WO2013016673A2 - Use of dpep1 and tpx2 expression for evaluating treatment or survival time of patients with pancreatic ductal adenocarcinoma - Google Patents

Abstract

What is described is a method of using expression levels of dipeptidase 1 (DPEPl) for evaluating treatment and/or survival time of patients with pancreatic ductal adenocarcinoma (PDAC), comprising assaying an expression levels of DPEPl and of targeting protein for Xklp2 (TPX2) in the PDAC tissue of the patient, and evaluating treatment and/or survival based on the PDAC levels of DPEPl and TPX2 measured in the patient.

Description

USE OF DPEP1 AND TPX2 EXPRESSION FOR EVALUATING TREATMENT OR SURVIVAL TIME OF PATIENTS WITH PANCREATIC DUCTAL ADENOCARCINOMA

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of United States Provisional Application No. 61/512,302 filed July 27, 2011 which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

[0002] The technology relates to a method for evaluating treatment and/or survival time of patients with pancreatic ductal adenocarcinoma (PDAC). The method is based on

measurement of expression levels of dipeptidase 1, a renal enzyme (DPEP 1) and targeting protein for Xklp2 (TPX2). Applicants have discovered that expression levels of DPEP1 and TPX2 in the PDAC tissue can evaluate survival time and guide the choice of therapy.

BACKGROUND

[0003] PDAC is a devastating malignancy worldwide with a median survival of about six months. Because of the current lack of early detection strategies, more than 80% of patients present with advanced disease at diagnosis, and the overall 5-year survival for PDAC patients is 3-5% (Hezel et al., 2006, Genes Dev 20:1218-49). Gemcitabine is the only first-line chemotherapeutic drug approved for advanced pancreatic. However, single agent Gemcitabine is only moderately effective showing a tumor response rate of about 12% (Oettle et al., 2007, JAMA 297:267-77). The progress with the new treatments for pancreatic cancer has been disappointingly slow. The failure of many novel targeted agents used in pancreatic cancer clinical trials may be a result of the molecular heterogeneity found in pancreatic cancers, including somatic mutations and epigenetic changes of oncogenes and tumor suppressor that regulate cell proliferation, survival, and other homeostatic functions (Mahalingam et al., 2009, Expert Opin Emerg Drugs 14:311-28). Therefore, better biomarkers and novel therapeutic targets are desperately needed to improve the survival rate of patients with PDAC.

[0004] The most commonly used pathologic predictors of survival after surgery are the stage, grade, and the resection margin status. Resection margin status was confirmed as an influential prognostic factor in many studies. For instance, patients with positive microscopic resection margins (Rl) have a worse survival of 10.9 months versus 16.9 months for patients with negative resection margins (R0) within the context of the adjuvant European Study Group for Pancreatic Cancer-1 (ESP AC- 1) study (Neoptolemos et al, 2001, Ann Surg 234:758-68). However, due to intrinsic heterogeneity, cancer patients with equivalent TNM stage, resection margin and grade may have quite different response to treatment and clinical behavior. Hence, beside these conventional clinical-pathologic factors it is necessary to look for new prognostic molecular biomarkers with the advantage of being quantifiable and objective to aid in the classification and management of PDAC (Garcea et al., 2005, Eur J Cancer 41 :2213-36).

[0005] Gene-expression profiling using microarrays has been utilized to identify genes or gene signatures that are associated with pancreatic cancer (Goggins, 2007, Semin Oncol 34:303-10; Kolbert et al, 2008, Technol Cancer Res Treat 7:55-59; Grutzmann et al, 2005, Oncogene 24:5079-88). The majority of these studies have compared PDAC to either noncancerous pancreases or the other pancreatic cancers (Han et al, 2003, Cancer Res 62:2890-96; Lowe et al, 2007, PLoS One 2:e323; lacobuzio-Donahue et al, 2002, Am J Pathol 160: 1239- 49.), resulting in large datasets of differentially expressed pancreatic cancer-specific genes (Yeh, 2009, Future Oncol 5:313-21). Very few studies have defined and validated prognostic markers that are of biological significance in pancreatic cancer (Campagna et al, 2008 Int J Clin Exp Pathol 1 :32-43; Kim et al, 2007, Pancreas 34:325-34; Stratford et al, PLoS Med 7:el000307). Predicting prognosis for patients with pancreatic cancers may identify a subset that could benefit from aggressive intervention including surgery and/or chemotherapy (Garcea 2005) . In addition, the development of a prognostic gene signature might provide insight into molecular subtypes of pancreatic cancer (Yeh 2009). Clearly, there are subsets of patients, 2-4% of all patients diagnosed with pancreatic cancer, with rare long-term survival (Han et al, 2006, Pancreas 32:271-75; Schnelldorfer et al, 2008, Ann Surg 247:456-62; Ferrone et al, 2008, J Gastrointest Surg 12:701-06).

SUMMARY

[0006] Applicants have discovered that expression levels of dipeptidase 1, renal, [Homo sapies] (DPEPl) (NCBI Gene ID: 1800) and targeting protein for Xklp2, microtubule-associated, homolog (Xenopus laevis) [Homo sapies] (TPX2) (NCBI Gene ID: 22974) are useful as prognostic predictors for PDAC. Moreover, these expression levels are capable of revealing potential targets for new therapies in PDAC cases.

[0007] One aspect of the description is a method of using expression levels of dipeptidase 1 (DPEPl) for prognosis of pancreatic ductal adenocarcinoma (PDAC) in a patient, comprising: (a) causing a measurement of an expression level of DPEPl in a PDAC tissue of a patient; (b) causing a comparison of the expression level of DPEPl in the PDAC tissue to a reference level of DPEPl ; and (c) causing a prognosis to be made based on the difference between the PDAC levels compared to the normal levels. Another embodiment is the method further comprising: (a) causing a measurement of an expression level of targeting protein for Xklp2 (TPX2) in the PDAC tissue; (b) causing a comparison of the expression level of TPX2 in the PDAC tissue to a reference normal level of TPX2; and causing a prognosis to be made based on the difference between the PDAC levels of both DPEP l and TPX2 measured in the patient to the reference levels of DPEPl and TPX2, respectively. Another embodiment is the method 2 further comprising the steps of: (a) collecting at least one first sample from a PDAC patient; (b) administering to the subject a cancer treatment; (c) collecting at least one second sample following said treatment; (d) measuring levels of DPEP l or TPX2 in each of the samples; (e) comparing levels of DPEP 1 or TPX2 before and after treatment; and (e) providing a prognosis, wherein the prognosis is based on the difference in the levels of DPEP 1 or TPX2 before and after treatment. These methods are useful to identify high-risk PDAC patients, or to predict an outcome of a therapy for treating PDAC, or to develop a therapy for treating PDAC. One possible outcome of the prognosis is when the levels of DPEPl and TPX2 are found to change following the treatment, including wherein the prognosis is favorable. Another outcome is wherein the levels of DPEP 1 and TPX2 do not change following the treatment, including wherein the prognosis is unchanged following treatment.

[0008] Another aspect of the method comprises the steps of: extracting RNA from the first and second samples; and contacting said RNA with at least one nucleic acid probe to measure levels of DPEPl or TPX2. RNA may be is extracted from tissue obtained from each sample. RNA may be extracted from tumor tissue, and levels of DPEP l or TPX2 may be measured by quantitative PCR, or by a microarray comprising a multiplicity of single stranded oligonucleotides to measure tissue levels of DPEP l or TPX2. The measurement may comprise contacting said RNA with at least one nucleic acid probe to measure levels of a control RNA. Control RNA may be used, e.g., GAPDH, beta-actin, and 18S RNA.

[0009] Another aspect of the method uses as sample consisting of a multiplicity of tissue samples, obtained from a multiplicity of subjects and/or a multiplicity of tissue samples. A median or average level of DPEP 1 or TPX2 may be determined from the multiplicity of tissue samples. The method, therefore, may further involve collecting tissue samples from a multiplicity of normal subjects, determining the median or average level of DPEPl or TPX2 in normal subjects, and providing a prognosis based on a comparison of the levels of DPEPl or TPX2 of the PDAC patient with the median or average level of DPEPl or TPX2 of normal subjects. In this respect, a reference level of DPEPl or TPX2 may be measured, which may be the median or average level of DPEPl or TPX2 of normal subjects, or in tissues of PDAC patients, including non-cancerous tissue of PDAC patients. The method may involve making a comparison of the level of DPEPl or TPX2 in the first sample of the subject with cancer to the reference level of DPEPl or TPX2, and a further step of providing a prognosis based on the comparison. The prognosis may favorable for an anti-cancer treatment, including where the level of DPEP l or TPX2 in the subject with cancer is statistically the same as the reference level of DPEP l or TPX2. The prognosis may not be favorable for a kinase inhibitor treatment, including where the level of DPEPl or TPX2 in the subject with cancer is statistically substantially different than the reference level of DPEPl or TPX2. Samples may be collected from the subject with cancer at various times before and after anti-cancer treatment.

[0010] Another aspect of the method involves comparing the level of DPEP l or TPX2 in the first sample with the level of DPEPl or TPX2 in each of the multiplicity of second samples. Again, the prognosis is favorable, including wherein the levels of DPEPl or TPX2 changes over time following treatment, in which an alternate treatment modality is provided. The alternate treatment modality may include administering a kinase inhibitor treatment, and/or a cytotoxic drug.

[0011] Another aspect of the method is where the nucleic acid probe used in the analysis is a single stranded nucleic acid, including wherein the single stranded probe hybridizes with the nucleic acid having the sequence of DPEPl (SEQ ID NO: l) or TPX2 (SEQ ID NO:2), or a known genetic variant of said sequence. The method may convert the RNA to cDNA using a reverse transcriptase, including when the cDNA is amplified in a polymerase chain reaction.

