WO2011094847A1

WO2011094847A1 - Methods and compositions for diagnosing and treating patients having multiple myeloma that respond to statin therapy

Info

Publication number: WO2011094847A1
Application number: PCT/CA2011/000122
Authority: WO
Inventors: Linda Z. Penn; James W. Clendening; Paul C. Boutros
Original assignee: Penn Linda Z; Clendening James W; Boutros Paul C
Priority date: 2010-02-04
Filing date: 2011-02-04
Publication date: 2011-08-11

Abstract

A method of determining whether a sample or cancer cell from a subject is likely to be sensitive to a treatment that depletes mevalonate such as statin treatment comprising: determining whether the sample or cancer cell has a dysregulated mevalonate pathway, for example by determining a level of gene expression activity or gene copy number of one or more genes listed in Figure 4 and/or in Tables 3, 4, 5 and/or 6. Dysregulation of the mevalonate pathway and/or expression level patterns that are similar to statin sensitive cells is indicative that the cancer cell is likely sensitive to for example statin treatment.

Description

Methods and compositions for diagnosing and treating patients having multiple myeloma that respond to statin therapy

[0001] This is a Patent Cooperation Treaty Application which claims the benefit of 35 U.S.C. 119 based on the priority of corresponding U.S. Provisional Patent Application No. 61301304, filed February 4, 2010, which is herein incorporated by reference in its entirety.

Field of the Disclosure

[0002] The present disclosure pertains to methods and compositions for identifying cancer patients who respond to statin therapy and particularly to methods and compositions for identifying and treating patients having multiple myeloma who respond to statin therapy.

Background of the Disclosure

[0003] Statins are a family of hydroxymethylglutaryl coenzyme A reductase (HMGCR) inhibitors commonly used to treat patients with hypercholesterolemia that are also known to induce apoptosis in a variety of types of tumor cells. To date, there are several lines of preclinical and epidemiological evidence to support the anti-cancer potential of statins.^1,2 Epidemiological studies have demonstrated up to 50% reduction in cancer risk among statin users^3"5 and partial or complete responses have been observed in some patients enrolled in phase l/ll trials.^6"12 These responses underscore the importance of reliably identifying the subset of patients who stand to benefit most from statin-based anti-cancer therapy. To advance statins as anti-cancer agents, it is therefore crucial to understand the molecular mechanisms involved in their anti-cancer activity and to delineate markers which distinguish the subset of tumors that are sensitive to statin-induced apoptosis.

[0004] Statin-induced apoptosis results directly from inhibiting HMGCR, the rate-limiting enzyme of the mevalonate (MVA) pathway.² The MVA pathway is a complex biochemical pathway required for the generation of several fundamental end-products including cholesterol, isoprenoids, dolichol, ubiquinone, and isopentenyladenine.^2,13 Both HMGCR and the MVA pathway received considerable attention 20-30 years ago through the Nobel Prize winning efforts of Goldstein and Brown and the development of statins as blockbuster cholesterol- lowering drugs. This work defined how inhibition of HMGCR in non-transformed cells triggers a robust homeostatic feedback response that ensures the cells upregulate the mevalonate pathway.¹³ Once statins have blocked HMGCR and depleted the intracellular end-products of the MVA pathway, cytoplasmic, transcription factors known as sterol regulatory element binding proteins (SREBPs) are activated.¹⁴ These transcription factors translocate to the nucleus, bind DNA at promoter regions containing sterol response elements (SREs), and induce the transcription of several key target genes including HMGCR and the low-density lipoprotein receptor (LDLR). Upregulated LDLR on the cell surface then binds and internalizes extracellular LDL-laden cholesterol, thus reducing plasma cholesterol. It is this extraordinary feedback mechanism that has been successfully exploited to control hypercholesterolemia with statins.^15,16

[0005] One of the tumor types that is sensitive to statin-induced apoptosis is multiple myeloma (MM).^17"21 MM is a plasma cell malignancy with a median survival time of 5-10 years despite the use of high-dose chemotherapy and autologous stem cell transplants.^22,23 As such, there is an urgent need for advances in both molecular diagnosis and treatments. Novel therapeutics are currently under investigation in MM but most, with the recent exceptions of bortezomib, thalidomide, and lenalidomide, have yet to show substantial efficacy and will require considerable pre-clinical and toxicity testing. Statins have an established track record for safety and statin-induced apoptosis is tumor-specific with limited collateral damage to non-transformed cells.^24,25 These agents are therefore poised to make an immediate impact on cancer patient care.

Summary of the Disclosure

[0006] In an aspect, the disclosure includes a method of determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to a treatment that depletes levels of mevalonate comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to the treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to the treatment. [0007] In an embodiment, the method includes a step prior to determining whether a cancer cell and/or cancer has a dysregulated mevalonate pathway, of obtaining a sample from the subject. In an embodiment, the step of determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway comprises determining a level of one or more genes selected from the genes listed in Figure 4 and/or listed in Tables 3-6, in a sample from the subject; and comparing each level to a control, wherein an altered level of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer has a dysregulated mevalonate pathway.

[0008] In an aspect the disclosure includes a method of determining whether a cancer cell from a subject is likely to be sensitive to a treatment that depletes levels of mevalonate comprising: determining a level of one or more genes selected from the genes listed in Figure 4 and/or listed in Tables 3-6, in a sample from the subject; and comparing each level to a control, wherein an altered level of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer is likely to be sensitive to a treatment that depletes levels of mevalonate.

[0009] In an embodiment, the treatment that depletes mevalonate is an HMGCR inhibitor. In an embodiment, the HMGCR inhibitor is a statin.

[0010] In a further aspect the disclosure includes a method of determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to statin treatment comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to statin treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to statin treatment.

[0011] In another aspect the disclosure includes a method of determining whether a cancer cell from a subject is likely to be sensitive to statin treatment comprising: determining a level of one or more genes selected from the genes listed in Figure 4 and/or listed in Tables 3-6, in a sample from the subject; and comparing each level to a control, wherein an altered level of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer is likely to be sensitive to a treatment that depletes levels of mevalonate.

[0012] In an embodiment, the method comprises determining a level of gene expression or level of polypeptide activity of one or more genes selected from the genes listed in Figure 4 and/or listed in Tables 3-6, in a sample from the subject; and comparing the level to a control, wherein an altered level of gene expression or level of polypeptide activity in the sample of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer has a dysregulated mevalonate pathway. In an embodiment, the method comprises determining a gene copy number of one or more genes selected from the genes listed in Figure 4 and/or listed in Tables 3-6, in a sample from the subject; and comparing the gene copy number to a control, wherein an altered gene copy number in the sample of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer has a dysregulated mevalonate pathway.

[0013] In an embodiment, the method comprises determining a level of gene expression of one or more, and optionally one, gene selected from Tables 4, 5 and/or 6. In yet another embodiment, the one or more genes comprises HMGCS1. In another embodiment, the one or more genes comprises HMGCR, including any isoform or variant of HMGCR, such as HMGCR-FL or HMGCR-D 3.

[0014] In an embodiment, the method comprises determining a statin induced HMGCR level or HMGCS1 level in a sample from the subject; and comparing the HMGCR level to a control, wherein a decreased level of HMGCR and/or HMGCS1 is indicative the cancer cell is sensitive to the treatment. In an embodiment, the level determined comprises enzymatic activity.

[0015] In a further embodiment, the method comprises determining a profile such as an expression profile by measuring the gene expression levels of a plurality of genes selected from the genes listed in Figure 4 and/or Tables 3-6 in a sample of a subject; and classifying the cancer cell and/or cancer as likely sensitive or likely insensitive to statin treatment based on the expression profile.

[0016] In an embodiment, the cancer is a hematological cancer, for example multiple myeloma (MM). [0017] In an embodiment, the one or more genes comprise the 4, 5, or 20 gene signature.

[0018] In an embodiment, the method comprises comparing, an expression profile of a sample of a subject, the expression profile comprising measurements of expression levels of a plurality of genes, to one or more reference profiles comprising measurements of expression levels of the plurality of genesand associated with statin treatment sensitivity or statin treatment insensitivity, the plurality of genes selected from the genes listed in Figure 4 and/or genes listed in Tables 3, 4, 5 and/or 6; and classifying, for example using a computer, the cancer cell as sensitive to statin treatment or insensitive to statin treatment, wherein the similarity of the expression profile to one of the reference profiles indicates the statin sensitivity of the cancer cell or cancer.

[0019] A further aspect of the disclosure includes a method of treating a subject with cancer or reducing tumor burden in the subject comprising: identifying a subject with a cancer sensitive to a treatment that depletes mevalonate for example a statin treatment according to a method described herein; and administering a suitable treatment optionally a statin or a composition comprising a statin to the subject.

[0020] In an embodiment, the method comprises: administering to a subject in need thereof for treatment of a cancer an effective amount of a treatment that depletes mevalonate such as statin, indicated by the expression level of one or more genes selected from the genes listed in Table 4 and/or Tables 3-6 in a sample from the subject compared to a control.

[0021] A further aspect is a composition comprising two or more analyte specific reagents (ASR) for detecting a gene expression product of one or more genes listed in Figure 4 and/or Tables 3-6.

[0022] Yet another aspect includes an array comprising for each gene in a plurality of genes, the plurality of genes comprising at least 2 of the genes listed in Figure 4, Table 3, 4, 5 and/or 6, one or more nucleic acid probes complementary and hybridizable to a coding sequence in the gene.

[0023] Another aspect provided by the disclosure includes a kit for determining statin sensitivity of a cancer cell and/or for treating a statin sensitive cancer comprising a composition and/or an array described herein, and in an embodiment one or more specimen collectors, and/or RNA preservation solution and/or one or more statins for treating a statin sensitive cancer.

[0024] Other features and advantages of the present disclosure will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples while indicating preferred embodiments of the disclosure are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description.

Brief description of the drawings

[0025] Figure 1. Microarray analysis reveals distinct differences in mRNA levels in response to lovastatin in sensitive and insensitive MM cells. Three independent biological replicates of KMS11 , H929, LP1 , and SKMM1 cells were exposed to 20 μΜ lovastatin or a vehicle control for 16 hours prior to being harvested for mRNA abundance profiling by microarray. (A) The entire dataset was visualized using unsupervised machine-learning. The resulting heatmap demonstrates that global expression patterns of the sensitive cells were much more similar to each other than to insensitive cells. (B) Six MVA pathway genes, including HMGCR, are plotted from the microarray to show that all were upregulated in both LP1 and SKMM1 , but not substantially in KMS 1 and H929 cells, in response to lovastatin exposure. *^* p < 0.0001 ; model-based t-test with Bayesian moderation of standard error and false-discovery adjustment. Results were validated by real-time PCR for both HMGCR (C) and HMGCS1 (D), measured relative to GAPDH. ^* p < 0.05; Student's T-test with Welch's adjustment for heteroscedasticity comparing ethanol to lovastatin treatments. (E) MM cells were exposed to 20 μΜ lovastatin or a vehicle control for 24 hours prior to being harvested for protein lysates. Immunoblots were probed with anti- HMGCR and anti-tubulin as a loading control. All experiments were performed a minimum of three times and data represent means and standard deviations.

