymerase I

142 202712 creatine kinase, mitochondrial 1 CKMT1 NM 020990 143 202723 forkhead box O1A FOXOIA NM 002015 144 202724 forkhead box 01 A FOXO 1 A NM 002015 145 202735 emo binding protein EBP NM 006579 146 202754 KIAA0029 protein KIAA0029 NM 015361 147 202759 A kinase (PRKA) anchor protein 2 AKAP2 NM 007203 148 202760 A kinase (PRKA) anchor protein 2 AKAP2 NM 007203 149 202761 s na tic nuclei expressed gene 2 KIAA1011 NM 015180 150 202789 hos holi ase C, y 1 NM 002660 151 202820 aryl hydrocarbon receptor AHR NM 001621 152 202824 transcription elongation factor B SIII , polypeptide 1 TCEB1 NM

153 202834 serine (or cysteine) proteinase inhibitor, clade A SERPINA8 member 8 154 202841 7-60 protein 7-60 NM 007346 155 202848 G protein-coupled receptor kinase 6 NM 002082 156 202854 hypoxanthine hos horibos Itransferase 1 HPRT1 NM 000194 157 202860 KIAA0476 gene product KIAA0476 NM 014856.

158 202889 microtubule-associated protein 7 NM 003980 159 202890 microtubule-associated protein 7 NM 003980 160 202947 I co horin C, transcript variant 1 GYPC NM 002101 161 202949 four and a half LIM domains 2 FHL2 NM 001450 162 202957 hematopoietic cell-specific Lyn substrate 1 HCLS1 NM 005335 163 203044 KIAA0990 protein KIAA0990 NM 014918 164 203053 breast carcinoma amplified sequence 2 BCAS2 NM 005872 165 203133 protein translocation complex beta SEC61 B NM 006808 166 203138 histone acetyltransferase 1 HAT1 NM 003642 167 203139 death-associated protein kinase 1 DAPK1 NM 004938 168 203140 B-cell CLLI m homa 6 BCL6 NM 001706 169 203142 adaptor-related protein complex 3, 131 subunit AP3B1 NM 003664 170 203211 KIAA1073 protein KIAA1073 NM 016156 171 203213 cell division cycle 2, G1 to S and G2 to M NM 001786 172 203255 vitiligo-associated protein VIT-1 VIT1 NM 018693 173 203262 chromosome X uni ue 9928 expressed sequence DXS9928E NM 004699 174 203287 ladinin 1 LAD1 NM 005558 175 203316 small nuclear ribonucleoprotein ol e tide E SNRPE NM_003094 176 203332 inositol pol hos hate-5- hos hatase, 145kD INPP5D NM 005541 177 203362 MAD2 mitotic arrest deficient, yeast, homolo -like 1 MAD2L1 NM

178 203371 NADH deh dro enase 1 beta subcomplex, 3 NDUFB3 NM 002491 179 203385 diac I I cerol kinase, alpha DGKA NM 001345 180 203396 proteasome (prosome, macro ain subunit, a type, 4 PSMA4 NM 002789 181 203437 putative receptor protein PMI NM 003876 182 203460 presenilin 1, transcript variant 1-463 PSEN1 NM 007318 183 203484 Sec6l gamma SEC61 G NM 014302 184 203514 mitogen-activated protein kinase kinase kinase 3 NM 002401 185 203528 sema domain, Ig domain, TM domain and short SEMA4D NM_006378 c o lasmic domain, 4D

186 203531 cullin 5 NM 003478 187 203581 RAB4, member RAS onco ene family NM 004578 188 203610 ring finger protein 15 NM 006355 189 203613 NADH deh dro enase 1 beta subcomplex, 6 NDUFB6 NM 002493 190 203621 NADH deh dro enase 1 beta subcomplex, 5 NDUFB5 NM 002492 191 203666 stromal cell-derived factor 1 SDF1 NM 000609 192 203667 tubulin-specific chaperone a TBCA NM 004607 193 203674 KIAA0054 gene product; Helicase KIAA0054 NM 014877 194 203743 thymine-DNA I cos lase TDG NM 003211 195 203748 RNA binding motif, single stranded interacting RBMS1 NM_016839 rotein 1, transcript variant MSSP-2 196 203761 Src-like-adapter SLA NM 006748 197 203827 hypothetical protein FLJ10055 FLJ10055 NM 017983 198 203845 p300CBP-associated factor NM 003884 199 203882 interferon-stimulated transcription factor 3, y ISGF3G NM 006084 200 203886 fibulin 2 FBLN2 NM 001998 201 203893 adrenal gland protein AD-004 LOC51578 NM 016283 202 203922 cytochrome b-245, beta polypeptide NM 000397 203 203936 matrix metalloproteinase 9 MMP9 NM 004994 204 203940 KIAA1036 protein KIAA1036 NM 014909 205 203960 HSPCO34 protein LOC51668 NM 016126 206 203965 ubi uitin s ecific protease 20 USP20 NM 006676 207 204014 dual specificity phosphatase 4 DUSP4 NM 001394 208 204015 dual specificity phosphatase 4 DUSP4 NM 001394 209 204049 KIAA0680 ene product KIAA0680 NM 014721 210 204067 sulfite oxidase NM 000456 211 204082 pre-B-cell leukemia transcription factor 3 PBX3 NM 006195 212 204141 tubulin, beta polypeptide TUBB NM 001069 213 204145 FSHD re ion ene 1 FRG1 NM 004477 214 204146 RAD51-interactin protein NM 006479 215 204158 T-cell, immune regulator 1 TCIRG1 NM 006019 216 204168 microsomal glutathione S-transferase 2 MGST2 NM 002413 217 204170 CDC28 protein kinase 2 CKS2 NM 001827 218 204215 hypothetical protein MGC4175 NM 024315 219 204294 aminomethyltransferase AMT NM 000481 220 204351 S100 calcium-binding protein P S100P NM 005980 221 204361 SKAP55 homologue SKAP-Hom NM 003930 222 204362 SKAP55 homologue SKAP-Hom NM 003930 223 204411 KIAA0449 protein KIAA0449 NM 017596 224 204416 a o{i o rotein C-I APOC1 NM 001645 225 204528 nucleosome assembly protein 1-like 1 NAP1L1 NM 004537 226 204640 s eckle-t e POZ protein SPOP NM 003563 227 204642 endothelial differentiation, sphingolipid G-protein- EDG1 NM_001400 cou led rece tor, 1 228 204652 nuclear res irato factor 1 NRF1 NM 005011 229 204694 al ha-feto rotein AFP NM 001134 230 204698 interferon stimulated gene ISG20 NM 002201 231 204729 syntaxin 1A STX1A NM 004603 232 204766 nudix -t pe motif 1 NUDT1 NM 002452 233 204767 flap structure-specific endonuclease 1 NM 004111 234 204777 T-cell differentiation protein, transcript variant a MAL NM 002371 235 204863 interleukin 6 signal transducer NM 002184 236 204864 interleukin 6 signal transducer IL6ST NM 002184 237 204881 UDP-glucose ceramide glucosyltransferase UGCG NM 003358 238 204885 mesothelin, transcript variant 1 MSLN NM 005823 239 204905 eukaryotic translation elongation factor 1 epsilon 1 EEF1 E1 NM