[0012] Another aspect of the method is wherein the prognosis is in a report, or a result of a computer calculation. The method may consist of the additional step of causing said report to be produced in a tangible medium. A computing device may be used, comprising a means to store data to be configured in a prognosis in the form of a report, wherein the prognosis is calculated by comparing a level of expression of DPEPl or TPX2 in tissue of a PDAC patient to a reference value. The computing device may be used to generate as the result of data indicative of DPEP l or TPX2 levels in various samples, the data having been subject to a method of prognosis by the computing device. The report may be displayed. The computing device may also include components for data storage, manipulation, processing, configuration, prognosis, display, and calculation. The computing device may consist of a personal computers, server computers, hand held or laptop devices, smart phones, multiprocessor systems, microprocessor- based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, or distributed computing environments. The computing device may comprise one or more processors and one or more memories, including read only memory (ROM) or random access memory (RAM). The computing device may include disks and drives for writing and reading data, one or more user inputs, and/or a network environment with one or more logical connections to one or more computers, wherein data and information may be sent and received by the computing device and may be manipulated. The computing may be configured to receive data related to one or more measurements of DPEP 1 or TPX2, or store data related to one or more measurements of DPEP 1 or TPX2 in a memory associated with the computing device. The computing device may process the data related to a DPEP 1 or TPX2 based on one or more clinical trials, or is indicative of one or more prognosis, and/or be useful to produce a prognosis of PDAC based on a measurement of a level of expression of DPEP 1 or TPX2.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Fig. 1 : DPEP1 and TPX2 are associated with cancer-specific mortality in two independent cohorts. Fig. 1A: Germany test cohort. Kaplan Meier analysis DPEP 1, TPX2, and PRRl 1 in the Germany test cohort. Fig. IB: Maryland validation cohort. The association of DPEP 1 or TPX2 with patient survival was confirmed in the Maryland validation cohort. PRRl 1 was not associated with survival in the validation cohort. Fig. 1C: Combined analysis of Germany test and Maryland validation cohorts.

[0014] Fig. 2. DPEP1 expressed at lower level and TPX2 at higher level and in pancreatic tumors as compared to adjacent non-tumor tissue. Fig. 2A: Germany test cohort. Expression of DPEP 1 and TPX2 in Germany test cohort. Fig. 2B: Maryland validation cohort. Validation of DPEP 1 and TPX2 expression in independent Maryland cohort. Dot plots represent gene expression level with relative threshold cycle value (Ct) normalized with endogenous control gene GAPDH. Bars indicate median value. Wilcoxon matched-pairs t-tests P value and tumor: non-tumor ratios (T:N) are indicated in the graphs.

[0015] Fig. 3. DPEP1 (Fig. 3 A) and TPX2 (Fig. 3B) expression in KPC mutant mice. WT: littermate Wild-type control group (N=6, age of 35 weeks); MT-early: mutant KPC mice with early stage of PDAC (N=6, age of 6-17 week); MT-late: mutant mice with late stage of PDAC (N=8, age of 24-28 weeks). Total RNA were extracted from frozen pancreatic tissues by using Trizol. Quantitative RT-PCR reactions were performed using Taqman Gene Expression Assays. Mouse GAPDH was used as endogenous control to normalize across the samples. T-test analysis revealed that DPEP 1 and TPX2 expressions were significantly different between WT and MT-early or between WT and MT-late (* <0.01). Expression levels in MT-early and MT- late were similar for DPEP1 and TPX2.

[0016] Fig. 4. KRAS and EGF regulated DPEP1 and TPX2 expression through MAPK pathway. Fig. 4A: siRNA transfection of Pane- 1 cells. 24 hours after trans fection, gene expression was measured by Taqman RT-PCR. GAPDH was used as endogenous control to normalize across the samples. Log₂ ratio represents the effect of target siRNA compared to negative control siRNA on Panc-1 cells. Fig. 4B: EGF and MAPK inhibitor U0126 treatment on Panc-1 cells. Cells were starving in 0.1% FBS for 16 hours before treatments. Cells were treated with RPMI medium containing EGF (20ng/mL) or U0126 (10 μΜ) alone for 24 hours. EGF and U0126: Cells were pretreated with U0126 for 1 h before addition of EGF. Control cells remained in RPMI with DMSO. Log2 ratio represents the effect of treatment compared to untreated control cell. Each assay was performed in triplicate. * T-test <0.01.

[0017] Fig. 5. Effect of EGF, AZD6244 and LY294002 on DPEP1 expression. Cells were starved in 0.1% FBS for 16 hours before treatments. Cells were treated with RPMI medium containing EGF (30ng/mL), AZD6244 (1.5 μΜ) or LY294002 (1.5 μΜ) alone for 24 hours. EGF+AZD6244 or EGF+LY294002: Cells were pretreated with AZD6244 or LY294002 for lh prior to the addition of EGF. Untreated control cells were maintained in RPMI with DMSO. Fig. 5 A: Real-time PCR was done to determine DPEP1 mRNA levels. Relative expression of DPEP1 represents the effect of treatment on gene expression compared to untreated control. Data are means ± S.D. from 3 independent experiments. * T-test P <0.01. Fig. 5B: Western blot showed similar changes at protein level of DPEP 1 after 24 hour treatment. Fig. 5C: Western blot demonstrated the efficiency and specificity of AZD6244 and LY294002.

[0018] Fig. 6. DPEP 1 overexpression enhances sensitivity to gemcitabine. DPEP1 overexpressing cells and control cells were analyzed for cellular sensitivity to gemcitabine using Panel (A) and MIApaca2 (B). Overexpession of DPEP1 increased the sensitivity of Panel and MIApaca2 cells to gemcitabine. Control cells are Panel or MIApaca2 cells transfected with GFP control vectors. Cells were treated with Gemcitabine for 96 hours at different doses. The MTS assay was used to quantitate cytotoxicity (cell death) according to the manufacturer's instructions. Relative cytotoxicity (%) was calculated using the formula: [1-(OD₅₇₀ of drug treated cells / OD57₀ of untreated cells)] ¾ 100%. Data are means ± S.D. from 3 independent experiments. * T-test P <0.01.

[0019] Fig. 7. DPEP1 overexpression inhibits cell invasion in Panel and MIApaca2 cells. Fig. 7A: Cell invasion was analyzed in Panel (upper panel) and MIApaca2 (lower panel) cells using Biocoat matrigel invasion assay. The invaded GFP-positive cells were counted under a fluorescence microscope. Fig. 7B: Relative cell invasion is expressed as the ratio of the percent invasion of a test cell over the percent invasion of a control cell. * P < 0.01.

[0020] Fig. 8. Correlation of the tumor/noncancerous tissue expression ratio comparing the RT-PCR data with microarray data in the Germany test cohort. Human GAPDH was used as endogenous control to normalize across the samples. Spearman correlation test r=0.89,

O.0001.

[0021] Fig. 9. Combined analysis of Germany test and Maryland validation cohorts, stratified by resection margin status.

[0022] Fig. 10. cDNA sequence for DPEP1 ( CBI Reference Sequence NM_0044133) (SEQ ID O: l)

[0023] Fig. 1 1 cDNA sequence for TPX2 (NCBI Reference Sequence NM_012112.4) (SEQ ID NO:2)

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Definitions

[0024] The terminology described herein is for the purpose of describing particular embodiments of the disclosure and is not intended to be limiting.

[0025] The singular forms "a", "an", and "the" as used herein include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to a composition containing "a compound" includes a mixture of two or more compounds. It should also be noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.

[0026] As used herein, "messenger RNA" or "mRNA" means a coding RNA sequence of 5 to 4000 nucleotides in length that can be detected in a biological specimen. Some mRNAs are derived from precursors transcripts processed by nuclear to a mature species. Synthetic DNA complementary to a mRNA (cDNA) can bind to corresponding mRNA or amplified double stranded forms of mRNA and provide a means for detection.

[0027] "Biological sample" or "body tissue" can be used interchangeably and refer to a fluid isolated from a mammal. Such samples include, but are not limited to, serum, plasma, urine, ascitic fluid, tissue isolated from the PDAC itself. The sample refers to all biological materials isolated from any given subject. In the context of the description such samples include, but are not limited to, the PDAC tissue itself. [0028] "mRNA Variants" are common, for example, among different animal species.. These variants demonstrate a scope of acceptable variation in the sequence of the mRNAs that does not impair function or the ability to detect the mRNA(s). Some modifications may affect the ability to detect the mRNA by qRT-PCR directed against a canonical species, but not by microarray.

[0029] The terms "polynucleotide", "oligonucleotide", or "nucleic acid" can be used interchangeably and refer to nucleotide sequences of any length, including DNA and RNA. The nucleotides can be deoxyribonucleotides, ribonucleotides, modified nucleotides or bases, and/or their analogs, or any substrate that can be incorporated into a nucleotide sequence, for example by DNA or RNA polymerase, or by chemical reaction. Nucleic acids may be single stranded or double stranded, or may contain portions of both double and single stranded sequence. A single strand can provide a probe that hybridizes to a target sequence.

[0030] An "isolated" polynucleotide is a nucleic acid molecule that is identified and separated from at least one contaminant nucleic acid molecule with which it is ordinarily associated in its natural source. An isolated nucleic acid molecule is other than in the form or setting in which it is found in nature. Isolated nucleic acid molecules therefore are distinguished from the specific nucleic acid molecule as it exists in natural cells.