[0026] Figure 2. Real-time PCR analysis shows that both HMGCR splice variants are preferentially upregulated by insensitive MM cells exposed to lovastatin. (A) A schematic illustrating the real-time PCR strategy used to detect HMGCR-FL (/), HMGCR-D13 (/^"/), and total endogenous HMGCR (///). MM cells were exposed to the indicated concentrations of lovastatin for various lengths of time and assayed for HMGCR-FL (B) or HMGCR-D13 (C) expression by real-time PCR using primers /^' and /^'/^' (see Figure 2A), respectively, measured relative to GAPDH. Both the dose range for 16 hours (left) and time course at 20 μΜ lovastatin (middle) indicated that LP1 cells upregulated HMGCR expression more significantly than KMS11 cells. This differential was extended to include other sensitive and insensitive MM cell lines exposed to 20 μΜ lovastatin for 16 hours (right). ^* p < 0.05; Student's t-test with Welch's adjustment for heteroscedasticity. All experiments were performed a minimum of three times and data represent means and standard deviations.

[0027] Figure 3. Ectopic expression of catalytic domain HMGCR-FL, but not HMGCR-D13, reduces sensitivity to lovastatin in sensitive MM cells. (A) Schematic of the ectopic HMGCR catalytic domain constructs. (B) KMS11 and LP1 cell lines ectopically expressing either the empty GFP vector control, cHMGCR-FL or cHMGCR-D13 constructs were generated and assessed for transcript expression of HMGCR-FL (left), HMGCR-D13 (middle), and total endogenous HMGCR (right) using primers /^', /^'/^', and /// (see Figure 2A), respectively, by real-time PCR, measured relative to GAPDH. (C) KMS11 cells ectopically expressing either the empty GFP vector control, cHMGCR-FL, CHMGCR-D13, or BCL2 were assessed for protein expression with anti-HMGCR, anti-BCL2, and anti-actin as a loading control. (D) KMS11 cells expressing the cHMGCR constructs were exposed to increasing concentrations of lovastatin in an MTT assay to measure cell viability (left). Only the cells expressing cHMGCR-FL demonstrated an increase in their MTT₅₀ for lovastatin, the concentration that is required to reduce viability of the population by 50% (right). (E) Cells expressing the vector control, cHMGCR-FL, CHMGCR-D13, or BCL2 were also exposed to increasing concentrations of either lovastatin (left), melphalan (middle), or bortezomib (right) and assayed for the proportion of their pre-G1 populations by fixed PI. ^* p < 0.05; Student's t-test with Welch's adjustment for heteroscedasticity comparing expression in the ectopic construct expressing cells to the GFP control. All experiments were performed a minimum of three times and data represent means and standard deviations.

[0028] Figure 4. Analysis of the basal mRNA expression of sterol- responsive genes identified HMGCS1 , but not LDLR, to be more highly expressed in insensitive MM cells compared to sensitive cells. A publically available dataset comprised of basal expression profiles for many MM cell lines was mined for sterol-responsive genes that are differentially expressed in sensitive and insensitive MM cell lines. (A) The log2 fold difference between insensitive and sensitive MM cells revealed that HMGCS1 (black arrow) but not LDLR (white arrow) was more highly expressed in insensitive cells. (B) The p-values assessing the significance of any given gene's differential expression showed that HMGCS1 (black arrow) expression was more significantly different in sensitive and insensitive MM cells than most other genes, including LDLR (white arrow). mRNA from representative sensitive and insensitive MM cell lines was harvested for real- time PCR analysis of the expression of HMGCS1 (C) and LDLR (D), measured relative to GAPDH.

[0029] Figure 5. Like HMGCR, the expression of HMGCS1 , but not LDLR, is also differentially regulated in response to lovastatin exposure in insensitive MM cells. MM cells were exposed to the indicated concentrations of lovastatin for various lengths of time and assayed for HMGCS1 (A) or LDLR (B) expression by real-time PCR, measured relative to GAPDH. Both the dose range for 16 hours (left) and time course at 20 μΜ lovastatin (middle) indicated that LP1 cells upregulated HMGCS1 expression, but not LDLR, more significantly than KMS11 cells. This differential was extended to include other sensitive and insensitive MM cell lines exposed to 20 μΜ lovastatin for 16 hours (right). * p < 0.05; Student's t- test with Welch's adjustment for heteroscedasticity. All experiments were performed a minimum of three times and data represent means and standard deviations.

[0030] Figure 6. Statin-sensitive primary patient MM cells express lower levels of HMGCR and show a lack of its upregulation upon statin exposure. Mononuclear cells freshly isolated from bone marrow aspirates were cultured in the presence of a vehicle control, 20 μΜ lovastatin, or 20 μΜ atorvastatin. After 16 hours, a portion of the sample was sorted for the CD138+ MM population and RNA was harvested for cDNA synthesis and real-time PCR. (A) The remainder was exposed to statin or control for a total of 48 hours prior to being labeled with anti-CD138-PE and FITC-conjugated annexin V for apoptosis analysis. Two samples were identified as being sensitive to statin-induced apoptosis by a decrease in the viable CD138+ MM population (top left quadrant) and three were insensitive; representative samples are shown. (B) Real-time PCR was employed to assess the expression of HMGCR-FL, HMGCR-D13, and HMGCS1 , all measured relative to GAPDH. Data represent individual measurements.

[0031] Figure 7. When statin-sensitive MM tumors are identified, atorvastatin can be used safely and effectively to decrease tumor burden. Sub- lethally irradiated NOD/SCID mice were intravenously injected with KMS11-luc cells. The animals received 10 or 50 mg/kg of atorvastatin or a PBS vehicle control by oral gavage three times a week for 37 days, until the tumor bioluminescence in the control mice saturated the detectors. When subsequently injected with luciferin, the bioluminescent myeloma cells in these animals were imaged (A; Day 31) and quantified over several weeks (B). * p < 0.001 ; One-way analysis of variance comparing each atorvastatin group to the PBS group. (C) Survival curves were determined based upon when the mice were humanely euthanized after the onset of hind-limb paralysis due to tumor burden. Arrows indicate the beginning (day 2) and end points (day 37) of treatment. Each group comprised of 7-8 mice and data represent means and standard deviations.

[0032] Figure 8. MM cell lines show a dichotomized response to lovastatin. In a previous study²¹ it was determined that, of 17 MM cell lines assayed, 8 were relatively sensitive to lovastatin-induced apoptosis while 9 were relatively insensitive. Apoptosis has been ascertained by a combination of terminal dUTP nick-end labeling (TUNEL), immunoblotting for poly(ADP-ribose) polymerase (PARP) cleavage, and fixed propidium iodide (PI) experiments as recommended in the quantification of cell death.²⁶ (A) Summary table listing those cell lines identified as being either relatively sensitive or insensitive to lovastatin- induced apoptosis.²¹ Cell lines were classified as sensitive if evidence of both TUNEL-positive staining (>10%) and PARP cleavage was observed after exposure to 20 μΜ lovastatin for 48 hours. By this approach, the following 8 of 17 cell lines (in alphabetical order) were shown to be sensitive to lovastatin-induced apoptosis: 8226, H929, KHM11 , KMM1, KMS11 , MM-S1 , OCI MY7, and OPM2. Those considered insensitive to lovastatin-induced apoptosis (also alphabetically) include ANBL-6, ARK, EJM, JJN3, LP1 , MM.1-144, OCI MY5, SKMM1, and U266. (B) Fixed PI data for two sensitive (KMS 1 and H929) and two insensitive (LP1 and SKMM1) cell lines exposed to ethanol, 5 μΜ, or 20 μΜ lovastatin for 48 hours. ^* p < 0.05; Student's t-test with Welch's adjustment for heteroscedasticity comparing lovastatin-exposed cells to vehicle control-exposed cells. Experiments were performed three times and data represent means and standard deviations.

[0033] Figure 9. Intracellular cholesterol levels are similar in statin- sensitive and statin-insensitive MM cells. Three independent biological replicates of 5 million KMS11 , H929, LP1 , and SKMM1 cells were exposed to 20 μΜ lovastatin or a vehicle control for 48 hours prior to being harvested for lipid analysis as has been described previously.²⁷ Cells were cultured and treated in full serum (10% FBS), the same conditions under which differentials in sensitivity to statin-induced apoptosis and in regulation of MVA pathway gene expression were demonstrated. Briefly, lipids from cell sonicate were extracted²⁸ in the presence of tridecanoylglycerol as the internal standard, and phospholipids were digested by phospholipase C.²⁹ Samples were derivatized with Sylon BFT (Supelco, Bellefonte, PA) and analyzed by gas chromatography (Agilent Technologies, 6890 Series equipped with a flame ionization detector; Palo Alto, CA). Samples were injected onto a Phenomenex high performance capillary column (ZB-5, 15 m x 0.32 mm x 0.25 pm).

[0034] Figure 10. Heatmap for 4 gene signature. A four-gene signature predicting response to statins was developed using a two-stage feature selection. First, genes were ranked in ascending order of univariate predictive capacity (as assessed by a two-tailed t-test). Second, the coefficient of variation was calculated for the top ten univariate genes. Third, the top four of these were integrated using a 10,000-tree Random Forest supervised machine-learning classifier. This is an unsupervised representation of those four genes using the DIANA divisive hierarchical clustering algorithm with genes as columns and cell-lines as rows. The intensity of each cell reflects the signal intensity of that gene on that microarray. The greyscale bar on the right of the figure indicates perfect separation between sensitive and resistant cell-lines.

[0035] Figure 11. Heatmap for 20 gene signature. A twenty-gene signature predicting response to statins was developed using a two-stage feature selection. First, genes were ranked in ascending order of univariate predictive capacity (as assessed by a two-tailed t-test). Second, the top twenty of these were integrated using a 10,000-tree Random Forest supervised machine-learning classifier. This is an unsupervised representation of those twenty genes clustering algorithm with genes as columns and cell-lines as rows. The intensity of each cell reflects the signal intensity of that gene on that microarray. The greyscale bar on the right of the figure indicates perfect separation between sensitive and resistant cell-lines.