240 204923 753P9 on chromosome Xq25-26.1 NM 018990 241 204950 KIAA0955 protein KIAA0955 NM 014959 242 204951 ras homolog gene family, member H ARHH NM 004310 243 204955 sushi-re eat-containin protein, X chrom SRPX NM 006307 244 204966 brain-specific an io enesis inhibitor 2 BA12 NM 001703 245 204989 integrin, beta 4 ITGB4 NM 000213 246 204990 integrin, beta 4 ITGB4 NM 000213 247 205033 defensin, alpha 1, myeloid-related sequence DEFA1 NM 004084 248 205087 DKFZP566KO23 protein NM 015485 249 205108 a oli o rotein B APOB NM 000384 250 205133 heat shock lOkD protein 1 HSPE1 NM 002157 251 205176 integrin beta 3 binding protein ITGB3BP NM 014288 252 205213 KIAA0050 gene product ACAP1 NM 014716 253 205270 I m hoc te cytosolic protein 2 LCP2 NM 005565 254 205323 metal-re ulato transcription factor 1 MTF1 NM 005955 255 205335 signal recognition particle 19kD SRP19 NM 003135 256 205336 parvalbumin PVALB NM 002854 257 205339 TALl (SCL) interru tin locus SIL NM 003035 258 205361 prefoldin 4 U41816 259 205403 interleukin 1 receptor, type II IL1 R2 NM 004633 260 205453 homeo box B2 HOXB2 NM 002145 261 205467 caspase 10 CASP10 NM 001230 262 205600 homeo box B5 HOXB5 NM 002147 263 205601 homeo box B5 HOXB5 NM 002147 264 205608 an io oietin 1 NM 001146 265 205609 an io oietin 1 ANGPT1 NM 001146 266 205644 small nuclear ribonucleoprotein polypeptide G SNRPG NM 003096 267 205671 MHC, class II DO HLA-DOB NM 002120 268 205807 tuftelin 1 TUFT1 NM 020127 269 205849 gamma-aminobutyric acid A receptor 3, trans var 1 GABRB3 NM 006294 270 205967 H4 histone family, member G H4FG NM 003542 271 206066 RAD51 S. cerevisiae) homolog C RAD51C NM 002876 272 206148 interleukin 3 receptor, alpha IL3RA NM 002183 273 206150 tumor necrosis factor receptor su erfamil , mem 7 TNFRSF7 NM 001242 274 206177 arginase, liver ARG1 NM 000045 275 206219 vav 1 oncogene VAV1 NM 005428 276 206245 NS1-bindin protein NS1-BP NM 006469 277 206289 homeo box A4 HOXA4 NM 002141 278 206298 hypothetical protein from s 23549 and 23762 LOC58504 NM 021226 279 206618 interleukin 18 receptor 1 IL18R1 NM 003855 281 206674 fms-related tyrosine kinase 3 FLT3 NM 004119 282 206723 G protein-coupled receptor Edg-4 AF233092 283 206790 NADH deh dro enase 1 beta subcomplex, 1 NDUFB1 NM 004545 284 206868 KIAA0189 gene product KIAA0189 NM 014725 285 206874 Ste20-related serinethreonine kinase NM 014720 287 207072 interleukin 18 receptor accessory protein IL18RAP NM 003853 288 207111 egf-like module containing, mucin-like, hormone EMR1 NM001974 receptor-like sequence 1 289 207127 heterogeneous nuclear ribonucleoprotein H3 HNRPH3 NM 021644 290 207163 v-akt murine thymoma viral oncogene hom 1 AKT1 NM 005163 291 207165 hyaluronan-mediated motility receptor trans var 2 HMMR NM 012485 292 207266 RNA binding motif, single stranded interacting RBMS1 NM_016837 protein 1, transcript variant MSSP-3 293 207287 h othetical protein FLJ14107 FLJ14107 NM 025026 294 207335 solute carrier family 1 member 7 SLC1A7 NM 006671 295 207540 spleen tyrosine kinase SYK NM 003177 296 207551 male-specific lethal-3 Droso hila -like 1 MSL3L1 NM 006800 297 207568 choliner ic receptor, nicotinic, a polypeptide 6 CHRNA6 NM 004198 298 207571 basement membrane-induced gene ICB-1 NM 004848 299 207573 ATP synthase, H+ transporting, mitochondrial Fl FO, ATP5JG
NM_006476 subunit 300 207777 nuclear body protein Sp140 SP140 NM 007237 301 207826 inhibitor of DNA binding 3, dominant negative helix- ID3 NM_002167 loo -helix protein 302 207845 anaphase-promoting complex 10 APC10 NM 014885 303 207935 keratin 13 KRT13 NM 002274 304 207974 S-phase kinase-associated protein 1A 19A SKP1A NM 006930 305 208091 h othetical protein DKFZ 564K0822 NM 030796 306 208130 thromboxane A synthase 1 TBXAS1 NM 030984 307 208270 arginyl amino e tidase RNPEP NM 020216 308 208310 follistatin-like 1 FSTLI NM 007085 309 208325 I m hoid blast crisis oncogene LBC NM 006738 310 208414 homeo box B3 HOXB3 NM 002146 311 208420 su ressor of Ty (S.cerevisiae) 6 homolog SUPT6H NM 003170 312 208549 roth mosin a14 LOC51685 NM 016171 313 208598 upstream re ulato element binding protein 1 UREB1 NM 005703 314 208621 villin 2 ezrin NM 003379 315 208622 villin 2 ezrin NM 003379 316 208623 cytovillin 2 VIL2 NM 003379 317 208629 hydroxyacyl-Coenzyme A dehydrogenase3-ketoacyl- U04627 Coenzyme A thiolaseenoyl-Coenzyme A hydratase al ha subunit 318 208633 actin binding protein; macrophin AB029290 319 208643 Ku autoimmune antigen gene J04977 320 208656 cyclin I CYC1 AF135162 321 208667 putative tumor suppressor ST13 ST13 U17714 322 208672 s licin factor, arginineserine-rich 3 BC000914 324 208695 ribosomal protein L39 RPL39 BC001019 325 208745 ATP synthase, H+ transporting, mitochondrial Fl FO, AA917672 subunit 326 208746 Fl FO-type ATP synthase subunit AF070655 327 208754 DKFZp 762G106 AL162068 328 208808 hi ility group protein 2 BC000903 329 208819 mel transforming oncogene - RAB8 homolog BC002977 331 208894 MHC class II HLA-DR-alpha M60334 332 208900 topoisomerase I W025108 333 208904 ribosomal protein S28 RPS28 BC000354 334 208905 cytochrome c BC005299 335 208919 Similar to hypothetical protein FLJ13052 BC001709 336 208927 s eckle-t e POZ protein BC001269 337 208956 deoxyuridine tri hos hatase DUT U62891 338 208966 interferon, gamma-inducible protein 16 AF208043 339 208975 importin beta subunit L38951 340 208977 tubulin, beta, 2 BC004188 341 208981 plateletendothelial cell adhesion molecule NM 000442 342 209022 hi hl similar to autonomously re licatin sequence ARS AK026678 343 209063 ol aden late binding protein-interacting protein 1 BF248165 344 209066 mitochondrial ubi uinone-bindin protein M26700 345 209089 RAB5A, member RAS oncogene family BC001267 346 209119 nuclear receptor subfamily 2, group F, mem 2 AV703465 347 209120 nuclear receptor subfamily 2, group F, mem 2 AV703465 348 209122 adi ose differentiation-related protein NM 001122 349 209138 immunoglobulin lambda locus FLM87790 350 209172 centromere protein F U30872 351 209191 Similar to tubulin, beta, 4 BC002654 352 209209 mito en inducible gene mig-2 AW469573 353 209210 mitogen inducible gene_ mig-2 Z24725 354 209227 Putative prostate cancer tumor suppressor NM 006765 355 209228 Putative prostate cancer tumor suppressor NM 006765 356 209239 nuclear factor of kappa light polypeptide gene p105 M55643 enhancer in B-cells 1 357 209257 chondroitin sulfate proteoglycan 6 bamacan NM 005445 358 209258 chondroitin sulfate roteo I can 6 bamacan NM 005445 359 209270 laminin S B3 chain LAMB3 NM 000228 360 209282 protein kinase D2 NM 016457 361 209289 nuclear factor IB NM 005596 362 209295 TRAIL receptor 2 AF016266 363 209303 NADH deh dro enase Fe-S protein 4 NM 002495 364 209309 zinc-al ha2- I co rotein D90427 365 209324 retinally abundant regulator of G-protein signalling 16 hRGS-r NM