[0031] "Complement" or "complementary" as used herein in reference to a nucleic acid sequence means Watson and Crick or Hogsteen base pairing between nucleotides or nucleotide analogs.

[0032] "Percent (%) nucleic acid sequence identity" as used herein means the percentage of nucleotides in a candidate sequence that are identical with the nucleotides in a nucleic acid sequence of interest, after aligning the sequences and introducing gaps, if necessary, to achieve the maximum percent sequence identity. Alignment for purposes of determining percent nucleic acid sequence identity can be achieved in various ways that are within the skill in the art, for instance, using publicly available computer software such as BLAST, BLAST-2, ALIGN, ALIGN-2 or Megalign (DNASTAR) software. Thymine (T) and uracil (U) may be considered equivalent when comparing DNA and RNA.

[0033] As used herein, "differential expression" means qualitative or quantitative differences in the expression pattern of one or more polynucleotides, including mRNA, in a biological sample. Expression of the one or more polynucleotides may be upregulated, resulting in an increased amount of transcripts, or downregulated, resulting in a decreased amount of transcripts. Expression of the one or more polynucleotides may be upregulated or downregulated in a particular state, such as a disease state, relative to a reference state, such as a normal state, thus permitting comparison of two or more states. The one or more polynucleotides may exhibit a pattern of expression in said body fluid, cell, or tissue that is detectable by standard techniques, including but not limited to expression arrays, quantitative reverse transcriptase PCR, northern analysis, and real-time PCR. Some of the polynucleotides may be expressed in one state but not another.

[0034] As used herein, "gene" includes any polynucleotide sequence or portion thereof with a functional role in encoding or transcribing a protein or regulating other gene expression. The gene may consist of all the nucleic acids responsible for encoding a functional protein or only a portion of the nucleic acids responsible for encoding or expressing a protein. The polynucleotide sequence may contain a genetic abnormality within exons, introns, initiation or termination regions, promoter sequences, other regulatory sequences or unique adjacent regions to the gene.

[0035] As used herein, "tumor" refers to malignant neoplastic cell growth and proliferation, and all pre-cancerous and cancerous cells and tissues.

[0036] "Treatment" is an intervention performed with the intention of preventing the development or altering the pathology of a disease or disorder. Accordingly, "treatment" herein refers to both therapeutic treatment and prophylactic or preventative measures. Those in need of treatment include those already with the disease or disorder as well as those in which the disease or disorder is to be prevented. In tumor (e.g., cancer) treatment, a therapeutic agent may directly decrease the pathology of tumor cells, or render the tumor cells more susceptible to treatment by other therapeutic agents, e.g., radiation and/or chemotherapy. mRNA Variation

[0037] Mature mR As are described herein as useful for having specific nucleotide sequences. However hundreds of genomic variants are known for both DPEP 1 and TPX2 Some of these lead to variant mRNA sequences. While SEQ ID NOS: l and 2 are disclosed herein, variants of these mRNA sequence are hereby included in the current description by reference to the NCBI SNP Database and/or Ensembl (EMBL).

Methods of Detecting and Identifying mRNAs

[0038] The disclosure provides methods for detecting and identifying mRNA. In one example, mRNA is isolated from a tumor or cancer tissue, the isolated mRNA is converted to cDNA, and amplified. [0039] Generally, mRNA can be detected by various methods, including reverse transcription polymerase chain reaction (RT-PCR), northern blotting, ribonuclease protection assay (RPA), and in situ hybridization (ISH), or kits such as QuantiGene® (Panomics, Fremont, CA).

[0040] Kits for isolating RNA, and in particular mRNA, from a biological sample are known and commercially available, such TRIzol® (Invitrogen™).

[0041] cDNA can be generated by reverse transcription of isolated mRNA using reverse transcription conventional techniques. mRNA reverse transcription kits are known and commercially available. Examples of suitable kits include, but are not limited to, the TaqMan® and/or High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City, CA). Specific primers are known and commercially available, for example, from Applied Biosystems (Foster City, Ca), Ambion (Austin, TX), and Qiagen (Valencia, CA).

[0042] The reverse transcript of the mRNA can be amplified using conventional PCR techniques including, but not limited to, real time PCR. Kits for quantitative real time PCR of RNA and mRNA are known and commercially available. The RNA can be ligated to a single stranded oligonucleotide containing universal primer sequences, a polyadenylated sequence, or adaptor sequence prior to reverse transcriptase and amplified using a primer complementary to the universal primer sequence, poly(T) primer, or primer comprising a sequence that is complementary to the adaptor sequence.

[0043] A biological sample can be obtained from a single individual or pooled, for example, from a group of individuals suffering from a particular disease or disorder. mRNA can be isolated from the sample by any number of methods, for example, as described herein, and the abundance of one or more mRNA can be determined by any of a number of methods, for example by calculating average Ct values. Data for each candidate mRNA in a given sample can be normalized by subtracting the Reference Ct for that sample. Normalized data can be represented by a Act value (Normalized = Average Ct of the mRNA assayed - Reference Ct). Given that Ct values are on a log₂ scale, the value 2^ACt represents linear scale expression values that can be compared directly for subsequent statistical analyses.

[0044] Amplification curves are checked to verify that Ct values are assessed in the linear range of each amplification plot. Typically, the linear range spans several orders of magnitude. For each candidate biomarker mRNA assayed, whether known or novel, a chemically synthesized version of the mRNA can be obtained and analyzed in a dilution series to determine the limit of sensitivity of the assay, the linear range of quantitation, and to estimate the absolute abundance of the candidate mRNAs measured. Protein detection

[0045] Protein expression products of the DPEP1 and TPX2 genes can also be measured and utilized as a means of monitoring expression levels of one or both of these genes. Protein expression levels can be measured using antibodies (e.g„ immunohistochemistry of tissues) (Millipore, Sigma, R&D Systems, OriGene Antibodies, GenScript, Novus Biologicals

Antibodies, Epitomics) or by ELISA measurements of solubilized tissue or blood samples.

Additionally, protein mass spectroscopy can be utilized to measure expression levels of one or both of the marker proteins. In addition, specific peptides are known for both genes. Biochemical assays for these are known (See EMD Millipore, Sigma-Aldrich, R&D Systems, OriGene, GenScript, Cell Signaling Technology, Enzo Life Sciences, and/or Uscn)

Prognosis

[0046] "Prognosis" as used herein refers to the probable outcome of a disease, preferably when a patient is diagnosed as having a tumor, and, more preferably, when the patient is diagnosed as having a cancer. The prognosis of a cancer includes the probable outcomes of using a treatment modality, preferably when the treatment involves use of a therapeutic drug. The prognosis can include an outcome in which the cancer is refractory to a possible treatment modality, preferably where the treatment modality will improve the prospects for recovery, increase the chances of survival, reduce the recovery period for the cancer, or minimize the probability of recurrence of the cancer. Most preferably the method of prognosis will identify a suitable treatment modality to improve the probability of a favorable outcome for the patient. A favorable outcome is one in which the cancer patient has at least a 70% chance, and preferably an 80% chance that the cancer will not recur or metastasize within 2, 3, 4, 5, 6, 7, 8, 9, 10 or more months from beginning a therapeutic regime.

[0047] The treatment regime is related to the level of DPEP1 and/or TPX2 in the blood, serum or bodily fluid of the patient.

• In a first instance, one can observe that treatment causes a change in DPEP1 and/or TPX2 levels in cancer patient. In that instance, the prognosis and the outcome of the treatment is favorable or unfavorable, depending on the direction of the change.

• In another instance, treatment does not cause a change in levels of DPEP1 and/or TPX2. In this instance, the prognosis is not favorable, because the cancer may be refractory to treatment. • In another instance, a cancer patient presents with low levels of DPEPl and/or high TPX2 and is refractory to therapy. In this instance, a more aggressive therapy should be considered. Such therapy includes a kinase inhibitor drug and/or treat with a cytotoxic agent. A cytotoxic agent may be chemotherapy or a high dose of radiotherapy.

• In another instance, a cancer patient presents with a high level of DPEP l and/or low TPX2. This patient likely will have a favorable prognosis. If their DPEPl level increases and/or TPX2 level decrease when a kinase inhibitor drug is administered, they more likely have better response to conventional chemotherapy, such as Gemcitabine treatment.

[0048] Whether a level of DPEPl and/or TPX2 is high or low for a specific cancer may be determined by the ordinary skilled worker from a clinical trial conducted with that cancer. In one instance, whether a tissue level of DPEP l and/or TPX2 is low or high depends on the standard value which is predetermined for the specific cancer. In another instance, tissue levels of DPEP l and/or TPX2 are low or high relative to a median or average value obtained from normal, healthy subjects. In another instance, whether tissue levels of DPEPl and/or TPX2 are low depends on an median or average value obtained from different patients with the same cancer. In another instance, the reference value is a median obtained by factoring the value from same cancer patients. The level of DPEPl and/or TPX2 may be normalized by comparing it to the level of a control gene. The skilled person can realize that the reference value may be chosen depending on factors as the normalization method and the control values used.

[0049] The amount of DPEPl and/or TPX2 in a biological sample can be compared to a reference control, for example a matched sample of normal body tissue, a previously analyzed sample, or a suitable standard control developed for the particular assay.