[0036] Figure 12. Heatmap for 5 gene signature. A 5-gene signature predicting response to statins was developed using a one-stage feature selection. For each gene on the microarray, we determined if there was perfect separation between known statin-sensitive and known statin-insensitive cell-lines. Here, perfect separation indicates that either all statin-sensitive cell-lines had higher signal intensities than all statin-insensitive cell-lines or that all statin-sensitive cell- lines had lower signal intensity than all statin-insensitive cell-lines. This analysis produced a set of 17 genes, which was then further filtered using a background threshold cutoff of 500 intensity units. This retained five genes, which were integrated using a 10,000-tree Random Forest supervised machine-learning classifier. This is an unsupervised representation of those five genes using the DIANA divisive hierarchical clustering algorithm with genes as columns and cell- lines as rows. The intensity of each cell reflects the signal intensity of that gene on that microarray. The greyscale bars on the right of the figure indicates near-perfect separation between sensitive and resistant cell-lines.

[0037] Figure 13. Venn diagram. To assess the gene-wise overlap between the 4-gene, 20-gene, and 5-gene signatures a gene-wise Venn diagram was constructed. All four genes present in the four-gene signature are in the 20-gene signature. One gene in the 5-gene signature is also in the 20-gene signature. Detailed description of the Disclosure

I. Definitions

[0038] As used herein "dysregulation of the mevalonate pathway" refers to deficient activation of the feedback loop downstream of cellular sterol changes such as those caused by statin treatment. Deficient upregulation of HMGCR and/or deficient upregulation of HMGCS1 in response to statin treatment are markers for deficient activation of this feedback loop. For example, cells with deficient activation of the feedback loop that are exposed to a statin typically upregulate HMGCR and/or HMGCS1 less than 2 fold.

[0039] As used herein "regulation of the mevalonate pathway" refers to activation of the classic feedback loop downstream of cellular sterol changes such as those caused by statin treatment, for example upregulation of HMGCR and/or HMGCS1 , in response to statin treatment.

[0040] As used herein "a treatment that depletes mevalonate" means any agent, including any chemical, polypeptide or nucleic acid molecule that when contacted with a cell either directly or indirectly results in depletion of the mevalonate levels in the cell, for example by at least 50%, 60%, 70% or more compared to a similar cell not contacted with the treatment, and includes without limitation, HMGCR inhibitors such as statins, and nucleic acid agents such as siRNA, shRNA or antisense molecules that deplete enzymes of the mevalonate pathway, for example that target and deplete HMGCR and/or HMGCS1. ^3,30

[0041] As used herein "HMGCR inhibitor' means any agent, including any chemical, polypeptide, or nucleic acid molecule, that decreases the level and/or activity (e.g. enzymatic activity) of HMGCR, for example by directly inhibiting HMGCR enzyme activity or indirectly inhibiting HMGCR gene expression. For example, included are an HMGCR neutralizing antibody, HMGCR specific RNAi agents or antisense molecules, and statins.

[0042] As used herein "HMGCR inhibitor sensitivity" or "sensitive to HMGCR inhibitor treatment" in terms of a cancer cell, means a cancer cell that is sensitive to the anti-proliferative effects of an HMGCR inhibitor and undergoes for example growth arrest and/or cell death, such as apoptotic cell death, when contacted with an HMGCR inhibitor either directly or indirectly. For example, in a population of cancer cells, contact with an HMGCR inhibitor causes cells to arrest or die. The greater the number of cells that arrest or die, the greater the HMGCR inhibitor sensitivity of the population. Similarly, HMGCR inhibitor insensitivity in terms of a cancer cell, means a cancer cell that is insensitive to the anti- proliferative effects of a HMGCR inhibitor and does undergo for example growth arrest and/or cell death when contacted with the HMGCR inhibitor. Sensitivity can be quantified as the fraction of cells that undergo for example growth arrest and/or cell death when contacted by the HMGCR inhibitor. For example, when looking at a population of cells, the population of cells is HMGCR inhibitor sensitive if contact with an HMGCR inhibitor induces anti-proliferative effects such as cell death in at least 10%, at least 20%, at least 30%, at least 40% or at least 50% of the cells.

[0043] The term "statins" as used herein refers to the general class of compounds that are known inhibitors of HMG-CoA reductase. In an embodiment the statin will have, within its structure, a moiety that mimics the reaction intermediate formed during the HMG-CoA reductase catalyzed reaction. In a further embodiment this moiety is a group of the formula la or lb:

la lb

[0044] further embodiment, the statin is in the form of a neutral compound or as pharmaceutically acceptable salt. In another embodiment, the statin, or salt thereof, is in the form of a solvate or prodrug thereof. In a further embodiment, the statin may be a mixture of two or more statins, or pharmaceutically acceptable salts, solvates or prodrugs thereof. In another embodiment, the statin is selected from lovastatin, simvastatin, atorvastatin, fluvastatin, rosuvastatin, pravastatin, cerivastatin or pitavastatin, or a pharmaceutically acceptable salt, solvate or prodrugs thereof, or a mixture thereof. In yet another embodiment, the statin is lovastatin, atorvastatin, fluvastatin, or pitavastatin or a pharmaceutically acceptable salt, solvate or prodrugs thereof, or a mixture thereof. [0045] As used herein "statin sensitivity" or "sensitive to statin treatment" in terms of a cancer cell, means a cancer cell that is sensitive to the anti-proliferative effects of statins and undergoes, for example, growth arrest and/or death, such as apoptotic cell death, when contacted with a statin. For example, in a population of cancer cells, contact with a statin causes cells to arrest or die. The greater the number of cells that arrest or die, the greater the statin sensitivity of the population. Similarly, "statin insensitivity", "statin resistance" or "insensitive to statin treatment" in terms of a cancer cell, means a cancer cell that does not undergo growth arrest and/or death, when contacted with a statin. For example, in a population of cancer cells, a majority of cells contacted with a statin survive and/or proliferate. The greater the number of cells that survive and/or proliferate, the greater the statin insensitivity or resistance of the population. For example, when looking at a population of cells, the population of cells is statin sensitive if contact with a statin induces anti-proliferative effects such as cell death in at least 10%, at least 20%, at least 30%, at least 40% or at least 50% of the cells.

[0046] As used herein "anti-proliferative effects", in terms of an agent such as an HMGCR inhibitor or a statin, when contacted with a cell, means the effects of the agent relating to inhibition of cell proliferation, including for example, any of growth arrest, cell death, necrosis, apoptosis, autophagy, senescence, mitotic catastrophe, etc.

[0047] The term "expression level" of a gene as used herein refers to the measurable quantity of gene product produced by the gene in a sample of a patient or a cell, such as a cancer cell, wherein the gene product can be a transcriptional product (e.g. mRNA and/or corresponding cDNA) or a translated transcriptional product (e.g. polypeptide). Accordingly, the expression level can pertain to a nucleic acid gene product such as RNA or cDNA or a polypeptide gene product. The expression level is derived from a patient sample comprising a cancer cell and/or a control sample, and can for example be detected de novo or for the control can correspond to a previous determination. The expression level can be determined or measured for example, using microarray methods, PCR methods, and/or antibody based methods, as is known to a person of skill in the art. RNA can also be directly quantitated using for example direct RNA sequencing or can be quantitated from cDNA pools. Where for example, the expression level is modulated due to gene mutation or gene amplification, for example amplification of the HMGCR gene, the presence of the mutated or amplified gene, for example by fluorescence in situ hybridization, quantitative realtime PCR, comparative genomic hybridization or chromosomal microarray analysis, full or partial genome sequencing, etc can be detected.

[0048] The term "polypeptide activity" as used herein refers to the enzymatic activity of a polypeptide, wherein the polypeptide is ari enzyme and/or DNA binding activity for example where the polypeptide is a transciption factor.

[0049] The term "gene copy number" as used herein refers to the number of copies of a gene or gene segment in a genome. For example, humans which are diploid typically have two copies of most autosomal genes. Amplification and/or deletion events can alter the copy number of a gene.

[0050] As used herein a "profile" means an expression profile, an activity profile or a gene copy number profile. [0051] As used herein "an expression profile" refers to, for a plurality of genes, gene expression levels that are associated with treatment for example statin sensitivity or statin insensitivity. For example, an expression profile can comprise the quantitated relative expression levels of at least 2 or more genes listed in Figure 4, Tables 3, 4, 5 and/or 6 in a cancer cell, wherein the pattern of gene expression levels indicates if the cancer cell is likely statin sensitive or insensitive based on similarity to one or more reference profiles known to be associated with statin sensitivity or insensitivity. An expression profile can for example be detected by measuring RNA expression using methods such as microarray analysis, RT-PCR, multiplex PCR, directly quantitating RNA levels using for example RNA sequencing and/or by measuring polypeptide expression using methods such as flow cytometry and Western blotting and/or by measuring polypeptide activity using methods such as enzyme activity assays or DNA binding affinity assays.

[0052] The term "activity profile" refers to, for a plurality of genes, polypeptide activity levels that are associated with treatment for example statin sensitivity or statin insensitivity. [0053] The term "gene copy number profile" as used herein refers to, for a plurality of genes, the gene copy numbers that are associated with treatment for example statin sensitivity or statin insensitivity.

[0054] A "reference profile" as used herein refers to the expression activity or gene copy number signature of a plurality of genes, of a cancer cell or cancer sample known to be associated with sensitivity or insensitivity to a treatment that depletes mevalonate, for example statin sensitivity or insensitivity. The reference profile is for example determined using cancer cell lines determined to be sensitive or insensitive to, for example, statin treatment. The reference profile is similar between reference cancer cells and/or cancer patients with a similar treatment sensitivity. The reference profile is for example, a reference profile or reference signature of the expression activity or gene copy number of one or more genes listed in Figure 4 and/or Tables 3, 4, 5 and/or 6, to which the levels and gene copy numbers of the corresponding genes in a patient sample are compared in methods for determining treatment for example statin sensitivity.

[0055] The term "hematological cancer" as used herein refers to cancer of blood or bone marrow cells.

[0056] The term "leukemia" as used herein means any disease involving the progressive proliferation of abnormal leukocytes found in hemopoietic tissues, other organs and usually in the blood in increased numbers. For example, leukemia includes, amongst others, acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL) and chronic myelogenous leukemia (CML) including cytogenetically normal and abnormal subtypes.