366 209325 retinally abundant regulator of G-protein si nalin hRGS-r NM 002928 367 209362 suppressor of RNA polymerase B, yeast homolog SRB7 NM 004264 368 209369 1,2-c clic-inositol- hos hate phosphodiesterase ANX3 NM 005139 369 209372 tubulin, beta polypeptide BC001352 370 209377 thyroid hormone receptor interactor 7 AF274949 371 209385 proline synthetase co-transcribed AL136616 372 209411 ADP-ribosylation factor binding protein GGA3 AF219139 373 209443 proteinase inhibitor, clade A member 5 NM 000624 374 209471 farnes I rotein transferase alpha-subunit NM 002027 375 209492 ATP synthase, H+ transporting, mitochondrial FO BC003679 complex, subunit e 376 209500 tumor necrosis factor su erfamil , member 13 TNSF13 NM 003808 377 209507 replication protein A3 NM 002947 378 209520 nuclear cap binding protein subunit 1 NM 002486 379 209560 delta-like homolog NM 003836 380 209585 multiple inositol ol hos hate phosphatase NM 004897 381 209628 hypothetical protein P15-2 NM 018698 382 209662 centrin, EF-hand protein, 3 NM 004365 383 209679 hypothetical protein from 643 NM 020467 384 209687 stromal cell-derived factor 1 U19495 385 209730 sema domain, Ig, short basic domain, secreted, 3F U38276 386 209734 hemato oietic protein I NM 005337 387 209810 pulmonary surfactant-associated protein B SP-B NM 000542 388 209827 interleukin 16 IL16 NM 004513 389 209838 thyroid receptor interacting protein 15 AF212227 390 209868 RNA binding motif, single stranded interacting prot 1 SCR2 NM