Kits

[0050] Kits adapted for the determination of DPEP l and/or TPX2 mRNA expression and prognosis of disease are provided herein. Such kits may include materials and reagents adapted to specifically determine the presence and/or amount of a DPEPl and/or TPX2 mRNA in a sample. The kit can include nucleic acid molecules or probes in a form suitable for the detection of DPEPl and/or TPX2 mRNA. The nucleic acid molecules can be in any composition suitable for the use of the nucleic acid molecules according to the instructions. The kit can include a detection component, such as a microarray, a labeling system, a cocktail of components (e.g., suspensions required for any type of PCR, especially real-time quantitative RT-PCR), membranes, color-coded beads, columns and the like. Furthermore, the kit can include a container, pack, kit or dispenser together with instructions for use.

[0051] A kit may contain, for example, forward and reverse primers designed to amplify and detect the DPEP1 and/or TPX2 mRNA in biological. Many different PCR primers can be designed and adapted as necessary to amplify one or more mRNA that are differentially expressed in a body fluid and correlate to a particular disease or disorder. In one embodiment, the primers are designed to amplify a DPEP 1 and/or TPX2 mRNA. The kit may also contain single stranded oligonucleotide containing universal primer sequences, polyadenylated sequences, or adaptor sequences prior and a primer complementary to said sequences. The mRNA isolated from the biological sample is ligated to the single stranded oligonucleotide containing universal primer sequence, polyadenylated sequence, or adaptor sequence prior to reverse transcription and amplified with said complementary primers. In an embodiment, the kit comprises primers that amplify the DPEP 1 and/or TPX2 mRNA. In another embodiment, poly- A-tailing is used to generate a sequence that can then be hybridized to a poly-T primer that is used for reverse transcription. See, for example, Shi et al, 2005, BioTechniques 39:519-25.

Processing, storage, and data manipulation related to DPEP1 and/or TPX2 mRNA

[0052] In an embodiment, certain aspects of the present disclosure may take place on a general computing device. Aspects of storing, manipulating, calculating, configuring, or displaying data, prognosing and/or any other computing operations may be performed by a computing device. Examples of well-known computing systems, environments, and/or configurations that may be suitable include, but are not limited to, personal computers, server computers, hand held or laptop devices, smart phones, multiprocessor systems, microprocessor- based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments and the like.

[0053] An example computing device may comprise one or more processors and one or more memories. The memories may be coupled to the one or more processors. As one example, the memories may be coupled to the one or more processors by a system bus, which may be of a type of bus structure known in the art, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The memories may be read only memory (ROM), random access memory (RAM) and the like. A basic input/output system (BIOS) may be used to transfer data and information between elements within the computing device.

[0054] A computing device may include disks and drives for writing and reading data.

These may be hard drives, floppy drives, removable storage, optical disks, magnetic discs, magnetic cassettes, flash memory, or any other disk or drive for storing data. The disks and drives may be computer readable media and may have stored thereon instructions that when executed by a processor cause the processor to perform one or more actions included herein.

[0055] A computing device may be associated with one or more user inputs, such as, for example, a mouse, a keyboard, a voice recorder, touchscreen, joystick, a camera, a medical device with digital output and the like. These and other peripheral input devices may be connected via serial or parallel port to the system bus or any other interface. A monitor, tv, touchscreen, or other type of display device may also be connected to the system bus via an interface such as a video adapter. The same may be true for a printer, a video output, or speakers.

[0056] A computing device may be in a network environment with one or more logical connections to one or more computers. These may include servers, routers, PCs, network nodes and the like. Data and information may be sent and received by the computing device and may be manipulated.

[0057] In an embodiment, a computing device may be configured to receive data related to one or more measurements of DPEP l and/or TPX2 mRNA. The data related to one or more measurements of DPEP l and/or TPX2 mRNA may be stored in a memory associated with the computing device. As one example the one or more measurements may be stored in a database. These data may be configured to provide indications of the efficacy of the prognosis for various levels of DPEP l and/or TPX2 mRNA as indicated above.

[0058] In an embodiment, a series of trials related to DPEPl and/or TPX2 mRNA mRNA may provide information indicative of the one or more levels of DPEP l and/or TPX2 mRNA based on one or more trials, as well as information indicative of one or more prognoses. Other information indicative of one or more elements related to each prognosis may also be included. The information indicative of the one or more levels of DPEPl and/or TPX2 mRNA mRNA, the information indicative of the one or more prognoses and the information indicative of one or more elements related to the prognosis may be stored in a database.

[0059] In one embodiment, information indicative of one or more elements related to clinical markers of the cancer.

[0060] The database may be configured as a tool for prognosis. For example, the database may be used as a comparison for data indicative of the levels of DPEP l and/or TPX2 mRNA mRNA. A patient may have their levels of DPEPl and/or TPX2 mRNA mRNA measured. This measurement may be compared to one or more outputs of the database, such as, for example, calculations, similar prognosis factors, patient similarities and the like. In another embodiment, the database may be configured to provide a graphical display of the levels of DPEP l and/or TPX2 mRNA and prognosis.

[0061] Further, the data in the database may be displayed on a display device, or it may be used in one or more calculations. As one example, a calculation may comprise a prognosis of a patient associated with the data. In other words, the computing device may be configured to provide an output related to the prognosis of a patient as a response to receiving one or more measurements of DPEPl and/or TPX2 mRNA mRNA.

[0062] In an embodiment, computer readable medium may have stored thereon instructions that when executed by a processor may cause the processor to receive data indicative of one or more levels of DPEPl and/or TPX2 mRNA mRNA, manipulate the data, store the data, place the data in a database, subject the data to one or more calculations such as those described herein, and provide a prognosis.

[0063] Further, a computing device or computer readable medium may be configured to send data indicative of one or more levels of DPEPl and/or TPX2 mRNA; receive, as a response to the sending, an indication of the prognosis of a subject; and display, on a display, the prognosis of the subject.

[0064] In another embodiment, the database above may be used to instruct a researcher or a person in control of choosing an appropriate therapy on the basis of the prognosis of cancer patient.

* * *

[0065] All publications and patent applications in this specification are indicative of the level of ordinary skill in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated by reference.

Examples

Example 1 : Tissue Collection and RNA Isolation

[0066] Pairs of primary PDAC and adjacent non-tumor tissues came from 45 patients at the University of Medicine, G5ttingen, Germany, and from 27 patients recruited from the

University of Maryland Medical Center at Baltimore, Maryland. Tissues were flash frozen immediately after surgery. Demographic and clinical information for each tissue donor, including age, sex, clinical staging, resection margin status, survival times from diagnosis, and receipt of adjuvant chemotherapy were collected. Tumor histopathology was classified according to the World Health Organization (WHO) Classification of Tumor system (Aaltonen et al.,WHO, Int'l Agency for Research on Cancer. Lyon Oxford: IARC Press, 2000). Use of these clinical specimens was approved by the Office of the Human Subject Research (OHSR, Exempt # 4678) at the National Institutes of Health, Bethesda, MD.

Example 2: RNA Isolation and Microarray Processing

[0067] RNA from frozen tissue samples was extracted using standard TRIzol® protocol. RNA quality was confirmed with the Agilent 2100 Bioanalyzer (Agilent Technologies) before the microarray gene expression profiling. Tumors and paired nontumor tissues from Germany cohort were profiled separately using the Affymetrix GeneChip Human Exon 1.0 ST arrays according to the manufacturer's protocol at LMT microarray core facility at National Cancer Institute, Frederick, MD. All arrays were RMA normalized and gene expression summaries were created for each gene by averaging all probe sets for each gene using Partek^® Genomics Suite 6.5. All data analysis was performed on gene summarized data. The data discussed in this publication have been deposited in National Center for Biotechnology

Information's (NCBI's) Gene Expression Omnibus.

Example 3 : qRT-PCR

[0068] Total RNA was reverse transcribed using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). High-throughput quantitative RT-PCR of gene expression was performed using 96.96 dynamic array chips from Fluidigm Corporation according to the manufacturer's protocol. Pre-amplification reactions were done in a GeneAmp PCR System 9700 from Applied Biosystems. The IFC Controller HX (Fluidigm Corporation) utilizes pressure to control the valves in the chips and load samples and gene expression assay reagents into the reaction chambers. The BioMark system (Fluidigm Corporation) is a real-time PCR instrument designed to thermal cycle these microfluidic chips and image the data in real time. qRT-PCR reactions in 384 well plates were performed using Taqman Gene Expression Assays on an ASI Prism 7900HT Sequence

[0069] Detection instrument from Applied Biosystems. Expression levels of ACTS and

GAPDH were used as the endogenous controls. All assays were performed in quadruplicate or triplicates. For quality control, any samples with a gene cycle value greater than 36 were considered of poor quality and removed. If a tumor or non-tumor sample failed quality control from qRT-PCR that case was removed from the analysis. All the primers for qRT-PCR in the present study were purchased from Applied Biosystems (Table 3). Example 4: Statistical Analysis

[0070] T-test, Wilcoxon matched-pairs t-tests, and Expression graphs were used to analyze differences in gene expression between tumors and paired non-tumor tissue using Graphpad Prism 5.0 (Graphpad Software Inc, San Diego, California). Correlation analysis and Kaplan-Meier analysis was performed with Graphpad Prism 5.0. Cox Proportional-hazards regression analysis was performed using Stata 1 1 (StataCorp LP, College Station, Texas). Univariate Cox regression was performed on genes and clinical covariates to examine influence of each on patient survival. Final multivariate models were based on stepwise addition and removal of clinical covariates found to be associated with survival in univariate models (P<0.05). For these models, resection margin status was dichotomized as positive (Rl/2) vs. negative (RO); TNM staging was dichotomized based on non-metastatic (Ι-ΠΑ) vs. metastatic (IIB-IV) disease.