[0057] The term "lymphoma" as used herein means any disease involving the progressive proliferation of abnormal lymphoid cells. For example, lymphoma includes mantle cell lymphoma, Non-Hodgkin's lymphoma, and Hodgkin's lymphoma. Non-Hodgkin's lymphoma would include indolent and aggressive Non- Hodgkin's lymphoma. Aggressive Non-Hodgkin's lymphoma would include intermediate and high grade lymphoma. Indolent Non-Hodgkin's lymphoma would include low grade lymphomas. [0058] The term "myeloma" and/or "multiple myeloma" as used herein means any tumor or cancer composed of cells derived from the hemopoietic tissues of the bone marrow. Multiple myeloma is also known as MM and/or plasma cell myeloma.

[0059] As used herein "sample" includes but is not limited to a fluid, cell or tissue sample that comprises a cancer cell such as multiple myeloma cell, which can be assayed for gene expression levels. The sample includes for example a blood sample, a fractionated blood sample, a bone marrow sample, a biopsy, a frozen tissue sample, a fresh tissue specimen, a cell sample, and/or a paraffin embedded section, material from which, for example, RNA or DNA can be extracted in sufficient quantities and with adequate quality to permit measurement of relative RNA levels and/or permit detection of gene mutations and/or gene copy number, or material from which polypeptides can be extracted in sufficient quantities and with adequate quality to permit measurement of relative polypeptide levels. In an embodiment, the sample comprises serum. The sample can include cancer cells and can include for example the stromal cells adjacent to the cancer cell.

[0060] The term "cancer cell" as used herein includes, for example, a primary cancer cell as well as a metastatic cancer cell. A cancer as referred to herein means one or more cancer cells.

[0061] The term "subject" also referred to as "patient" as used herein refers to any member of the animal kingdom, preferably a human being.

[0062] The term "control" as used herein refers to a cell, cell sample and/or a numerical value or range corresponding to a gene expression or polypeptide activity level or gene copy number in a cell or cell sample, wherein the cell or cell sample is known to have a particular sensitivity or insensitivity to a treatment that depletes mevalonate, such as a statin treatment, and/or is known to have a regulated or dysregulated mevalonate pathway. Where the control is a numerical value or range, the numerical value or range is a predetermined value or range that corresponds to a level of gene expression, polypeptide activity or copy number or range of levels of the genes in the cancer cells known to be sensitive or insensitive. [0063] The term "negative control" as used herein refers to a cell or sample wherein the cell or sample is known to be insensitive to a treatment that depletes mevalonate, such as statin treatment, and/or is known to have a regulated mevalonate pathway. For example the negative control can be used to determine an expression level, or polypeptide activity of one or more of the genes described herein as useful for identifying sensitivity to a treatment that depletes the mevalonate pathway, for example the genes listed in Figure 4, and/or Tables 4-6. When referring to the level of a gene in a negative control, the negative control level can also refer to a numerical value corresponding to a negative control cell or sample. For example, a negative control can include the expression level, or activity level or corresponding numerical value of HMGCR levels in insensitive cells. Statin insensitive cells are demonstrated herein to have increased basal and statin induced HMGCR levels compared to a statin sensitive cell. In methods involving determining expression or activity levels after contact with for example a statin, the negative control is for example an untreated cell, such as an untreated cancer cell. The negative control can also be a housekeeping gene that is not upregulated for example, to statin exposure, or an external sample spiked in to aid in relative or absolute quantitation. The negative control can for example be a ratio of the level one or more genes to a housekeeping gene or other control. As a further example, the negative control can be the absolute quantity of a gene expression level or polypeptide activity in a cell known to be insensitive to a treatment that depletes mevalonate or has a regulated mevalonate pathway.

[0064] The term "positive control" as used herein refers to control wherein the cell or sample is known to be sensitive to a treatment that depletes mevalonate, such as statin treatment and/or is known to have a dysregulated mevalonate pathway. For example the positive control can be used to determine an expression level, polypeptide activity or gene copy number of one or more of the genes described herein as useful for identifying sensitivity to a treatment that depletes the mevalonate pathway, for example the genes listed in Figure 4, and/or Tables 3-6. When referring to the level of a gene in a positive control, the positive control level can also refer to a numerical value corresponding to positive control cell or sample. For example, a positive control can include the expression level, or activity level or corresponding numerical value of HMGCR levels in treatment sensitive cells. Statin sensitive cells are demonstrated herein to have decreased basal and statin induced HMGCR levels compared to a statin insensitive cells. In methods involving determining expression or activity levels after contact with for example a statin, the positive control is for example a statin treated cell, such as a statin treated cancer cell. The level or copy number of the positive control can for example be compared to a housekeeping gene that is not upregulated for example in response to statin exposure, or an external sample spiked in to aid in relative or absolute quantitation. The negative control be a ratio of the level one or more genes to a housekeeping gene. Where gene copy number is assessed, the negative control is the gene copy number in a cell known to be insensitive to a treatment that depletes mevalonate or has a regulated mevalonate pathway. As a further example, the positive control can be the absolute quantity of a gene expression level or polypeptide activity in a cell known to be sensitive to a treatment that depletes mevalonate or has a dysregulated mevalonate pathway.

[0065] The term "analyte specific reagent" or "ASR" as used herein refers to a reagent that specifically binds or otherwise detects (e.g. quantifies) the analyte of interest (e.g. the gene expression product) in the sample and can be for example an isolated polypeptide, nucleic acid, antibody and/or chemical compound.

[0066] The term "hybridize" as used herein refers to the sequence-specific non-covalent binding interaction with a complementary nucleic acid. Appropriate stringency conditions which promote hybridization are known to those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1 6.3.6. For example, 6.0 x sodium chloride/sodium citrate (SSC) at about 45°C, followed by a wash of 2.0 x SSC at 50°C may be employed. With respect to an array, appropriate stringency conditions can be found and have been described for commercial microarrays, such as those manufactured and/or distributed by Agilent Inc, Affymetrix Inc, Roche-Nimblegen Inc. and other entities.

[0067] The term "microarray" as used herein refers to an ordered or unordered set of probes fixed to a solid surface that permits analysis such as gene expression analysis of a plurality of genes. A DNA microarray refers to an ordered set of DNA fragments fixed to the solid surface. For example, the microarray can be a gene chip and/or a bead array. Methods of detecting gene expression and determining gene expression levels using arrays are well known in the art. Such methods are optionally automated.

[0068] The term "isolated nucleic acid sequence" as used herein refers to a nucleic acid substantially free of cellular material or culture medium when produced by recombinant DNA techniques, or chemical precursors, or other chemicals when chemically synthesized. The term "nucleic acid" is intended to include DNA and RNA and can be either double stranded or single stranded.

[0069] The term "probe" as used herein refers to a nucleic acid molecule that comprises a sequence of nucleotides that will hybridize specifically to a target nucleic acid sequence e.g. a coding sequence of a gene listed in Figure 4, Tables 4, 5, 6 and/or 7. For example the probe comprises at least 10 or more bases or nucleotides that are complementary and hybridize to contiguous bases and/or nucleotides in the target nucleic acid sequence. The length of probe depends on the hybridization conditions and the sequences of the probe and nucleic acid target sequence and can for example be 10-20, 21-70, 71-100, 101-500 or more bases or nucleotides in length. For example, the probe can comprise a nucleic acid sequence comprised in a probe set identified by probe set ID in Tables 5, 6 or 7. The probes can optionally be fixed to a solid support such as an array chip or a microarray chip.

[0070] The term "probe set" as used herein refers to a set of probes that hybridize with the RNA of a specific gene and identified for example by a probe set ID number, such as the probe set numbers listed in Table 4, 5 and/or 6. Each probe set comprises one or more probes, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12 or more probes, optionally specific for the expression nucleic acid of 1 or more than 1 gene.

[0071] The term "primer" as used herein refers to a nucleic acid molecule, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand is induced (e.g. in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH). The primer must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon factors, including temperature, sequences of the primer and the methods used. A primer typically contains 15-25 or more nucleotides, although it can contain less. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art.

[0072] The term "sequence identity" as used herein refers to the percentage of sequence identity between two polypeptide sequences or two nucleic acid sequences. To determine the percent identity of two amino acid sequences or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in the sequence of a first amino acid or nucleic acid sequence for optimal alignment with a second amino acid or nucleic acid sequence). The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position. The percent identity between the two sequences is a function of the number of identical positions shared by the sequences (i.e., % identity=number of identical overlapping positions/total number of positions.times.100%). In one embodiment, the two sequences are the same length. The determination of percent identity between two sequences can also be accomplished using a mathematical algorithm. A preferred, non-limiting example of a mathematical algorithm utilized for the comparison of two sequences is the algorithm of Karlin and Altschul, 1990, Proc. Natl. Acad. Sci. U.S.A. 87:2264-2268, modified as in Karlin and Altschul, 1993, Proc. Natl. Acad. Sci. U.S.A. 90;5873- 5877. Such an algorithm is incorporated into the NBLAST and XBLAST programs of Altschul et al., 1990, J. Mol. Biol. 215:403. BLAST nucleotide searches can be performed with the NBLAST nucleotide program parameters set, e.g., for expectation-score=10, wordlength=7 to obtain nucleotide sequences homologous to a nucleic acid molecules of the present application. BLAST protein searches can be performed with the XBLAST program parameters set, e.g., to expectation- scored 0, wordlength=2 to obtain amino acid sequences homologous to a protein molecule of the present invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., 1997, Nucleic Acids Res. 25:3389-3402. Alternatively, PSI-BLAST can be used to perform an iterated search which detects distant relationships between molecules (Id.). When utilizing BLAST, Gapped BLAST, and PSI-Blast programs, the default parameters of the respective programs (e.g., of XBLAST and NBLAST) can be used (see, e.g., the NCBI website). The percent identity between two sequences can be determined using techniques similar to those described above, with or without allowing gaps. In calculating percent identity, typically only exact matches are counted.

[0073] The term "antibody" as used herein is intended to include monoclonal antibodies, polyclonal antibodies, and chimeric antibodies. The antibody may be from recombinant sources and/or produced in transgenic or non- transgenic animals. The term "antibody fragment" as used herein is intended to include Fab, Fab', F(ab')2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, and multimers thereof and bispecific antibody fragments. Antibodies can be fragmented using conventional techniques. For example, F(ab')2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab')2 fragment can be treated to reduce disulfide bridges to produce Fab' fragments. Papain digestion can lead to the formation of Fab fragments. Fab, Fab' and F(ab')₂, scFv, dsFv, ds- scFv, dimers, minibodies, diabodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques.