391 209907 intersectin 2 long isoform ITSN2 AF182198 392 210093 ma o-nashi homolog, proliferation-associated NM 002370 393 210097 retinoic acid repressible protein AF130102 394 210137 dCMP deaminase BC001286 395 210139 peripheral myelin protein 22 GAS3 L03203 396 210180 s licin factor, arginineserine-rich 10 U87836 397 210233 interleukin 1 receptor accessory protein IL1 RAP AF167343 398 210235 LAR-interactin protein la U22815 399 210283 Sim to poly aden late binding protein-interacting I BC005295 400 210284 TAK1-binding protein 2 AF241230 401 210347 C2H2-t e zinc-finger protein AF080216 402 210448 ionotropic ATP receptor P2X5b U49396 403 210453 DKFZp566GO13 AL050277 404 210510 soluble neuropilin-1 AF145712 405 210563 FLICE-like inhibitory protein short form U97075 407 210615 neuropilin-1 soluble isoform 11 NRP1 AF280547 408 210633 acidic keratin-10 KRT10 M19156 409 210639 a o tosis-related protein APG5L AF293841 410 210649 BRG1-Associated Factor 250a BAF250a AF231056 411 210666 iduronate-2-sulfatase IDS NM 000202 412 210785 basement membrane-induced gene ICB-1 beta AB035482 413 210786 Friend leukemia virus integration 1 FLI-1 M93255 414 210840 IQ motif cont'g GTPase activating protein 1 D29640 415 210854 GABAnoradrenaline transporter U17986 416 210859 CLN3 protein CLN3 AF077973 417 210982 MHC class II HLA-DRA HLA-DRA M60333 418 211000 gp130 of the rheumatoid arthritis antigenic peptide- gp130-RAPS