[0071] All stepwise addition models gave the same final models as stepwise removal models. All univariate and multivariate Cox regression models were tested for proportional hazards assumptions based on Schoenfeld residuals, and no model violated these assumptions. The statistical significance was defined as <0.05. All P values reported are 2-sided.

Example 5 : Cell lines and culture conditions

[0072] Human pancreatic carcinoma cell lines PANC-1 (ATCC CRL-1469), were obtained from American Type Culture Collection ATCC (Rockville, MO, USA). Cells were maintained in GIBCO® RPMI Media 1640 supplemented with GlutaMAX™-l (Invitrogen), penicillin-streptomycin (50 IU/ml and 50 mg/ml, respectively), and 10% (v/v) fetal calf serum (FCS). Cells were incubated at 37°C in a humidified atmosphere with 10% CO₂. Human recombinant EGF was purchased from BO Biosciences. MEK-1/2 inhibitor U0126 was purchased from Cell Signaling. LY294002 was purchased from Cell Signaling and dissolved in DMSO (dimethyl sulfoxide) to make 50mM stock solution. AZD6244 were purchased from ChemieTek and dissolved in DMSO to make 40 mM stock solutions. All siRNAs and transfection reagent were purchased from Dharmacon. Si-DPEPl : ON-TARGETplus siRNA for OPEP 1 (J-005852-05-0010); Si-Kras: SMART pool siRNA for Kras (J-005069-00-0005); and Si-TPX2: SMART pool siRNA for TPX2 (L-010571-00-00005). Example 6: Transgenic mouse model

[0073] Conditional LSL-Kras^G12D/+, LSL-Trp53^R172H/+, and Pdx-l-Cre strains were interbred to obtain LSL-Kras^G12D/+, LSL-Trp53^R172H/+; Pdx-l-Cre triple mutant animals (KPC) on a mixed 129/SvJae/ C57B1/6 background (Hingorani et al, 2005, Cancer Cell 7:469-83). All animals were maintained in Mouse Model of Human Cancer Consortium at NCI-Frederick and all studies were conducted according to an experimental protocol reviewed and approved by the Animal Care and Use Committee, NCI-Frederick, MD. KPC mice spontaneously develop PDAC and have a dramatically shortened median survival of approximately 5 months as compared with their control littermates (wild-type Pdx-l-Cre mice). The majority oiKPC animals develop cachexia and abdominal distension, highly reminiscent of clinical findings seen in the human disease. Pancreatic tissue samples were collected from three groups: group I, littermate wild-type control group (N=6, age of 35 weeks); group II, KPC mice with early stage tumor (N=6, age of 6-17 weeks); and group III, KPC mice with advanced stage tumor (N=8, age of 24-28 weeks). Tissues were flash frozen immediately after euthanasia.

Example 7: Gene expression microarray profiling in pancreatic tumors

[0074] The characteristics of the patients with PDAC in the test cohort (N=45, from Germany) and the validation cohort (N=27, from Maryland) are shown in Table 1. The two cohorts were similar in TNM staging, resection margin status, and cancer-specific mortality ( =0.44, Kaplan-Meier log rank) with 1-year survival rate of 64.5% for the Germany cohort and 63.0% for the Maryland cohort. The Germany cohort was considerably older than the Maryland cohort (average 68.4 vs 61.9 years).

[0075] Gene expression profiles were compared in 45 pairs of pancreatic tumor and adjacent nontumor tissues in the Germany cohort using Affymetrix GeneChip Human Exon 1.0 ST arrays. Tumor gene expression profiles were distinctly different from non-tumor profiles. Using ANOVA in Partek^®, 7352 independent genes were found to be differentially expressed in tumors ( O.01). Cox-regression analysis was performed and 486 of the differentially expressed genes were determined to be associated with survival ( <0.05). The list of 486 genes was then submitted for pathway and biomarker analyses by Ingenuity Pathways Analysis (IP A). 53 genes were selected based on literatures supporting their roles in cancer for further analyses (Table 3).

Example 8: DPEP 1 and TPX2 are associated with cancer-specific mortality

[0076] Compared to microarray, qRT-PCR is a more accurate, specific and sensitive

RNA quantification method. Therefore, expression levels of these 53 genes were measured by qRT-PCR in tumor and non-tumor tissues in the Germany test cohort. The microarray gene expression data and qRT-PCR data were highly correlated (Spearman r = 0.89, O.0001, Fig. 5). qRT-PCR results showed that 36 genes were differentially expressed in the test cohort (P<0.01, Table 3). Of note, the top differentially expressed genes between tumor and non-tumor were TPX2, DCBLD2 and ANLN which were significantly increased in tumors (T:N ratio>2.0, O.01), whereas CDOl, DPEPl, C7, ALDH1A1 and NR3C2 which were significantly decrease in tumors (T:N ratio<0.2, O.01).

[0077] The association of gene expression with prognosis in Germany test cohort was also evaluated with qRT-PCR data. High and low expression was compared for each gene, consistent with microarray experiments for the test cohort. Decreased expression of DPEPl, or an increased expression of TPX2 and PRR1 1, was found to be associated with poor survival ( <0.05, Kaplan-Meier log-rank test) in the test cohort (Fig. 1 A).

[0078] To validate these findings, quantitative RT-PCR was performed for these three genes in Maryland validation cohort and their associations were analyzed for prognostic value. Low DPEPl expression or high TPX2 expression in tumor was associated with poor prognosis in the Maryland validation cohort ( =0.016 and =0.007 respectively, Kaplan-Meier log-rank test) (Fig. IB), consistent with our results in the Germany test cohort. PRRl 1 expression failed to show statistically significant association with prognosis in Maryland validation cohort (Fig. IB). Furthermore, DPEPl expression was decreased (T: N ratio of 0.1 in test cohort and 0.16 in validation cohort, O.01) and TPX2 expression was increased (T: N ratio of 2.14 in test cohort and 2.2 in validation cohort, O.001) in tumor tissue, compared to non-tumor tissues in both cohorts (Fig. 2).

[0079] The Germany and Maryland cohorts were similar in age, gender, resection margin status, TNM staging and overall survival. Therefore, to increase statistical power, these cohorts were combined for further analyses. Low DPEP l or high TPX2 was associated with poor cancer-specific mortality for all patients in the combined cohort ( O.01, Kaplan-Meier log rank; Fig. lC). When stratified by resection margin status (positive vs. negative), both DPEPl and TPX2 were associated with cancer-specific mortality in resection margin positive patients (P<0.01, Kaplan-Meier log rank; Fig. 9A). TPX2 was also associated with prognosis in resection margin negative patients (P<0.05, Kaplan-Meier log rank; Fig. 9B). These data evidence that TPX2 may be a useful biomarker to identify a subset of patients with poor prognosis in the resection margin negative patients, for which micro-metastasis is not yet detectable by current clinical methods. [0080] Multivariate Cox proportional hazards analysis was used to further evaluate the association of DPEP1 or TPX2 expression in tumors with prognosis in the combined cohort (Table 2). The dichotomized DPEP1 or TPX2 expression values were not associated with resection margin status or tumor stage. Univariate Cox regression analysis for all cases found that high DPEP1 (HR, 0.42; 95% CI, 0.23-0.75; =0.004), high TPX2 (hazard ratio (HR), 2.83; 95% CI, 1.51-5.33; P=0.001), and resection margin positive (HR, 1.93; 95% CI, 1.06-3.52; =0.030) were each associated with poor prognosis but not the tumor stage (Table 2).

Multivariate analyses showed that both high DPEP1 (HR, 0.51 ; 95% CI, 0.27-0.96) and high TPX2 (HR, 2.31; 95% CI, 1.184.52) were independent of one another and resection margin. Additionally, the multivariate model including DPEP1, TPX2 and resection margin did significantly better than a model without TPX2 or a model without DPEP1 (P =0.012 and =0.036 respectively, likelihood ratio test). Therefore, the combination of margin status, TPX2 expression and DPEP1 expression is significantly better at predicting prognosis in PDAC than any factor alone, demonstrating their potential as prognostic biomarkers.

[0081] Expression patterns of DPEP 1 and TPX2 were found to be associated with pancreatic cancer prognosis. A strong association of low DPEP1 or high TPX2 expressions in tumors was observed with poor survival in the Germany test cohort and the Maryland validation cohort. These associations were independent of each other and other clinical covariates in the combined cohort, indicating that DPEP 1 and TPX2 expression may be useful prognostic indicators, in addition to resection margin status, to help identify patients at a higher risk of pancreatic cancer specific mortality. High TPX2 expression in resection margin negative patients was found to be associated with poor prognosis. This association may help predict the clinical outcome in individuals for whom micro-metastasis may not be detectable by current clinical methods and identify patients who are candidates for more aggressive adjuvant therapies.

[0082] Loss of DPEP1 expression was associated with breast cancer, colorectal cancer and Wilms' tumour (Green et al, 2009, Breast Cancer Res Treat 1 13 :59-66; Mciver et al, 2004, Cancer Lett 209:67-74; Austruy et al, 1993, Cancer Res 53:2888-94). No link has previously been found between DPEP 1 and pancreatic cancer. The results disclosed herein showed for the first time that a marked reduction in DPEP1 expression in pancreatic tumor compared with non- tumor tissues and the association of low DPEP1 expression with poor survival.

[0083] In conclusion, the results presented herein provide evidence that DPEP1 and TPX2 are prognostic biomarkers, independent of resection margin status and other clinical covariates, in multiple cohorts of PDAC. Furthermore, an association of these two biomarkers with RAS/MAPK signaling pathway provides insights in developing efficient multi-target treatments for PDAC.

Example 9: DPEPl and TPX2 expression regulated by activated KRAS mutant.