[0074] To produce polyclonal antibodies, animals can be injected once or repeatedly with an antigen representing a peptide fragment of the protein product corresponding to the nucleotide sequence of interest, alone or in conjunction with other proteins, potentially in combination with adjuvants designed to increase the immune response of the animal to this antigen or antigens in general. Polyclonal antibodies can then be harvested after variable lengths of time from the animal and subsequently utilized with or without additional purification. Such techniques are well known in the art.

[0075] To produce human monoclonal antibodies, antibody producing cells (lymphocytes) can be harvested from a human having cancer and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells. Such techniques are well known in the art, (e.g. the hybridoma technique originally developed by Kohler and Milstein (Nature 256:495-497 (1975)) as well as other techniques such as the human B- cell hybridoma technique (Kozbor et al., Immunol. Today 4:72 (1983)), the EBV- hybridoma technique to produce human monoclonal antibodies (Cole et al., Methods Enzymol, 121 :140-67 (1986)), and screening of combinatorial antibody libraries (Huse et al., Science 246:1275 (1989)). Hybridoma cells can be screened immunochemically for production of antibodies specifically reactive with cancer cells and the monoclonal antibodies can be isolated. [0076] Specific antibodies, or antibody fragments, reactive against particular target polypeptide gene product antigens (e.g. Table 2, 4 or 6 polypeptide), can also be generated by screening expression libraries encoding immunoglobulin genes, or portions thereof, expressed in bacteria with cell surface components. For example, complete Fab fragments, VH regions and FV regions can be expressed in bacteria using phage expression libraries (See for example Ward et al., Nature 341 :544-546 (1989); Huse et al., Science 246:1275-1281 (1989); and McCafferty et al., Nature 348:552-554 (1990)).

[0077] As used herein, and as well understood in the art, "treatment" is an approach for obtaining beneficial or desired results, including clinical resultsand includes medical procedures and applications including for example chemotherapy, pharmaceutical interventions, surgery, radiotherapy and naturopathic interventions as well as test treatment. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of extent of disease, stabilized (i.e. not worsening) state of disease, preventing spread of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. "Treatment" can also mean prolonging survival as compared to expected survival if not receiving treatment. [0078] As used herein, the phrase "effective amount" or "therapeutically effective amount" means an amount effective, at dosages and for periods of time necessary to achieve the desired result. For example in the context or treating a cancer such as a hematological cancer, an effective amount is an amount that for example induces remission, reduces tumor burden, and/or prevents tumor spread or growth compared to the response obtained without administration of the compound.

[0079] As used herein "a user interface device" or "user interfaced" refers to a hardware component or system of components that allows an individual to interact with a computer e.g. input data, or other electronic information system, and includes without limitation application programmer's interfaces, command line interfaces and graphical user interfaces.

[0080] In understanding the scope of the present disclosure, the term "comprising" and its derivatives, as used herein, are intended to be open ended terms that specify the presence of the stated features, elements, components, groups, integers, and/or steps, but do not exclude the presence of other unstated features, elements, components, groups, integers and/or steps. The foregoing also applies to words having similar meanings such as the terms, "including", "having" and their derivatives. Finally, terms of degree such as "substantially", "about" and "approximately" as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies.

II. Methods

[0081] Statin inhibitors, used to control hypercholesterolemia, trigger apoptosis of some tumor cells. Evaluations of statins in acute myelogenous leukemia and multiple myeloma (MM) have shown statin efficacy is mixed, with only a subset of tumor cells being highly responsive.

[0082] In a previous study, it was determined that MM cell lines show a dichotomous response to lovastatin.²¹ Of 17 MM cell lines assayed, approximately 50% were relatively sensitive to lovastatin-induced apoptosis while the rest were relatively insensitive. This provided an ideal model system for identifying the molecular determinants of statin sensitivity. [0083] It is demonstrated herein that dysregulation of the mevalonate pathway is a key determinant of sensitivity to statin-induced apoptosis in multiple myeloma. In sensitive cells, the classic feedback response to statin exposure is lost. This results in deficient up-regulation of two isoforms of hydroxymethylglutaryl coenzyme A reductase (HMGCR), the rate limiting enzyme of the mevalonate pathway, and hydroxymethylglutaryl coenzyme A synthase 1 (HMGCS1). To ascertain the clinical utility of these findings it is demonstrated that a subset of primary myeloma cells is sensitive to statins and that monitoring dysregulation of the mevalonate pathway can distinguish these cancers. Several gene signatures are also provided that differentiate statin sensitive and statin insensitive cancers. It is also demonstrated that statins are highly effective and well tolerated in an orthotopic model of myeloma using cells harboring this dysregulation. This determinant of sensitivity further provides molecular rationale for the significant therapeutic index of statins on these tumor cells. [0084] Accordingly, an aspect of the disclosure includes a method of determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to a treatment that depletes levels of mevalonate comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to the treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to the treatment.

[0085] HMGCR is the rate limiting enzyme in the production of mevalonate in a cell. Accordingly, in an embodiment, the treatment that depletes levels of mevalonate is a HMGCR inhibitor therapy.

[0086] A further aspect includes a method of determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to a HMGCR inhibitor treatment comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to the treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to the treatment. [0087] Statins are known HMGCR inhibitors. Accordingly in an embodiment the HMGCR inhibitor is a statin.

[0088] In another aspect, the disclosure includes a method of determining whether a cancer cell, for example, from a subject is likely to be sensitive to statin treatment comprising: determining whether the cancer cell has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell is likely sensitive to statin treatment and regulation of the mevalonate pathway is indicative that the cancer cell is likely insensitive to statin treatment.

[0089] In an embodiment, the method includes a step prior to determining whether a cancer cell and/or cancer has a dysregulated mevalonate pathway, of obtaining a sample comprising a cancer cell from the subject.

[0090] In an embodiment, the step of determining whether the cancer cell or cancer has a dysregulated mevalonate pathway comprises determining a level of one or more genes selected from the genes listed in Figure 4 in a sample from the subject; and comparing the level to a control, wherein an altered level of gene expression polypeptide activity or gene copy number in the sample of at least one of the one or more genes compared to the control is indicative of whether the cancer cell or cancer has a dysregulated mevalonate pathway.

[0091] In another embodiment, the step of determining whether the cancer cell has a dysregulated mevalonate pathway comprises determining a level of gene expression of one or more genes selected from the genes listed in Figure 4 in the cancer cell(s); and comparing the level to a control, wherein an altered level of gene expression in the cancer cell of at least one of the one or more genes compared to the control is indicative of whether the cancer cell is likely sensitive or insensitive to treatment that depletes mevalonate, for example statin treatment.

[0092] In an aspect the disclosure includes a method of determining whether a cancer cell from a subject is likely to be sensitive to a treatment that depletes levels of mevalonate comprising: determining a level of one or more genes selected from the genes listed in Figure 4, in a sample from the subject; and comparing each level to a control, wherein an altered level of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer is likely to be sensitive to a treatment that depletes levels of mevalonate.

[0093] In an embodiment, the one or more genes are selected from HMGCS1 , SCAP, SREBF1 and MVD.

[0094] Cancer cells known to be sensitive or insensitive to statin treatment, which is a treatment that depletes mevalonate, have different expression profiles as demonstrated herein (Tables 4-6). By analyzing these expression profiles, genes whose expression levels, and by implication, activity levels, vary with statistical significance are herein identified (Tables 4-6). As the cancer cells profiled that are sensitive to statin treatment are demonstrated to have a dysregulated mevalonate pathway, the expression and activity level and/or copy number of each of these genes can be predictive of a regulated or dysregulated mevalonate pathway, for example determined by comparison to a control, including for example a negative control or a positive control.

[0095] For example if the sample is compared to a negative control (e.g. characterized by insensitivity to a treatment that depletes mevalonate and/or a regulated mevalonate pathway) and the level and/or copy number is comparable to the negative control, the cancer cell or cancer is likely insensitive to the treatment. If for example, the level or copy number in the sample is compared to a positive control (e.g. characterized by sensitivity to a treatment that depletes mevalonate and/or a dysregulated mevalonate pathway) and the level or copy number is comparable, the cancer cell or cancer is likely sensitive to the treatment.

[0096] In an embodiment, the step of determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway comprises determining a level of one or more genes selected from the genes listed in Tables 4-6, in a sample from the subject; and comparing each level to a control, wherein an altered level of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer has a dysregulated mevalonate pathway.

[0097] In an aspect the disclosure includes a method of determining whether a cancer cell from a subject is likely to be sensitive to a treatment that depletes levels of mevalonate comprising: determining a level of one or more genes selected from genes listed in Tables 4-6, in a sample from the subject; and comparing each level to a control, wherein an altered level of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer is likely to be sensitive to a treatment that depletes levels of mevalonate.

[0098] Accordingly in an embodiment, the altered level is an increased level. In another embodiment, the altered level is a decreased level .

[0099] In an embodiment, an increased level of SREBF1 , SCAP, FASN, LDLR, MVK and/or IDI1 is indicative the cancer cell and/or cancer is likely to be sensitive to a treatment that depletes levels of mevalonate. In an embodiment, a decreased level of HMGCS1 , MVD, GGPS1 , ACACA, PMVK, PGGT1 B, FDFT1 , HMGCR, RABGGTB and/or RABGGTA is indicative the cancer cell and/or cancer is likely to be sensitive to a treatment that depletes levels of mevalonate.

[00100] In an embodiment, the control is a negative control. In another embodiment, the control is a positive control. The control is in another embodiment, a numerical value corresponding to the expression level of the gene in the control sample or cell. In another embodiment, the level in the cancer cell and the control is further compared by normalizing to a housekeeping gene or corresponding level, that is for example not modulated in response to statin treatment. In another embodiment, the control incorporates an external sample spiked into the primary one to aid in absolute quantitation. In another embodiment, absolute quantitation is performed directly on the cancer cells with no direct control.

[00101] In an embodiment, the positive control is a level of gene expression of the corresponding gene(s) in a control cell sensitive for example to statin treatment. In another embodiment, the level in the cancer cell and the positive control is further compared by normalizing to a housekeeping gene or corresponding level, that is not modulated in response to for example statin treatment, in each cell. In an embodiment, the level of gene expression is determined by absolute quantitation, for example by direct RNA sequencing or using cDNA pools or a technique that directly counts all or a random or representative sample of RNA molecules in a sample. The absolute quantity is compared to a numerical value that corresponds to a control level. By comparing the absolute quantity of the gene expression products to the control value or for example a cut-off value, the sensitivity of the sample to a treatment that depletes mevalonate can be determined.