bearing soluble form 419 211133 leukocyte lg-like receptor subfamily B member 3 LILRB3 NM 00864 420 211430 anti-hepatitis A I G M87789 422 211487 ribosomal protein S17 BC004886 423 211535 fibroblast growth factor receptor FGFR M60485 424 211645 immuno lobulin kappa-chain VK-1 I K M85256 425 211730 ol merase (RNA) II (DNA directed) ol e tide L BC005903 426 211743 roteo I can 2, bone marrow BC005929 427 211747 U6 snRNA-associated Sm-like protein BC005938 428 211762 ka o herin alpha 2 BC005978 429 211858 guanine nucleotide-binding protein Gs a sub iso L2 AF088184 430 211915 beta-tubulin TUB4 U83110 431 211921 fetal thymus prothymosin alpha AF348514 432 211932 heterogeneous nuclear protein similar to rat helix BE867771 destabilizing protein 433 211938 hypothetical protein PR01843 BF247371 434 211976 FLJ21862fis AK026168 435 211985 matrix Gla protein A1653730 436 212007 UBX domain-containing 1 D87684 439 212027 S164 protein BE466128 440 212181 di hos hoinositol polyphosphate hos hoh drolase2 NUDT4 AF191654 441 212201 KIAA0692 protein AB014592 442 212248 FLJ20738 fis A1972475 443 212269 minichromosome maintenance deficient 3-assoc GANP AJ010089 444 212277 KIAA0647 protein AB014547 445 212283 est:t 49b04.x1 AF016903 446 212285 est:t 49b04.x1 AF016903 447 212287 KIAA0160 protein BF382924 448 212298 neuropilin 1 NM 003873 449 212311 KIAA0746 protein AB018289 450 212382 FLJ11918fis AK021980 451 212384 HLA-B associated transcri t-1 BG341380 452 212385 FLJ11918 fis AK021980 453 212386 FLJ11918fis AK021980 454 212387 FLJ11918 fis AK021980 455 212426 tyrosine 3-monooxygenasetryptophan 5- BF033313 monooxygenase activation protein, theta polypeptide 457 212531 lipocalin 2 LCN2 NM 005564 458 212549 DKFZp586N1323 A1149535 463 212578 ribosomal protein S17 BF026595 464 212583 KIAA0560 gene product AB011132 465 212630 sec6 homolog AF055006 466 212664 tubulin, beta, 5 NM 006087 467 212740 hos hoinositide-3-kinase, re ulato sub 4 p150 BF740111 468 212757 FLJ22656fis BF111268 469 212771 DKFZp564AO26 AU150943 470 212833 TB1 ene M74089 472 212878 kinesin 2 AA284075 473 212907 hbc647 A1972416 476 213020 DKFZp566B213 GOSR1 NM 004871 477 213028 nuclear factor related to kappa B binding protein A1887378 478 213047 SET translocation AI278616 482 213129 I cine cleava e system protein H BE908931 483 213134 BTG family, member 3 BTG3 NM -006806 484 213147 homeo box A10 NM 018951 485 213150 homeo box A10 NM 018951 488 213188 Weakly similar to T15138 hyp protein T28F2.4 NM 032778 489 213231 d stro hia myotonica-containing WD repeat motif L19267 490 213253 structural maint of chromosomes 2 yeast-like 1 SMC2L1 AU154486 491 213287 ene for acidic (type I cytokeratin 10 X14487 492 213311 KIAA1049 protein BF000251 494 213414 ribosomal protein S19 BE259729 495 213423 Putative prostate cancer tumor suppressor A1884858 496 213479 neuronal pentraxin li NPTX2 NM 002523 497 213502 immuno lobulin lambda-like polypeptide 3 X03529 498 213539 CD3D antigen, delta polypeptide (TiT3 com lex CD3D NM 000732 499 213553 a oli o rotein C-1 W79394 500 213567 23728 sequence BF431965 501 213599 O a-interactin protein 5 BE045993 502 213687 uncharacterized hypothalamus protein HSMNP1 BE968801 503 213700 hypothetical protein FLJ10803 AA554945 504 213727 h othetical protein FLJ1 1585 A1743654 505 213754 ol aden late binding protein-interacting protein 1 AW613203 506 213811 transcri tion factor 3 BG393795 507 213843 accessory proteins BAP31 BAP29 AW276522 508 213867 actin, AA809056 509 213911 H2A histone famil , member Z BF718636 510 213941 ribosomal protein S7 A1970731 511 214003 ribosomal protein S20 BF184532 512 214051 thymosin, beta BF677486 513 214097 ribosomal protein S21 AW024383 516 214143 ribosomal protein L24 A1560573 517 214224 rotein NIMA-interacting, 4 parvulin BE674061 518 214264 est:tt46h03 A1656610 519 214288 proteasome (prosome, macro ain subunit, type, 1 W86293 520 214292 inte rin, beta 4 AA808063 521 214298 se tin 2 AL568374 522 214352 v-Ki-ras2 Kirsten rat sarcoma 2 viral oncogene hom BF673699 523 214377 est:Ul-H-B14-ao -e-12-0-UI.s1 BF508685 524 214433 selenium binding protein 1 SELENBP1 NM 003944 525 214494 cell matrix adhesion regulator CMAR NM 005200 526 214499 Bcl-2-associated transcription factor short form AF249273 527 214617 erforin 1 A1445650 528 214661 near HD on 4 16.3 with hom to hyp S. pombe gene R06783 529 214696 24659 sequence AF070569 531 214800 basic transcription factor 3 R83000 532 214820 transcri tional unit N143 AJ002572 534 215038 Huntin tin interacting protein AF049103 535 215073 nuclear receptor subfamily 2, group F, mem 2 AL554245 536 215096 esterase Dform I lutathione hydrolase AU145746 537 215121 immunoglobulin lambda locus AA680302 538 215127 RNA binding motif, single stranded interacting prot 1 AL517946 540 215147 23712 se uence AF007147 542 215227 acid hos hatase 1, soluble BG035989 543 215379 immunoglobulin lambda joining 3 AV698647 544 215380 FLJ11717 fis AK021779 545 215399 amplified in osteosarcoma A1683900 546 215446 I s I oxidase LOX L16895 547 215493 3 end of the BTN2A1 gene encoding but ro hilin 2A AL121936 548 215691 HSPCO34 protein AV702994 549 215764 ada tor-related protein complex 2, alpha 2 subunit AA877641 550 215812 creatine trans orter SLC6A1O U41163 551 215905 U5 snRNP-specific 40 kDa protein AL157420 552 216207 immunoglobulin kappa variable 1-13 AW408194 553 216348 transcri tion factor AP-2 beta AL049693 554 216384 roth mosin alpha PTMA AF257099 555 216641 ladinin LAD U58994 556 216833 I co horin HeP2 U05255 557 216899 PAC RP5-1139P1 from 7 15- 21 AC003999 558 216977 U2 snRNP-specific A protein, alternative transcript 3 AJ130972 559 217013 PAC RP4-604G5 from 7 22- 31.1 AC004522 560 217014 PAC RP4-604G5 from 7 22- 31.1 AC004522 561 217022 1 A1-A2 lambda hybrid GAU heavy chain S55735 562 217106 utative dimethyladenosine transferase AF091078 563 217122 Matrix Metallo roteinase Female Rep tract MIFR1 2 MMP2122A AL031282 564 217249 BAC CTB-162B4 from 4 AC004544 565 217293 an io oietin 1 AF209975 566 217329 cytochrome c oxidase subunit Vllb pseudogene COX7BP1 AF042164 567 217336 RP5-858M22 on chromosome 20 AL118510 568 217410 FLJ11524 fis AK021586 569 217419 FLJ 11524 fis AK021586 570 217491 cytochrome c oxidase subunit Vllc pseudogene COX7CP1 AF042165 571 217749 coat rotein gamma-cop LOC51137 NM 016128 572 217750 hypothetical protein FLJ13855 NM 023079 573 217754 putative nucleolar RNA helicase NOH61 NM 019082 574 217769 h othetical protein HSPCO14 NM 015932 575 217773 NADH deh dro enase 1 alpha subcomplex, 4 NDUFA4 NM 002489 576 217801 ATP synthase, H+ transporting, mitochondrial Fl ATP5E NM006886 com lex, epsilon subunit 577 217812 hi h lucose-re ulated protein 8 HGRG8 NM 016258 578 217825 CGI-76 protein AF151039 579 217833 NS1-associated protein I NM 006372 581 217843 HSPC126 protein HSPC126 NM 014166 582 217853 h othetical protein FLJ13732 similar to tensin TENC1 NM 022748 583 217860 NADH deh dro enase 1 alpha subcomplex, 10 NDUFAIO NM 004544 584 217866 h othetical protein FLJ12529 FLJ12529 NM 024811 585 217875 transmembrane, prostate androgen induced RNA TMEPAI NM 020182 586 217877 hypothetical protein SP192 SP192 NM 021639 587 218003 FK506-binding protein 3 FKBP3 NM 002013 589 218058 CpG binding protein CGBP NM 014593 590 218062 Cdc42 effector protein 4; binder of Rho GTPases 4 CEP4 NM 012121 591 218103 h othetical protein FLJ20062 FLJ20062 NM 017647 592 218116 hepatocellular carcinoma-associated anti en 59 LOC51759 NM 016520 593 218117 ring-box 1 RBX1 NM 014248 594 218175 hypothetical protein FLJ22471 FLJ22471 NM 025140 595 218188 translocase of inner mitochondrial membrane 13 TIMM13B NM012458 (yeast) homolog B