[0084] Pancreatic adenocarcinoma is driven by activating mutation in the KRAS oncogene, which is present in more than 95% of all cases (Almoguera et al., 1988, Cell 53 :549- 54; Morris et al, Nat Rev Cancer 10:683-95). Given the dominant role oiKRAS mutation in PDAC, KRAS possibly could play a role in the observed alterations of DPEPl and TPX2 level in PDAC. Therefore, DPEP 1 and TPX2 expression were compared between KPC transgenic mice and the littermate wild-type control mice.

[0085] In the context of endogenous oncogenic KRAS^012® mutation expression in pancreas, KPC mice spontaneously develop invasive and widely metastatic pancreatic ductal adenocarcinoma that recapitulates the principal clinical, histopathological and genomic features of the cognate human PDAC (Hingorani et al, 2005, Cancer Cell 7:469-83). PanIN-1 lesions are observed in mutant mice as young as two weeks old. As the mice ages, higher-grade PanlNs develop with increasing frequency. A significant disease burden becomes apparent in animals starting at 10 weeks of age. Thus, this transgenic mice model allowed us to study the gene expression alteration of DPEP 1 and TPX2 from early to late stages of PDAC.

[0086] As demonstrate in Fig. 3, about 4-fold decrease ( O.001) in DPEP l level and a more than 3-fold increase (P<0.01 ) in TPX2 level were observed in KPC mice (MT) with constitutively activating KRAS^012®, compared to wild-type mice (WT). Expressions of DPEP l and TPX2 were similar between younger (6- 17 weeks with early stage of PDAC) and older KPC mice (24-28 weeks with late stage of PDAC). These data indicated that decreased DPEPl and increased TPX2 may be early events in pancreatic tumorigenesis, initiated by KRAS mutation.

[0087] KRAS expression in Panc-1 cells was knocked down by siRNA. Since these cells endogenously express mutant KRAS^012®, KRAS knockdown may affect expression of DPEP l and TPX2. DPEPl was found to be increased and TPX2 was decreased when KRAS expression was knocked down 90% (Fig. 4A). Interestingly, direct knocking-down of TPX2 also increased DPEPl level, indicating that TPX2 itself could be an upstream regulator for DPEPl .

Example 10: MEK-MAPK pathway is required in the regulation of TPX2 and DPEP l expression.

[0088] The mechanism of KRAS mediated regulation of DPEP 1 and TPX2 was determined by analyzing the MAPK pathway, the key downstream cascade oiKRAS signaling. Levels of DPEPl and TPX2 in Panc-1 cells in response to specific inhibitors that target MEK was measured. Inhibition of MEK by U0126 (10 μΜ) led to a significant increase in endogenous DPEP l mRNA levels and a reduction of TPX2 (Fig. 4B). These results indicate that MAPK pathway is required for the KRAS-mediated regulation of DPEP 1 and TPX2 in pancreatic cancer cells.

[0089] EGF (epidermal growth factor) represents a classical stimulator of the Ras- MAPK cascade (Giehl et al., 2000, Oncogene 19:2930-42). Therefore, it is possible that EGF also could modulate TPX2 and DPEPl expression in pancreatic cancer cells. EGF (20 ng/mL) treatment was found to significantly increase TPX2 and decrease DPEPl expression in Panc-1 pancreatic cancer cell line. The inhibitory effect of U0126 was also observed in Panc-1 cells treated with EGF. When Panc-1 cells were pretreated with inhibitor for 1 h before EGF treatment, U0126 abolished EGF mediated changes in the expression of DPEP l and TPX2 (Fig. 4B).

[0090] These data demonstrated that the MAPK pathway is an important mediator in the regulation of TPX2 and DPEP 1 expression by mutant KRAS or growth factor such as EGF in pancreatic cancer.

[0091] The discriminatory power of DPEPl and TPX2 to differentiate between tumor and nontumor tissue suggests that predictable changes of DPEPl and TPX2 expression patterns may occur during tumorigenesis and may be representative of PDAC. In the present study, DPEPl was found to be expressed at a lower level and TPX2 at a higher as compared to the adjacent non-tumor tissue in PDAC suggesting a tumor suppressor or oncogenic role, respectively, for these genes. These findings are consistent with the earlier reports providing evidence for altered expression of DPEPl and TPX2 in various malignancies.

[0092] Although the molecular mechanism of this association in pancreatic cancer remains unclear, there are several possible explanations regarding the role of DPEPl in tumorigenesis. DPEPl is a membrane bound dipeptidase and may be involved in the degradation of surrounding extracellular matrix components (Mciver et al, 2004,), a mechanism that would facilitate the invasion processes of tumors. DPEPl is also implicated in the metabolism of glutathione, an important antioxidant (24, 26). Reduce DPEP l expression leads to less glutathione with increased oxidative stress, which may promote carcinogenesis (Klaunig et al, 1998, Environ Health Perspect 106:Suppl 1 :289-95).

[0093] The TPX2 gene is located on the long arm of chromosome 20, at position

20ql 1.2. Amplification within 20q often occurs in various types of adenocarcinoma and is also prominent in pancreatic cancer (Beroukhim et a! . Nature 463 :899-905; Fukushige et al., 1997, Genes Chromosomes Cancer 19: 161-69). Copy number driven overexpression of TPX2 gene, has been described in giant-cell bone tumor, cervical, lung and ovarian carcinoma (Scotto et al, 2008, Genes Chromosomes Cancer 47:755-65; Tonon et al, 2005, PNAS 102:9625-30; Smith et al, 2006, Genes Chromosomes Cancer 45:957-66; Ramakrishna et al, PLoS One 5:e9983). In malignant astrocytoma and squamous cell lung cancer, TPX2 overexpression positively correlated with tumor grade and stage, with lympho-metastasis and was associated with poor survival rate (Ma et al, 2006, Clin Cancer Res 12: 1121-27; Li B et al. Brain Res 1352: 200-07). Recently, immunohistochemical staining of a tissue microarray showed that TPX2 protein level was higher in pancreatic tumors compared with their normal counterparts (36). Our study give the direct evidence from quantitative RT-PCR that TPX2 mRNA level was elevated in pancreatic tumor compared with surrounding non-tumor tissues. The results disclosed herein are the first evidence of a prognostic significance of TPX2 in multiple cohorts of PDAC. Increased TPX2 level was found to be useful to identify the high risk cases from the early stage patients even without detectable micro-metastasis.

[0094] The similar changes in DPEP l and TPX2 gene expression patterns were found in the pancreatic tumor tissues from a transgenic mouse model which spontaneously develops PDAC. This mouse model harbors mutant KRAS⁰¹²⁰ in pancreas and faithfully recapitulates multiple aspects of the human disease. The decrease in DPEPl and increase in TPX2 expressions were at the same level in both the early and late stages of pancreatic tumor development in KPC mice, suggesting that two genes might play an important role in the relatively early stages of KRAS driven pancreatic carcinogenesis. Consistently, gains in 20q were observed at the same frequency in early and advanced stages by using comparative genomic hybridization array and fluorescence in situ hybridization (Fukushige 1997).

[0095] Activating mutations of KRAS are the earliest consistently detected abnormality in the development of pancreatic cancer. The MAPK pathway is one of the most thoroughly analyzed downstream pathways of activated RAS (Lewis et al, 1998, Adv Cancer Res 74:49-

59). In various form of cancer, RAS-RAF-MEK-MAPK cascade plays a central role in mediating growth factor-triggered signals (Giehl, 2000; Roberts et al, 2007, Oncogene 26:3291-310). For instance, EGF has been found to promote pancreatic cancer cell migration and invasion by activating MAPK pathway and most human pancreatic carcinoma cells are characterized by overexpression of EGF and its receptor (EGFR) (Giehl, 2000; Friess et al, 1996, J Mol Med

74:35-42). Both KRAS and EGF were found to be upstream effectors that can suppress DPEPl and induce TPX2 in pancreatic cancer cells. Using the MEK-specific inhibitor U0126, the

MAPK signaling pathway was found to be required in the regulation of DPEPl and TPX2 expression. A significant increase (~2 fold) in DPEPl gene expression was also found when cells were treated with AZD6244 alone (PO.01) in 24h (Fig. 5A). However, LY294002 alone had no effect on DPEPl expression. These data indicate that MEK/ERK pathway but not PI3K pathway is involved in regulating DPEPl expression in pancreatic cancer.

[0096] A significant increase (~2 fold) in DPEPl gene expression was also found when cells were treated with AZD6244 alone (PO.01) in 24h (Fig. 5A). However, LY294002 alone had no effect on DPEPl expression. These data indicate that MEK/ERK pathway but not PI3K pathway is involved in regulating DPEP 1 expression in pancreatic cancer.

[0097] Given the association of high DPEP 1 and low TPX2 with a better prognosis in pancreatic cancer, a logical hypothesis is that a treatment, which can increase DPEP 1 and reduce TPX2 level, may improve patients' survival. The results described herein suggest a number of possible targets for pancreatic cancer treatment, such as MAPK pathway and TPX2 (Fig. 4C).

[0098] MEK inhibition by U0126 or AZD6244 was found to increase DPEP 1 and decrease TPX2 levels in Panc-1 cells. Previous studies found that U0126-mediated growth inhibition in different pancreatic cancer cell lines was associated with changes in the expression of the cell cycle regulatory proteins p21, p27, and cyclin Dl (Vip-Schneider et al, 2003, Pancreas 27:337-44). TPX2 is a microtubule-associated protein that plays a central role in mitotic spindle formation and therefore cell cycle progression (Gruss et al, 2001, Cell 104:83- 93). Thus, the results presented herein indicate that TPX2 is another putative mediator of cell cycle alteration in response to the inhibition of MAPK pathway.