[00102] In another embodiment, the disclosure includes a method of determining whether a cancer cell from a subject is likely to be sensitive or insensitive to a treatment that depletes mevalonate, for example a statin treatment comprising: determining a level of gene expression or polypeptide activity of one or more genes selected from the genes included in Figure 4 and/or Tables 4-6 in the cancer cell; and comparing the level or copy number to a level of gene expression or polypeptide activity, or copy number of corresponding gene(s) in a control cell sensitive and/or insensitive to statin treatment, wherein a decreased level of gene expression, polypeptide activity or gene copy number in the cancer cell of at least one of the one or more genes compared to the control cell insensitive to statin treatment is indicative the cancer cell is likely sensitive to statin treatment and/or wherein an increased level of gene expression, polypeptide activity and/or gene copy number in the cancer cell of at least one of the one or more genes compared to the control cell sensitive to statin treatment is indicative the cancer cell is likely insensitive to statin treatment. For example, for genes shown in Figure 4 and/or Tables 4-6 whose expression and predicted activity level is increased in treatment insensitive cells relative to the level in cells known to be statin sensitive, increased levels of said genes in a sample from a subject is indicative that the subject's cancer is likely insensitive to a treatment that depletes mevalonate such as statin treatment. Similarly, for genes shown in Figure 4 and/or Tables 4-6 whose expression and predicted activity level is decreased in treatment insensitive cells relative to the level in cells known to be statin sensitive, decreased levels of said genes in a sample from a subject is indicative that the subject's cancer is likely insensitive to a treatment that depletes mevalonate such as statin treatment. Conversely, for genes shown in Figure 4 and/or Tables 4-6 whose expression and predicted activity level, is increased in treatment sensitive cells relative to the level in cells known to be statin insensitive, detecting increased levels of said genes in a sample from a subject is indicative that the subject's cancer is likely sensitive to a treatment that depletes mevalonate such as statin treatment. Similarly, for genes shown in Figure 4 and/or Tables 4-6 whose expression and predicted activity level, is decreased in treatment sensitive cells relative to the level in cells known to be statin insensitive, detecting decreased levels of said genes in a sample from a subject is indicative that the subject's cancer is likely sensitive to a treatment that depletes mevalonate such as statin treatment.

[00103] A further embodiment includes a method of determining whether a cancer cell or cancer is sensitive or insensitive to statin treatment comprising: determining a profile such as an expression profile by measuring the gene expression or activity levels of a plurality of genes selected from genes included in Figure 4 and/or Tables 4-6 in the cancer cell and/or a sample from a subject comprising or derived from the cancer cell; and classifying the cancer cell as likely sensitive or likely insensitive to statin treatment based on the profile for example expression profile.

[00104] In an embodiment, the method of determining whether a cancer cell or cancer is likely to be sensitive to statin treatment comprises determining a profile such as an expression profile by measuring the gene expression or activity levels of a plurality of genes selected from genes listed in Figure 4 and/or Tables 4-6 in the cancer cell and/or a sample from a subject comprising or derived from the cancer cell; comparing the expression profile to a reference profile, for example a reference profile of a cell sensitive to statin treatment, and/or a reference profile of a cell insensitive to statin treatment, and classifying the cancer cell as likely sensitive or likely insensitive to statin treatment based on similarity of the expression profile to the reference profile.

[00105] Another aspect of the disclosure includes a computer implemented method for determining whether a cancer cell or cancer, for example a hematological cancer cell or hematological cancer, is sensitive or insensitive to a treatment that depletes mevalonate for example statin treatment comprising comparing, on a computer, a profile such as an expression profile or an activity profile of a cancer cell for example comprised in a sample of a subject, the profile comprising measurements of expression or activity levels of a plurality of genes, to one or more reference profiles comprising measurements of expression or activity levels of the plurality of genes associated with for example statin sensitivity or for example statin insensitivity, the plurality of genes selected from genes included in Figure 4 and/or Tables 4-6; and classifying, on a computer, the cancer cell as sensitive to the treatment for example statin treatment or insensitive to the treatment for example statin treatment according to the similarity of the profile, for example an expression profile to one of the reference profiles.

[00106] In an embodiment, the one or more genes and/or the plurality of genes comprises HMGCS1. In another embodiment, the gene is HMGCS1. In another embodiment, the one or more genes and/or the plurality of genes comprises HMGCR. In another embodiment, the gene is HMGCR. HMGCR has at least two isoforms. In an embodiment, the isoform detected is the full length (HMGCR-13). In another embodiment, the isoform detected is the exon 13 alternately spliced transcript (HMGCR-13). Exon 13 codes for a small region of the catalytic domain of the enzyme, including several residues important for binding both substrates and statins (Fig. 1A). The effect splicing would have on enzymatic function and regulation have not yet been directly addressed, however, expression of HMGCR-D13 has been associated with a decreased cholesterol- lowering response in lymphocytes exposed to simvastatin, suggesting it is refractory to inhibition by statins ³¹.

[00107] In an embodiment, the method comprises determining HMGCR level or HMGCS1 level in a sample from the subject; and comparing the HMGCR level to a control, wherein a decreased level of HMGCR and/or HMGCS1 is indicative the cancer cell is sensitive to the treatment. In an embodiment, the level determined comprises enzymatic activity.

[00108] In an embodiment, the HMGCR or HMGCS1 level determined is a statin induced HMGCR or HMGCS1 level.

[00109] In an embodiment, the method additionally comprises contacting the cell and/or cancer and control with an agent that modulates mevanolate metabolism, for example a statin, prior to determining the level of the one or more genes. Differences in induced expression or activity levels between insensitive and sensitive cells may for example, further exacerbate the detectable differences.

[00110] It is also demonstrated herein that statin-sensitive and statin- insensitive cancer cells exhibit differential expression of genes listed in Table 3 in response to statin exposure including, for example, hydroxymethylglutaryl coenzyme A reductase (HMGCR), hydroxymethylglutaryl coenzyme A synthase 1 (HMGCS1), mevalonate diphosphate decarboxylase (MVD), farnesyl pyrophosphate synthase (FDPS), acetoacetyl-CoA thiolase 2 (ACAT2), and mevalonate kinase (MVK).

[00111] Accordingly, in an embodiment, determining whether the cancer cell has a dysregulated mevalonate pathway and/or statin-sensitivity comprises determining a level for example of gene expression activity or gene copy number of one or more genes selected from genes listed in Table 3, for example Hmgcr, hmgcsl , mvd, fdps, acat2 and mvk in a cancer cell contacted with a statin; comparing the level of gene expression of the one or more genes to a negative control, wherein an altered increased level of gene expression, activity or copy number in the cancer cell of one of the one or more genes compared to the negative control is indicative of whether the cancer cell is likely insensitive or sensitive to statin treatment and wherein a lack of increased level of gene expression compared to the negative control is indicative the cancer cell is sensitive to statin treatment. Without wishing to be bound by theory, the genes listed in Table 3 are predictive of whether a cancer cell has a dysregulated mevalonate pathway and/or statin-sensitivity or insensitivity. The transcript levels for the genes listed in Table 3 were identified to be differentially regulated upon statin exposure in at least two cell lines (two sensitive MM cell lines and two insensitive MM cell lines were tested). Further, the regulation of various transcript levels in response to statins is different in sensitive tumor cells compared to insensitive tumour cells as shown in Table 3. One example of how the regulation of transcript levels differs in statin-sensitive versus statin-insensitive cells is in transcripts coding for components of the mevalonate pathway. In particular, one of the transcripts identified in Table 3 as differentially regulated by statins, was HMGCR, the rate-limiting enzyme in the mevalonate pathway and the molecular target of the statin family of inhibitors. Statin-insensitive cells upregulated the expression of HMGCR in response to lovastatin exposure while sensitive cells did not.ln an embodiment, the negative control is a housekeeping gene that is not upregulated to statin exposure. For example, for genes which do not increase upon statin exposure, comparing the level of gene expression to the level of gene expression of a housekeeping gene in the cancer cell, indicates whether the cancer cell is sensitive (e.g. no increase or little increase relative to housekeeping gene, for genes that do not increase upon statin exposure in statin sensitive cells but do increase in statin insensitive cells) or insensitive (e.g. increased, for example greater than 2 fold for genes that do increase in statin insensitive cells but do not increase in statin sensitive cells) after exposure to for example 8 hours of statin treatment. In yet another embodiment, the method further comprises calculating a ratio of the one or more genes to a housekeeping gene and comparing the ratio to a negative control (e.g. a statin insensitive cell, untreated cancer cell etc).

[00112] In another embodiment, the step of determining whether the cancer cell has a dysregulated mevalonate pathway comprises determining a level of gene expression activity or copy number of one or more genes selected from genes listed in Table 3 for example HMGCR, HMGCS1 , MVD, FDPS, ACAT2 and MVK in a cancer cell contacted with a statin; comparing the level of gene expression of the one or more genes to a positive control, wherein a level of gene expression in the cancer cell of one of the one or more genes that is comparable or increased compared to the positive control is indicative the cancer cell is likely insensitive to statin treatment and wherein a decreased level of gene expression compared to the positive control is indicative the cancer cell is likely sensitive to statin treatment.

[00113] In an embodiment, the positive control is further compared by normalizing to a housekeeping gene or corresponding level, that is not modulated in response to statin treatment, in each cell. [00114] In another embodiment, the step of determining whether the cancer cell has a dysregulated mevalonate pathway comprises determining the level of gene expression, or activity of one or more genes selected from Table 3, 4, 5 and/or 6 in the cancer cell; comparing the level of the one or more genes to a negative control. [00115] In an embodiment, the disclosure includes a method for determining whether a cancer is likely to be sensitive to statin treatment comprising: determining the level of gene expression in a sample of a subject of one or more genes selected from Table 3, 4, 5 and/or 6 in a sample from the subject; comparing the level of gene expression of the one or more genes to a positive and/or negative control.

[00116] In an embodiment, the disclosure includes a method for determining whether a hematological cancer cell from a subject is likely to be sensitive to statin treatment comprising: determining the level of gene expression, activity or gene copy number of one or more genes selected from the genes listed in Figure 4 and/or Tables 3-6, for example HMGCR, HMGCS1 , in a sample from the subject; comparing the level of gene expression or activity or gene copy number of the one or more genes to a negative and/or positive control.

[00117] In an embodiment, the level or copy number determined is the level or copy number of HMGCR, alternatively full length HMGCR (FL) or spliced HMGCR (D13).