596 218213 chromosome 11 open reading frame 10 C11orf10 NM 014206 597 218241 ol i autoantigen, golgin subfamily a, 5 GOLGA5 NM 005113 598 218256 nucleo orin p54 NUP54 NM 017426 599 218259 KIAA1243 protein KIAA1 243 NM 014048 600 218274 h othetical protein FLJ10415 FLJ10415 NM 018089 601 218276 WW Domain-Containing Gene WW45 NM 021818 602 218280 H2A histone family, member 0 H2AFO NM 003516 603 218283 kiaa-iso protein LOC51188 NM 016305 604 218288 h othetical protein MDS025 MDS025 NM 021825 605 218334 hypothetical protein FLJ23445 FLJ23445 NM 025075 606 218339 HSPC158 protein HSPC158 NM 014180 607 218350 geminin LOC51053 NM 015895 608 218367 ubi uitin specific protease 21 USP21 NM 012475 609 218368 type I transmembrane protein Fn14 FN14 NM 016639 610 218373 hyp protein FLJ13258 similar to fused toes FLJ13258 NM 022476 611 218395 h othetical protein FLJ13433 FLJ13433 NM 022496 612 218397 h othetical protein FLJ10335 FLJ10335 NM 018062 613 218447 DC13 rotein DC13 NM 020188 614 218467 x 003 rotein MDS003 NM 020232 615 218482 DC6 rotein DC6 NM 020189 616 218543 h othetical protein FLJ22693 FLJ22693 NM 022750 617 218563 NADH deh dro enase 1 alpha subcomplex, 3 NDUFA3 NM 004542 618 218576 dual s ecificity phosphatase 12 DUSP12 NM 007240 619 218603 homolog of Drosophila headcase hHDC NM 016217 620 218605 hypothetical protein FLJ23182 FLJ23182 NM 022366 621 218643 postsyn a tic protein CRIPT CRIPT NM 014171 622 218660 dysferlin, limb girdle muscular dystrophy 2B DYSF NM 003494 623 218671 ATPase inhibitor precursor LOC51189 NM 016311 624 218801 UDP- lucose: I co rotein glucosyltransferase 2 FLJ10873 NM 020121 625 218830 ribosomal protein L26 homolog LOC51121 NM 016093 626 218873 h othetical protein FLJ20203 FLJ20203 NM 017710 627 218936 HSPC128 protein HSPC128 NM 014167 628 218937 hypothetical protein FLJ20417 FLJ20417 NM 017810 629 218946 HIRIP5 rotein HIRIP5 NM 015700 630 219008 h othetical protein FLJ21820 FLJ21820 NM 021925 631 219030 CGI-121 protein LOC51002 NM 016058 632 219032 o sin 3 ence halo sin OPN3 NM 014322 633 219056 h othetical protein FLJ11712 FLJ11712 NM 024570 634 219105 ori in reco nition complex, subunit 6-like ORC6L NM 014321 635 219110 GAR1 rotein GAR1 NM 018983 636 219163 h othetical protein FLJ20079 FLJ20079 NM 017656 637 219218 h othetical protein FLJ23058 FLJ23058 NM 024696 638 219286 h othetical protein FLJ12479 FLJ12479 NM 022768 639 219293 h othetical protein PTD004 NM 013341 640 219347 h othetical protein FLJ10956 FLJ10956 NM 018283 641 219452 putative di e tidase LOC64174 NM 022355 642 219506 hypothetical protein FLJ23221 FLJ23221 NM 024579 643 219507 hypothetical protein LOC51319 NM 016625 644 219546 hypothetical protein DKFZ 434P0116 NM 017593 645 219759 amino e tidase LOC64167 NM 022350 646 219765 hypothetical protein FLJ12586 FLJ12586 NM 024620 647 219816 hypothetical protein FLJ10482 FLJ10482 NM 018107 648 219819 HSPCO07 protein HSPCO07 NM 014018 649 219906 hypothetical protein FLJ10213 FLJ10213 NM 018029 650 220001 peptidyl arginine deiminase, type V PAD NM 012387 651 220023 a oli o rotein B48 receptor APOB48R NM 018690 652 220052 TERF1 TRF1 )-interacting nuclear factor 2 TINF2 NM 012461 653 220060 hypothetical protein FLJ20641 FLJ20641 NM 017915 654 220155 h othetical protein FLJ13441 FLJ13441 NM 023924 655 220199 hypothetical protein FLJ12806 FLJ12806 NM 022831 656 220386 chromosome 2 open reading frame 2 C2ORF2 NM 019063 657 220404 PR00611 protein PR00611 NM 014076 658 220416 hypothetical protein FLJ21472 FLJ21472 NM_024837 659 220558 pan-hematopoietic expression PHEMX NM 005705 660 220671 carbon catabolite repression 4-like CCRN4L NM 012118 661 220741 inor anic ro hos hatase SID6-306 NM 006903 662 220755 G8 protein G8 NM 016947 663 220864 CGI-39 rotein; cell death-re ulato protein GRIM19 LOC51079 NM

664 220942 hypothetical protein, estradiol-induced E21G5 NM 014367 665 221009 PPAR amma an io oietin related protein PGAR NM 016109 666 221143 re lication protein A comp 34 kd subunit hom Rpa4 HSU24186 NM