[0099] Down-regulation of TPX2 reduced pancreatic cancer cell growth in culture, induced apoptosis, and inhibited growth in soft agar and in nude mice (Warner et al, 2009, Clin Cancer Res 15:6519-28). Results presented herein show that treatment with TPX2 targeting small interfering R As effectively knocked down TPX2, and at the same time increased DPEP l level, thereby demonstrating TPX2 as an ideal anticancer target.

[0100] Because tumorigenesis has been shown to be a multifaceted process involving a variety of potential therapeutic targets, combining different therapeutic concepts is likely to be required in the treatment of pancreatic cancer. An abundant literature has shown that combining MEK inhibitors with other signaling pathway inhibitors or conventional cytotoxic drugs represents a promising new strategy against cancer (Roberts 2007; Sebolt-Leopold et al, 2004, Nat Rev Cancer 4:937-47; Legrier et al, 2007, Cancer Res 67: 1 1300). The results presented herein provide a rationale for testing the combination of agents targeting the MAPK pathway and TPX2 in pancreatic cancer patients. Example 1 1 : Overexpression of DPEPl inhibits cell invasion in vitro.

[0101] We then compared the invasive ability of DPEPl cDNA transfectants and control GFP transfectants in Panel and MIApaca2 cells using 10% FBS as a chemoattractant in Matrigel invasion assays. DPEPl overexpressing cells showed markedly decreased invasive ability compared with control cells in both Panel and MIApaca2 (Fig. 7).

Example 12: DPEPl sensitizes pancreatic cancer cells to Gemcitabine

[0102] Rapid development of resistance in PDAC inevitably translates into poor patient outcomes[15]. To determine the effect of DPEPl expression on the sensitivity of pancreatic cancer cell lines to gemcitabine, we examined cytotoxicity of gemcitabine on Panel and MIApaca2 cells after 96h of drug exposure. We found that the relative cytotoxicity to gemcitabine at 0.2 μΜ for GFP control and DPEPl transfectants were 25% and 44%

respectively in Panel cells (Fig. 6A, O.01). The sensitivity of MIApaca2 to low dose of gemcitabine (0.06 μΜ) is also increased by DPEPl overexpression as compared to control cells (50% vs 28% of cytotoxicity, Fig. 6B). These results demonstrated that DPEPl overexpression significantly increased the sensitivity to gemcitabine (P<0.01) in pancreatic cancer cells.

[0103] Functional evidence that DPEPl inhibits tumor aggressiveness and enhances sensitivity to gemcitabine, suggests DPEP l as a candidate target for designing therapeutic strategies.

TABLE 1 Characteristics of population

Germany Maryland

cohort³ (n=45) cohort^b(n=27)

Age at enrollments (y) P

Mean(SD) 68.4 (7.5) 61.9 (10.6) 0.003^c

Range 47-83 38-82

Gender, no. (%) 0.63^d

Male 21 (47) 15 (56)

Female 24 (53) 12 (44)

Resection margin, no. (%) 0.46^d

Rl-2 23 (51) 17 (63)

R0 22 (49) 10 (37)

TNM stage, no. (%) 0.109^d

I 1 (2) 4 (15)

IIA 6 (14) 4(15)

IIB 21 (50) 15 (58)

III 9 (21) 1 (4)

IV 5 (12) 2 (8) ^a The survival information of three patients was not available in German cohort, so these patients were removed from analyses.

b For one patient with liver metastasis in the Maryland cohort, the exact stage of this patient is unclear, so this patient was removed from Fisher's exact test here. Because this patient already had liver metastasis, the tumor stage was treated as over IIB in the Cox regression. c T-test.

d Fisher's exact test.

TABLE 2 Cox regression analysis of DPEPl and TPX2 expression with cancer-specific mortality on combined Germany test cohort and Maryland validation cohort.

Variables Univariate analysis³ Multivariate analysis*³

(comparison/referent) HR (95% CI) P HR (95% CI) P

DPEP1 (high/low) 0.43 (0.24-0.76) 0.004 0.51 (0.27-0.94) 0.032

TPX2(high/low) 2.92 (1.57-5.40) 0.001 2.42 (1.27-4.61) 0.007

Resection Margin (R1/R0) 1.77 (0.99-3.18) 0.050 1.90 (1.05-3.45) 0.033

Grading (G3&4/1&2) 1.72 (0.97-3.04) 0.063

Tumor stage (IIB-IV/I-IIA) 1.58 (0.80-3.13) 0.191 ^a Univariate analysis is adjusted for cohort membership only

b Multivariate analysis is adjusted for cohort membership, TPX2, DPEPl, and resection margin status. Multivariate analysis used stepwise addition and removal of clinical covariates found to be associated with survival in Univariate model and final models include only those covariates that were significantly associated with survival (P<0.05).

TABLE 3. A list of 36 genes selected from microarray analysis and evaluated by RT-PCR in Germany test cohort.

RT-PCR Microarray analysis

Gene Tvs.N Tvs.N Tvs.N Tvs.N Hazard Cox

Symbol RefSeq Ratio p-value Ratio p-value Ratio p-value

ADAM 19 NM_. _033274 1.9 6.6E-05 1.8 1.3E-06 2.1 0.043

ADCY7 NM_. _001114 1.0 7.8E-01 1.4 2.1E-05 3.2 0.022

ALDH1A1 NM_. _000689 0.1 8.1E-12 0.5 1.1E-07 0.6 0.018

ANLN NM_. 018685 2.1 1.3E-05 4.0 2.0E-11 1.6 0.008

ARNTL2 NM_. _020183 1.5 9.6E-04 3.2 6.3E-10 1.4 0.044

C7 NM_. _000587 0.1 4.7E-09 0.4 1.3E-05 0.7 0.006

CAPRIN2 NM_. _001002259 0.6 3.8E-05 1.6 1.6E-06 1.9 0.035

CD01 NM_. _001801 0.1 9.4E-09 0.7 5.2E-08 0.4 0.031

CIT NM_. _007174 0.9 4.3E-01 1.7 4.2E-08 3.6 0.000

DCBLD2 NM_. _080927 2.1 2.9E-03 2.3 3.0E-08 1.7 0.011

DPEP1 NM_. _004413 0.1 2.4E-07 0.5 4.6E-05 0.6 0.043

ERCC3 NM_. _000122 0.5 1.8E-09 1.2 1.8E-04 11.1 0.016

FANCD2 NM_. _033084 0.7 6.4E-03 1.8 3.4E-08 2.3 0.026

FANCI NM_. _001113378 0.9 1.3E-01 1.8 5.8E-08 2.1 0.023

FGD6 NM_. _018351 1.2 1.1E-01 2.2 8.5E-11 1.7 0.050

ITGA3 NM_. _002204 1.9 1.2E-06 2.9 3.6E-12 1.6 0.035

KIF23 NM_. _138555 0.9 4.9E-01 2.6 1.6E-09 2.0 0.003

KNTC1 NM_. 014708 0.7 6.5E-05 1.6 7.8E-07 2.4 0.015

NCAPD2 NM_. _014865 0.6 1.6E-06 1.5 1.3E-07 2.7 0.021

NOSTRIN NM 001039724 0.3 7.0E-10 0.6 1.3E-05 0.4 0.003 NR3C2 NM_. _000901 0.2 5.6E-1 1 0.6 2.1 E-07 0.5 0.019

PAFAH1 B2 NM_. 002572 0.4 2.5E-06 1.2 9.0E-06 4.9 0.050

PRC1 NM_. _003981 0.8 1.2E-01 2.0 5.2E-09 2.2 0.007

PRR1 1 NM_. _018304 1.0 8.0E-01 2.1 1.3E-08 2.1 0.005

RACGAP1^* NM_. _013277 NA NA 2.0 8.2E-09 3.1 0.005

RALGAPB NM_. _020336 0.4 3.1 E-09 1.1 7.4E-05 1 1 .1 0.042

SEC14L2 NM_. _012429 1.2 1 .2E-01 1.6 6.3E-07 2.2 0.029

SEMA3A NM_. _006080 0.5 9.5E-05 1.8 8.0E-05 1.8 0.035

SLC20A1 NM_. _005415 1.0 7.0E-01 1.9 8.7E-08 2.0 0.018

SPOCK1 NM_. _004598 1.6 2.5E-03 1.9 8.4E-10 2.5 0.005

TGFB1 NM_. 000660 1.0 6.0E-01 1.6 1.4E-05 2.2 0.049

TMEM194A NM_. _001 130963 0.4 7.8E-09 1.4 1 .3E-05 3.2 0.009

TPX2 NM_. _0121 12 2.1 4.7E-06 2.2 2.6E-08 2.1 0.001

TRIO NM_. _0071 18 0.5 1 .8E-09 1.5 3.9E-06 5.9 0.004

WDHD1 NM_. _007086 0.7 1 .2E-05 1.7 1.1 E-06 2.9 0.001

YEATS2 NM 018023 0.6 2.9E-06 1.5 9.5E-08 3.6 0.009

^* For RACGAP1 , samples with a cycle value greater than 36 were considered of poor quality and removed.

Claims

What is Claimed:

1. A method of evaluating treatment of pancreatic ductal adenocarcinoma (PDAC) in a patient, comprising

assaying an expression level of dipeptidase 1 (DPEP1) in a PDAC tissue of the patient; and

assaying an expression level of targeting protein for Xklp2 (TPX2) in the PDAC tissue.