[00118] As mentioned, it is demonstrated herein that the expression levels of genes listed in Tables 4, 5 and/or 6 can also be used to identify statin sensitivity.

[00119] Accordingly, a further aspect of the disclosure includes a method of determining whether a cancer cell, for example, from a subject is likely to be sensitive to a treatment that depletes mevalonate comprising determining a level of gene expression, activity or copy number of one or more genes, selected from the genes in Tables 4, 5, and/or 6, in the cancer cell; and comparing the level to a control, wherein an altered, for example increased or decreased level of gene expression activity or copy number in the cancer cell of at least one of the one or more genes compared to the control is indicative of whether the cancer cell is likely insensitive or sensitive to statin treatment for example, wherein a lack of increase or decrease in the level of gene expression in the cancer cell of at least one of the one or more genes compared to the control, wherein the control comprises a cell or numerical value corresponding to a treatment insensitive cell, is indicative the cancer cell is likely sensitive to the treatment.

[00120] In an embodiment, the treatment is an HMGCR inhibitor therapy. In another embodiment, the treatment is a statin treatment.

[00121] In an embodiment, the disclosure provides a method of determining whether a cancer cell is sensitive to a treatment that depletes mevalonate such as a statin comprising determining a profile by measuring the gene expression levels, activity levels or gene copy number of a plurality of genes selected from genes listed in Tables 4, 5 and/or 6; and classifying the cancer cell as likely sensitive or likely insensitive to the treatment for example statin treatment based on the profile.

[00122] In a further embodiment, the method of determining whether a cancer cell is likely to be sensitive to a treatment that depletes mevalonate, for example a HMGCR inhibitor such as statin treatment comprises determining an expression profile by measuring the gene expression levels, activity levels or gene copy number of a plurality of genes selected from genes listed in Tables 3, 4, 5 and/or 6 providing a profile; comparing the profile to a reference profile, for example a reference profile of a cell sensitive to the treatment (e.g. statin treatment), and/or a reference profile of a cell insensitive to the treatment (e.g. statin treatment), and classifying the cancer cell as likely sensitive or likely insensitive to the treatment (e.g. statin treatment) based on similarity of the profile to the reference profile.

[00123] The methods described herein can be computer implemented. In an embodiment, the method further comprises: displaying or outputting to a user interface device, a computer readable storage medium, or a local or remote computer system, the classification produced by the classifying step (e.g. whether the cancer cell is likely to be sensitive or insensitive to statin treatment).

[00124] Another aspect of the disclosure provides a computer implemented method, comprising comparing, on a computer, a profile such as an expression profile of a sample of a subject, the profile comprising measurements of expression or activity levels of gene copy number of a plurality of genes, to one or more reference profiles comprising measurements of expression or activity levels or gene copy number of the plurality of genes associated with statin treatment sensitivity or statin treatment insensitivity, the plurality of genes selected from genes listed in Tables 3, 4, 5 and/or 6; and classifying, on the computer, the cancer cell as sensitive to a treatment that depletes mevalonate levels for example statin treatment or insensitive to such a treatment (e.g. statin treatment) according to the similarity of the profile, for example expression profile to one of the reference profiles. [00125] In an embodiment, the cancer cell is comprised in a sample of a subject with cancer. In another embodiment, the cancer cell is derived from a sample of a subject with cancer.

[00126] In an embodiment, the one or more genes is 1 gene. In an embodiment, the one or more genes or the plurality of genes is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes.

[00127] In an embodiment, the one or more genes or the plurality of genes comprises one or more genes selected from Tables 3, 4, 5 and/or 6. In another embodiment, the one or more genes or the plurality of genes comprises the genes listed in Table 4. In another embodiment, the one or more genes or the plurality of genes comprises the genes listed in Table 5. In yet a further embodiment, the one or more genes or the plurality of genes comprises the genes listed in Table 6. In an embodiment, the one or more genes or the plurality of genes are the genes listed in Table 4. In an embodiment, the one or more genes or the plurality of genes are the genes listed in Table 5. In an embodiment, the one or more genes or the plurality of genes are the genes listed in Table 6.

[00128] In an embodiment, the increased or decreased level of gene expression is at least 2 fold, at least 3 fold, at least 4 fold, or at least 5 fold. In another embodiment, the increased or decreased level of gene expression is about 2 to about 15 fold, about 3 to about 15 fold, about 4 to about 15 fold or about 5 to about 15 fold. In a further embodiment, the increase or decrease in the level of gene expression is about 2 to about 10 fold, about 3 to about 10 fold, about 4 to about 0 fold or about 5 to about 10 fold.

[00129] Other tumour types that have been reported to display evidence of dysregulation of the MVA pathway include breast, prostate, colon, lung, liver, brain, AML, CML, and lymphoma. Accordingly, in an embodiment, the cancer is breast, prostate, colon, lung, liver, or brain cancer or AML, CML, or lymphoma. In another embodiment, the cancer cell is a breast, prostate, colon, lung, liver, brain, AML, CML, and lymphoma cell. In an embodiment, the cancer is a hematological cancer. In another embodiment, the cancer cell is a hematological cancer cell.

[00130] In an embodiment, the hematological cancer cell is a multiple myeloma cell and/or the cancer is multiple myeloma. [00131] The level of gene expression can be determined by assaying nucleic acid expression products, for example mRNA or cDNA and/or by assaying polypeptide products. The level of gene expression can be determined or measured using an analyte specific reagent (ASR), wherein the analyte is a gene expression product of a gene described herein. In, an embodiment, the ASR is an antibody, receptor protein, nucleic acid such as a probe or primer set, capable of amplifying the analyte.

[00132] The level of polypeptide activity can be determined by enzyme assay, for example by assaying the activity of H GCR by high performance liquid chromatography.³²

[00133] In an embodiment, the methods comprise determining nucleic acid levels.

[00134] Wherein the gene expression level being determined is a nucleic acid, the gene expression levels can be determined using a number of methods for example a microarray chip or PCR, optionally multiplex PCR, northern blotting, or other methods and techniques designed to produce quantitative or relative data for the levels of mRNA species corresponding to specified nucleotide sequences present in a sample. These methods are known in the art. Accordingly, in an embodiment, the gene expression level is determined using a microarray chip and/or PCR, optionally multiplex PCR.

[00135] Gene copy number can be determined for example by fluorescence in situ hybridization, quantitative real-time PCR, comparative genomic hybridization or chromosomal microarray analysis, etc.

[00136] In an embodiment, the expression level is determined by one or more probes and/or one or more probe sets. In another embodiment, the one or more probes and/or the one or more probe sets, for example the probes comprised on Affymetrix U133 Plus 2.0 microarrays for the genes described herein, including non-exclusively for example probes identified by number listed in Tables 3, 4, 5 and 6. A person skilled in the art would be familiar with the procedures for accessing the sequence identified by the provided probe number. In another embodiment, the expression is determined using one or more primers sets for example, primers listed in Table 2 [00137] In another embodiment, the methods comprise determining polypeptide levels.

[00138] A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a polypeptide product of a gene described herein, including immunoassays such as flow cytometry, Western blots, ELISA, and immunoprecipitation followed by SDS-PAGE, as well as immunocytochemistry or immunohistochemistry. For example, flow cytometry or other methods for detecting polypeptides, can be used for detecting surface protein expression levels.

[00139] Further, for example a person skilled in the art would be familiar with the necessary normalizations and other computational, bioinformatic, or statistical analyses necessary for each technique.

[00140] In yet another embodiment, a method described herein also comprises first obtaining a sample from the subject. The sample, in an embodiment, comprises a cancer cell, for example a blood sample or a bone marrow sample. In another embodiment, the sample comprises serum. In an embodiment, the sample comprises fresh tissue, frozen tissue sample, a cell sample, or a formalin-fixed paraffin-embedded sample. In an embodiment, the sample is submerged in a RNA preservation solution, for example to allow for storage. In an embodiment, the sample is submerged in Trizol®. In an embodiment, the sample is stored as soon as possible at ultralow (for example, below -190°C) temperatures. Storage conditions are designed to maximally retain mRNA integrity and preserve the original relative abundance of mRNA species, as determined by those skilled in the art. The sample in an embodiment is optionally processed, for example, to obtain an isolated RNA fraction and/or an isolated polypeptide fraction. The sample is in an embodiment, treated with a RNAse inhibitor to prevent RNA degradation.

[00141] In another embodiment, the sample is a fractionated blood sample or a fractionated bone marrow sample. In an embodiment, the sample is fractionated to increase the percentage of cancer cells, for example CD138+ cells [00142] In certain embodiments, the sample comprises cancer cells which are optionally isolated and optionally treated with a statin, for example lovastatin or atorvastatin.

[00143] The control expression levels and/or reference profiles can be pre- generated, for example the control expression levels or reference profiles can be values corresponding to cell levels which are for example comprised in a database. They can also be generated de novo.

[00144] Similarity between a gene expression profile and a reference profile can be determined for example using an algorithm. A number of algorithms can be used to assess similarity. For example, a Naive Bayes probabilistic model is trained on data. In order to stratify whether a new patient has a sensitive or insensitive cancer the Naive Bayes classifier combines this probabilistic model with a decision rule: assign the sample to the class of sensitive cancer or insensitive cancer that is most probable; this is known as the maximum a posteriori or MAP decision rule. Another example would be the Random Forests supervised machine-learning technique. A Random Forest is a collection of one or more decision trees, each of which is developed on a subset selected with or without replacement of the data and/or variables. In order to determine whether a new patient has a sensitive or insensitive cancer, the Random Forest collection of decision trees can be polled to determine the number of votes for sensitivity and insensitivity or evaluated in other ways to provide a prediction of sensitivity or insensitivity. Another example would be the use of unsupervised machine- learning, such as hierarchical clustering, k-means clustering, fuzzy c-means clustering, self-organizing maps. In these algorithms the proximity of a patient profile to that of all other samples is calculated using a distance metric such as Euclidean distance or a similarity metric such as Pearson's, Spearman's, or Kendall's correlation. A patient profile is predicted to have the sensitivity status similar to its neighbours, as assessed by voting or probabilistic inference or other techniques.

[00145] The methods described herein can be computer implemented. In an embodiment, the method further comprises: displaying or outputting to a user interface device, a computer readable storage medium, or a local or remote computer system; the classification produced by the classifying step.

[00146] Another aspect of the disclosure includes a method of treating a subject with cancer or reducing tumor burden in the subject comprising: identifying a subject with a cancer sensitive to a treatment that depletes mevalonate for example a HMGCR inhibitor or a statin treatment according to a method described herein; and administering the treatment or a statin or a composition comprising a statin to the subject.