667 221253 hypothetical protein MGC3178 MGC3178 NM 030810 668 221432 hypothetical protein NPDO16 NPD016 NM 031212 669 221434 hypothetical protein DC50 DC50 NM 031210 670 221505 h othetical protein MGC5350 AW612574 671 221509 densit -re ulated protein SMAP-3 AB014731 672 221528 Similar to hypothetical protein FLJ11656 BC000143 673 221577 rostate differentiation factor AF003934 674 221593 Similar to ribosomal protein L31 BC001663 675 221599 Similar to PTD015 protein BC002752 676 221620 brain my025 AF061264 677 221669 ac I-Coenz me A deh dro enase family, member 8 BC001964 678 221776 bromodomain-containing 7 A1885109 679 221791 hypothetical protein BG167522 683 221935 FLJ13078 fis AK023140 684 221942 sec6l homolog A1719730 686 222192 FLJ11610fis AK021672 687 222193 FLJ11610fis AK021672 688 222203 FLJ13563fis AK023625 689 222280 Weakl similar to ALUC BG491393 691 33768 59 protein L19267 692 35666 semaphorin III family homolog U38276 693 37004 ulmona surfactant-associated protein B SP-B J02761 694 37005 Unknown product D28124 696 37996 m otonic dystrophy kinase DM kinase L08835 697 41858 DKFZp564EO53 FRAG1 NM 014489 699 49485 W22625:71 E5 Homo sapiens cDNA W22625 700 55692 75H3 / not-directional W22924 703 AFFX-HSAC07/X00351 M beta-actin X00351 704 AFFX-HUMISGF3A/M97935 MB transcription factor ISGF-3 M97935 705 AFFX-r2-Ec-bioB-3 E coli bioB gene biotin synthetase J04423 Reference list 5,242,974 5,561,071 5,384,261 5,571,639 5,405,783 5,593,839 5,412,087 5,599,695 5,424,186 5,624,711 5,429,807 5,658,734 5,436,327 5,700,637 5,445,934 6,004,755 5,472,672 6,133,305 5,527,681 6,218,114 5,529,756 6,218,122 5,532,128 6,271,002 5,545,531 6,271,210 5,554,501 6,284,764 5,556,752 6,306,897 Adnane et al. (2000) "Inhibition of farnesyltransferase increases TGFbeta type II receptor expression and enhances the responsiveness of human cancer cells to TGFbeta"
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350:2129-2139 McLean et al. (2004) "Pharmacogenomic analysis of cytogenetic response in chronic myeloid leukemia patients treated with imatinib" Clin. Cancer Res. 10:155-165 Methods in Molecular Biology, Vol. 33: In Situ Hybridization Protocols, Choo, ed., Humana Press, Totowa, NJ (1994) Morgan et al. (2001) "Cell-cycle-dependent activation of mitogen-activated protein kinase kinase (MEK-1/2) in myeloid leukemia cell lines and induction of growth inhibition and apoptosis by inhibitors of RAS signaling" Blood 97:1823-1834 Na et al. (2004) "Inhibition of farnesyltransferase prevents collagen-induced arthritis by down-regulation of inflammatory gene expression through suppression of p21(ras)-dependent NF-kappaB
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Okutsu et al. (2002) "Prediction of chemosensitivity for patients with acute myeloid leukemia, according to expression levels of 28 genes selected by genome-wide complementary DNA microarray analysis" Mol. Cancer Ther. 1:1035-1042 Paez et al. (2004) "EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to Gefitinib" Science 304:1497-1500 Pasqualucci et al. (2001) "Hypermutation of multiple proto-oncogenes in B-cell diffuse large-cell lymphomas" Nature 412:341-346 Pinkel (1988) " Fluorescence in situ Hybridization with Human Chromosome-Specific Libraries:
Detection of Trisomy 21 and Translocations of Chromosome 4" Proc Natl Acad Sci USA 85:9138-Pinkel et al. (1998) "High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays" Nature Genetics 20:207-211 Rao et al. (2004) "Phase III Double-Blind Placebo-Controlled Study of Farnesyl Transferase Inhibitor R115777 in Patients With Refractory Advanced Colorectal Cancer" J
Clin Oncol 22:3950-3957 Raponi, et al. (2004) "Identification of Molecular Predictors of Response to ZARNESTRATM
(Tipifarnib, R115777) in Relapsed and Refractory Acute Myeloid Leukemia" Blood 104:861a Reiss et al. (1990) "Inhibition of purified p2lras farnesyl:protein transferase by Cys'AAX
tetrapeptides" Cell 62:81-88 Reuter et al. (2000) "Targeting the Ras signaling pathway: a rational, mechanism-based treatment for hematologic malignancies?" Blood 96:1655-1669 Sahai et al. (2002) "RHO-GTPases and cancer" Nat. Rev. Cancer 2:133-142 Schoch et al. (2002) "Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles" Proc. Natl. Acad. Sci. USA 99:10008-Sterpetti et al. (1999) "Activation of the Lbc Rho exchange factor proto-oncogene by truncation of an extended C terminus that regulates transformation and targeting" Mol. Cell.
Biol. 19:1334-1345 Strachan and Read, Human Molecular Genetics, 1996 Takada et al. (2004) "Protein farnesyltransferase inhibitor (SCH 66336) abolishes NF-kappaB
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Testa et al. (2004) "Interleukin-3 receptor in acute leukemia" Leukemia 18:219-Thomas et al. (2001) "R115777, a farnesyl transferase inhibitor (FTI), has significant anti-leukemia activity in patients with chronic myeloid leukemia (CML)" Blood 98 Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology, Vol. 24:
Hybridization With Nucleic Acid Probes, Elsevier, NY
Toksoz et al. (1994) "Novel human oncogene lbc detected by transfection with distinct homology regions to signal transduction products" Oncogene 9:621-628 Valk et al. (2004) "Prognostically useful gene-expression profiles in acute myeloid leukemia" N.
Engl. J. Med. 350:1617-1628 Van Cutsem et al. (2004) "Phase III trial of gemcitabine plus tipifamib compared with gemcitabine plus placebo in advanced pancreatic cancer" J. Clin. Oncol. 22:1430-1438 Yagi et al. (2003) "Identification of a gene expression signature associated with pediatric AML
prognosis" Blood 102:1849-1856 Zhang et al. (2002) "Farnesyltransferase inhibitors reverse Ras-mediated inhibition of Fas gene expression" Cancer Res. 62:450-458 Zheng et al. (1995) "Direct involvement of the small GTP-binding protein Rho in Ibc oncogene function" J. Biol. Chem. 270:9031-9034 DEMANDE OU BREVET VOLUMINEUX