2. The method of claim 1, further comprising

assaying the expression level of DPEP 1 in a normal pancreatic tissue of the patient; and

assaying the expression level of TPX2 in the normal pancreatic tissue.

3. The method of claim 1, further comprising

assaying the expression level of DPEP 1 in a normal pancreatic tissue of at least one normal human subject; and

assaying the expression level of TPX2 in the normal pancreatic tissue of the normal human subject.

4. The method of claim 1 further comprising

administering a cancer treatment to the subject;

assaying the expression level of dipeptidase 1 (DPEP1) in a PDAC tissue of the patient following treatment; and

assaying the expression level of targeting protein for Xklp2 (TPX2) in the PDAC tissue following the treatment.

5. The method of claim 4, wherein the levels of DPEP 1 and TPX2 change following the

treatment.

6. The method of claim 4, wherein the levels of DPEP 1 and TPX2 do not change following the treatment.

7. The method of any of claims 1-6, wherein the assay for the expression levels of DPEP 1 comprises

extracting RNA from the tissue; and

contacting the extracted RNA with at least one single stranded oligonucleotide to measure levels of DPEP 1 mRNA.

8. The method of any of claims 1-6, wherein the assay for the expression levels of TPX2

comprises

extracting RNA from the tissue; and contacting the extracted RNA with at least one single stranded oligonucleotide to measure levels of TPX2 mRNA.

9. The method of any of claims 1-8, further comprising measuring levels of mRNA by

quantitative PCR.

10. The method of any of claims 1-8, wherein the RNA is contacted with a microarray

comprising a multiplicity of single stranded oligonucleotides to measure tissue levels of mRNA.

1 1. The method of any of claims 1-10, further comprising contacting measuring levels of DEP l or TPX2 mRNA in a second sample of RNA.

12. The method of claim 1 1, wherein the second sample of RNA is from normal pancreatic tissue of the patient.

13. The method of claim 1 1, wherein the second sample of RNA is from pancreatic tissue of at least one normal human subject.

14. The method of claim 1 1, wherein the sample consists of a multiplicity of tissue samples.

15. The method of claim 14, wherein the multiplicity of tissue samples is obtained from a

multiplicity of subjects.

16. The method of claim 1 1, wherein the second sample of RNA is from a second PDAC tissue sample of the patient at an earlier time during the cancer treatment.

17. The method of any of claims 1-10, further comprising measuring levels of a control RNA selected from the group consisting of GAPDH, beta-actin, and 18S RNA.

18. The method of claim 4, wherein the cancer treatment comprises administering a kinase

inhibitor.

19. The method of claim 18, wherein the cancer treatment further comprises administering a cytotoxic agent.

20. The method of any of claims 1-19, wherein the single stranded oligonucleotide hybridizes with the nucleic acid having the sequence of DPEP 1 (SEQ ID NO: l) or TPX2 (SEQ ID NO:2), or a known genetic variant of said sequence.

21. A method of using treating pancreatic ductal adenocarcinoma (PDAC) in a patient,

comprising

assaying an expression level of dipeptidase 1 (DPEP1) in a PDAC tissue of the patient;

assaying an expression level of targeting protein for Xklp2 (TPX2) in the PDAC tissue; and

administering a kinase inhibitor.

22. The method of claim 21, wherein the kinase inhibitor targets the RAS/MEK/ERK pathway.

23. The method of claim 22, wherein the kinase inhibitor targets KRAS.

24. The method of claim 22, wherein the kinase inhibitor targets MAPK.

25. The methods of any of claims 21-24, further comprising administering a cytotoxic agent.

26. The method of claim 19 or 25, wherein the cytotoxic agent is a chemotherapy or a high dose of radiotherapy.

27. The method of claim 26, wherein the chemotherapy is Gemcitabine.

28. A method of using expression levels of dipeptidase 1 (DPEP l) for evaluating a survival time of a patient with pancreatic ductal adenocarcinoma (PDAC), comprising

causing a measurement of an expression level of DPEPl in a PDAC tissue of a patient;

causing a comparison of the expression level of DPEPl in the PDAC tissue to a reference level of DPEP 1 ; and

causing a prognosis to be made based on the difference between the PDAC levels compared to the normal levels.

29. The method of claim 28, further comprising

causing a measurement of an expression level of targeting protein for Xklp2 (TPX2) in the PDAC tissue;

causing a comparison of the expression level of TPX2 in the PDAC tissue to a reference normal level of TPX2; and

causing a prognosis to be made based on the difference between the PDAC levels of both DPEP 1 and TPX2 measured in the patient to the reference levels of DPEP 1 and TPX2, respectively.

30. The method of claim 29 further comprising the steps of:

collecting at least one first sample from a PDAC patient;

administering to the subject a cancer treatment;

collecting at least one second sample following said treatment;

measuring levels of DPEPl or TPX2 in each of the samples;

comparing levels of DPEP 1 or TPX2 before and after treatment;

providing a prognosis, wherein the prognosis is based on the difference in the levels of DPEP 1 or TPX2 before and after treatment.

31. The method according to any of claims 28-31 to identify high-risk PDAC patients.

32. The method use according to claim 30 to evaluate survival time of the patient.

33. The use according to claim 30 to develop a therapy for treating PDAC.

34. The method of claim 30, wherein the levels of DPEPl and TPX2 change following the treatment.

35. The method of claim 34, wherein the prognosis is favorable.

36. The method of claim 30, wherein the levels of DPEPl and TPX2 do not change following the treatment.

37. The method of claim 36, wherein the prognosis is unchanged following treatment.

38. The method of claim 30, wherein the measuring of DPEP l or TPX2 comprises the steps of:

extracting RNA from the first and second samples;

contacting said RNA with at least one nucleic acid probe to measure levels of DPEPl or TPX2.

39. The method of claim 38, wherein the RNA is extracted from tissue obtained from each

sample.

40. The method of claim 39, wherein the RNA is extracted from tumor tissue.

41. The method any of claims 38-40, further comprising measuring levels of DPEP 1 or TPX2 by quantitative PCR.

42. The method any of claims 38-40, wherein the RNA is contacted with a microarray

comprising a multiplicity of single stranded oligonucleotides to measure tissue levels of DPEPl or TPX2.

43. The method of claim 40, further comprising contacting said RNA with at least one single stranded oligonucleotide to measure levels of a control RNA.

44. The method of claim 43, comprising contacting said RNA with at least one additional single stranded oligonucleotide to measure levels of a control RNA.

45. The method of claim 44, wherein the control RNA is selected from the group consisting of GAPDH, beta-actin, and 18S RNA.

46. The method of claim 30, wherein the sample consists of a multiplicity of tissue samples.

47. The method of claim 46, wherein the multiplicity of tissue samples is obtained from a

multiplicity of subjects.

48. The method of claim 30, wherein the second sample consists of a multiplicity of tissue

samples.

49. The method of claim 48, wherein the multiplicity of tissue samples is obtained from a

multiplicity of subjects.

50. The method of claim 48, further comprising a step of calculating a median or average level of DPEPl or TPX2 in the multiplicity of tissue samples.

51. The method of claim 30, further comprising steps of collecting tissue samples from a multiplicity of normal subjects, determining the median or average level of DPEPl or TPX2 in normal subjects, and providing a prognosis based on a comparison of the levels of DPEPl or TPX2 of the PDAC patient with the median or average level of DPEPl or TPX2 of normal subjects.

52. The method of claim 30, further comprising a step of measuring a reference level of DPEPl or TPX2.

53. The method of claim 52 wherein the reference level of DPEPl or TPX2 is the median or average level of DPEPl or TPX2 of normal subjects.

54. The method of claim 52, wherein the reference level of DPEPl or TPX2 is the median or average level of DPEPl or TPX2 in tissues of PDAC patients.

55. The method of claim 52, wherein reference level of DPEPl or TPX2 is the median or

average level of DPEPl or TPX2 in non-cancerous tissue of PDAC patients.

56. The method of claim 52, further comprising a step of making a comparison of the level of DPEPl or TPX2 in the first sample of the subject with cancer to the reference level of DPEPl or TPX2.

57. The method of claim 56, further comprising a step of providing a prognosis based on the comparison.

58. The method of claim 57, wherein the prognosis is favorable for an anti-cancer treatment.

59. The method of claim 58, where the level of DPEP l or TPX2 in the subject with cancer is statistically the same as the reference level of DPEPl or TPX2.

60. The method of claim 57, wherein the prognosis is not favorable for a kinase inhibitor

treatment.

61. The method of claim 60, where the level of DPEP l or TPX2 in the subject with cancer is statistically substantially different than the reference level of DPEPl or TPX2.

62. The method of claim 48, wherein the samples are collected from the subject with cancer at various times before and after anti-cancer treatment.

63. The method of claim 49, further comprising comparing the level of DPEPl or TPX2 in the first sample with the level of DPEPl or TPX2 in each of the multiplicity of second samples.

64. The method of claim 63, wherein the prognosis is favorable.

65. The method of claim 63, wherein the levels of DPEPl or TPX2 changes over time following treatment.

66. The method of claim 61, further comprising providing an alternate treatment modality.

67. The method of claim 66, wherein the alternate treatment modality comprises administering a kinase inhibitor treatment.

68. The method of claim 67, further comprising administering a cytotoxic drug.

69. The method of any of claims 28-30, wherein the nucleic acid probe is a single stranded

nucleic acid.

70. The method of claim 69, wherein the single stranded probe hybridizes with the nucleic acid having the sequence of DPEPl (SEQ ID NO: 1) or TPX2 (SEQ ID NO:2), or a known genetic variant of said sequence.