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Claims (39)

1. A method of assessing acute myeloid leukemia (AML) status comprising the steps of a. ~obtaining a biological sample from an AML patient; and b. ~measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 wherein the expression levels of the Marker genes above or below pre-determined cut-off levels are indicative of AML status.

2. A method of staging acute myeloid leukemia (AML) patients comprising the steps of a. ~obtaining a biological sample from an AML patient; and b. ~measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 wherein the expression levels of the Marker genes above or below pre-determined cut-off levels are indicative of AML survival.

3. A method of determining acute myeloid leukemia (AML) patient treatment protocol comprising the steps of a. ~obtaining a biological sample from an AML patient; and b. ~measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 wherein the expression levels of the Marker genes above or below pre-determined cut-off levels are sufficiently indicative of response to therapy to enable a physician to determine the degree and type of therapy recommended to provide appropriate therapy.

4. A method of treating a acute myeloid leukemia (AML) patient comprising the steps of:
a. ~obtaining a biological sample from an AML patient; and b. ~measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9 wherein the expression levels of the Marker genes above or below pre-determined cut-off levels are indicate a response to therapy and;
c. ~treating the patient with adjuvant therapy if they have a responder profile.

5. A method of determining whether a acute myeloid leukemia (AML) patient is high or low risk of mortality comprising the steps of a. ~obtaining a biological sample from an AML patient; and b. ~measuring Biomarkers associated with Marker genes corresponding to those selected from Table 3 wherein the expression levels of the Marker genes above or below pre-determined cut-off levels are sufficiently indicative of risk of mortality to enable a physician to determine the degree and type of therapy recommended.

6. The method of one of claims 1-5 further comprising measuring the expression level of at least one gene constitutively expressed in the sample.

7. The method of one of claims 1-5 wherein the specificity is at least about 40%.

8. The method of one of claims 1-5 wherein the sensitivity is at least at least about 80%.

9. The method of claim 28 wherein the p-value is less than 0.05.

10. The method of one of claims 1-5 wherein gene expression is measured on a microarray or gene chip.

11. The method of claim 10 wherein the microarray is a cDNA array or an oligonucleotide array.

12. The method of claim 10 wherein the microarray or gene chip further comprises one or more internal control reagents.

13. The method of one of claims 1-5 wherein gene expression is determined by nucleic acid amplification conducted by polymerase chain reaction (PCR) of RNA

extracted from the sample.

14. The method of claim 13 wherein said PCR is reverse transcription polymerase chain reaction (RT-PCR).

15. The method of claim 14, wherein the RT-PCR further comprises one or more internal control reagents.

16. The method of one of claims 1-5 wherein gene expression is detected by measuring or detecting a protein encoded by the gene.

17. The method of claim 16 wherein the protein is detected by an antibody specific to the protein.

18. The method of one of claims 1-5 wherein gene expression is detected by measuring a characteristic of the gene.

19. The method of claim 18 wherein the characteristic measured is selected from the group consisting of DNA amplification, methylation, mutation and allelic variation.

20. A method of generating an acute myeloid leukemia (AML) prognostic patient report comprising the steps of:
determining the results of any one of claims 1-5; and preparing a report displaying the results.

21. The method of claim 20 wherein the report contains an assessment of patient outcome and/or probability of risk relative to the patient population and/or likelihood or response to chemotherapy.

22. A patient report generated by the method according to claim 21.

23. A kit for conducting an assay to determine acute myeloid leukemia (AML) prognosis in a biological sample comprising: materials for detecting isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.

24. The kit of claim 24 further comprising reagents for conducting a microarray analysis.

25. The kit of claim 24 further comprising a medium through which said nucleic acid sequences, their complements, or portions thereof are assayed.

26. Articles for assessing acute myeloid leukemia (AML) status comprising:
materials for detecting isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.

27. The articles of claim 26 further comprising reagents for conducting a microarray analysis.

28. The articles of claim 27 further comprising a medium through which said nucleic acid sequences, their complements, or portions thereof are assayed.

29. A microarray or gene chip for performing the method of one of claims 1-5.

30. The microarray of claim 29 comprising isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.

31. The microarray of claim 30 wherein the measurement or characterization is at least 1.5-fold over- or under-expression.

32. The microarray of claim 30 wherein the measurement provides a statistically significant p-value over- or under-expression.

33. The microarray of claim 32 wherein the p-value is less than 0.05.

34. The microarray of claim 30 comprising a cDNA array or an oligonucleotide array.

35. The microarray of claim 30 further comprising or more internal control reagents.

36. A diagnostic/prognostic portfolio comprising isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes selected from the group consisting of Marker genes corresponding to those selected from Table 3, Table 4, Table 5, Table 7, Table 8 or Table 9.

37. The portfolio of claim 36 wherein the measurement or characterization is at least 1.5-fold over- or under-expression.

38. The portfolio of claim 37 wherein the measurement provides a statistically significant p-value over- or under-expression.

39. The portfolio of claim 37 wherein the p-value is less than 0.05.