[00147] Another embodiment includes a method of reducing tumor burden in the subject comprising: administering a treatment that depletes mevalonate for example a statin or a composition comprising a statin to the subject or another drug that alters MVA metabolism; and monitoring whether the tumor burden is reduced in the subject.

[00148] In an embodiment, the method comprises: administering to a subject in need thereof for treatment of a cancer an effective amount of a treatment that depletes mevalonate such as statin, indicated by the expression level of one or more genes selected from genes listed in Figure 4 and/or Tables 4, 5 and/or 6 in a sample from the subject compared to a control.

[00149] A further aspect is use of a treatment that depletes mevalonate for example a statin, for treating a statin sensitive cancer, wherein the statin sensitivity of the cancer is determined according to a method described herein.

[00150] In an embodiment, the treatment that depletes mevalonate or composition comprising a statin administered to a subject is a statin that comprises a moiety of formula la or formula lb. In an embodiment, the statin is selected from a statin in the form of a neutral compound or as pharmaceutically acceptable salt, in the form of a solvate or prodrug thereof, a mixture of two or more statins, or pharmaceutically acceptable salts, solvates or prodrugs thereof.

[00151] In another embodiment, the statin is selected from lovastatin, simvastatin, atorvastatin, fluvastatin, rosuvastatin, pravastatin, cerivastatin or pitavastatin, or a pharmaceutically acceptable salt, solvate or prodrugs thereof, or a mixture thereof. III. Compositions, Arrays and Kits

[00152] Another aspect of the disclosure includes a composition comprising two or more analyte specific reagents (ASR).

[00153] In an embodiment, the ASR comprises and/or is a nucleic acid molecule. In an embodiment, the ASRs are a set of at least two probes or at least two primers for determining the expression (e.g. mRNA levels) of one or more genes listed for example in Figure 4, and/or in Tables 3, 4, 5 and/or 6. [In an embodiment, the composition comprises at least 2 nucleic acid molecules, wherein each nucleic acid molecule comprises a primer listed in Table 2. In another embodiment, each of the nucleic acid molecules comprise a probe sequence selected from Tables 3, 4, 5 and/or 6.

[00154] In another embodiment, the ASR(s) comprises and/or is an antibody. In an embodiment, the composition comprises at least two antibodies for determining the expression (e.g. polypeptide levels) of one or more genes listed for example in Figure 4, and/or in Tables 3, 4, 5 and/or 6.

[00155] Another aspect of the disclosure includes an array comprising for each gene in a plurality of genes, the plurality of genes comprising at least 2 of the genes listed in Figure 4, Table 3, 4, 5 and/or 6, one or more nucleic acid probes complementary and hybridizable to a coding sequence in the gene.

[00156] In an embodiment, the plurality of genes comprises, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes. In a further embodiment the plurality of genes comprises, 4, 5, or 20 genes.

[00157] The array can be a microarray designed for evaluation of the relative levels of mRNA species in a sample.

[00158] Another aspect of the disclosure includes a kit for determining statin sensitivity of a cancer cell and/or for treating a statin sensitive cancer. In an embodiment, the kit comprises a composition described herein and/or an array described herein, and optionally one or more specimen collectors, and/or RNA preservation solution. In another embodiment, the kit comprises one or more statins for treating a statin sensitive cancer. [00159] In an embodiment, the specimen collector comprises a sterile vial or tube suitable for receiving a biopsy or other sample. In an embodiment, the specimen collector comprises RNA preservation solution. In another embodiment, RNA preservation solution is added subsequent to the reception of sample. In another embodiment, the sample is frozen at ultralow (for example, below 190°C) temperatures as soon as possible after collection.

[00160] In an embodiment the RNA preservation solution comprises one or more inhibitors of RNAse. In another embodiment, the RNA preservation solution comprises Trizol® or other reagents designed to improve stability of RNA.

[00161] In an embodiment, the kit comprises two or more ASRs. In an embodiment, the ASRs are nucleic acid molecules specific for one or more genes described herein useful for detecting cancer sensitivity in a cancer cell. In another embodiment, the ASRs are polypeptide molecules specific for one or more genes described herein useful for detecting cancer sensitivity in a cancer cell.

[00162] In an embodiment, the antibody or probe is labeled. The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio-opaque or a radioisotope, such as ³H, ¹⁴C, 32p 35_S 123| 125| 131 , . _a f|_Uorescent (fluorophore) or chemiluminescent

(chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.

[00163] In another embodiment, the detectable signal is detectable indirectly. A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a polypeptide product of a gene described herein, including immunoassays such as flow cytometry, Western blots, ELISA, and immunoprecipitation followed by SDS-PAGE, as well as immunocytochemistry or immunohistochemistry. For example, flow cytometry or other methods for detecting polypeptides, can be used for detecting surface protein expression levels.

[00164] In an embodiment, the kit further comprises instructions for determining statin sensitivity. [00165] In an embodiment, the composition, array and/or kit is used for a method described herein.

[00166] The following non-limiting examples are illustrative of the present disclosure:

Examples

Example 1

MATERIALS AND METHODS

Cell culture and compounds

[00167] All cell lines were assayed as asynchronously growing cells as described previously.²¹ Lovastatin powder was a gift of Apotex Corp. (Mississauga, ON, Canada) and was activated and dissolved in ethanol as described previously.²⁵ Atorvastatin calcium (21 CEC Pharmaceuticals Ltd., East Sussex, UK) was dissolved in ethanol. 3-4,5-dimethylthiazolyl-2,2,5-diphenyl tetrazolium bromide (MTT) was purchased from Sigma (Mississauga, ON, Canada).

Array Data Pre-Processing

[00168] For each cell line analyzed by microarray (KMS , H929, LP1 , and SKMM1) three lovastatin-treated biological replicates and three vehicle (ethanol)- treated replicates were hybridized separately to Affymetrix U133 Plus 2.0 microarrays. The data was loaded into the R statistical environment (v2.7.2) using the affy package (v1.18.2) of the BioConductor open-source library.³³ All arrays showed minimal distributional or spatial heterogeneity. Array data was pre- processed using the gcrma algorithm, as implemented in the gcrma package (v2.12.1) of BioConductor.³⁴ A custom, alternative CDF (hgu 33plus2hsentrezgcdf, v11.0.0) was used to ensure an updated and one-to- one mapping of ProbeSets to Entrez Gene IDs.³⁵ Raw and pre-processed data have been deposited into the GEO database (accession GSE15946).

Statistical Analysis

[00169] To identify genes significantly affected by lovastatin treatment we modeled the signal intensity of each ProbeSet as a linear sum of cell line effects and cell line-lovastatin interactions. A contrast matrix was then used to identify the effects of lovastatin on each individual cell line. This model was fit in the R statistical environment (v2.7.2) using the limma package (v2.14.7). Coefficients and p-values were extracted, then subjected to an empirical Bayes moderation of standard error³⁶ and a false-discovery rate adjustment for multiple-testing.

Downstream Analysis

[00170] Parameter sensitivity was assessed using p-value sensitivity analyses, where the number of differentially expressed genes was plotted as a function of the p-value threshold. Analysis was performed in the R statistical environment (v2.7.2) and visualization employed the lattice (vO.17-15) and latticeExtra (vO.5-3) packages using unsupervised machine-learning with multiple variance, signal-intensity, and F-statistic thresholds³⁷. Genes were filtered by each threshold and their signal intensities subjected to divisive hierarchical clustering using the DIANA algorithm, as implemented in the cluster package (v1.11.11) for the R statistical environment (v2.7.2). Pathway-specific clustering was performed by extracting genes with the gene ontology annotation GO:0016125 from the AmiGo database (database version 2009/01/29) and mapping them to Entrez Gene IDs by gene symbol. Pre-processed data was extracted for these genes, subjected to row- and column-jittering and divisive hierarchical clustering as described above. Pearson's correlation was used as a distance metric. Differential gene products in each cell line were subjected to GO ontological analysis using the GOMiner tool.³⁸ All human databases and all evidence codes were selected and 1000 permutations were used to estimate the null distributions. Categories with fewer than five genes were omitted from the analysis.

Analysis of cell line panel basal expression

[00171] A publically available dataset was mined in which basal mRNA levels of 46 lvl cell lines had been evaluated using Affymetrix U133A Plus 2.0 arrays.³⁹ Of the 46 cell lines, sensitivity to lovastatin was known for 16 (7 sensitive; 9 insensitive). Pre-processed data were associated with updated annotation using the Affymetrix NetAffx database (version na22). The set of cell lines were trichotomized into statin-sensitive, statin-insensitive, and unknown. Pair-wise comparisons between sensitive and insensitive groups were performed using t- tests with Welch's adjustment for heteroscedasticity as implemented in the R statistical environment (v2.5.1). The resulting p-value vectors were subjected to a false-discovery rate adjustment for multiple testing. When multiple probes were present for a given gene, only median values were visualized.

Real-time PCR

[00172] Approximately 5 x 10⁵ cells were seeded in 6-well tissue culture plates overnight and were either harvested directly or treated with ethanol (solvent control) or lovastatin at the concentrations and times described. RNA was harvested from cells using TRIZOL Reagent (Invitrogen) and cDNA was synthesized from 1 pg of RNA with Superscript II (Invitrogen). Primers (Table 2) to amplify total HMGCR, HMGCR-FL, HMGCR-D13, and GAPDH were used with SYBR Green master mix (Applied Biosystems) and TaqMan® Gene Expression Assays (Applied Biosystems) were used for GAPDH (Hs99999905_m1), HMGCS (Hs00266810_m1), and LDLR (Hs01092525_m1) with Taqman master mix (Applied Biosystems) to measure relative levels of transcript expression. Realtime PCR acquisition and analysis was performed on an ABI Prism 7900 Sequence Detection System (Applied Biosystems). Experiments were conducted in triplicate and expression of all transcripts relative to GAPDH was determined.

Immunoblotting

[00173] Approximately 5 x 10⁵ cells were seeded overnight and treated as indicated. Cells were pelleted, washed in cold PBS, and lysed (20 mM Tris pH 7.5, 150 mM NaCI, 1 mM EDTA, 1 mM EGTA, 1% Triton X-100, 2.5 mM sodium pyrophosphate, 1 mM 2- -glycerolphosphate, 1 mM Na₃V0₄, 1mM phenylmethylsulfonyl fluoride, 0.5 μg/ml antipain, 1 μg/ml leupeptin, 1 g/ml aprotinin, and 1 μg/ml prepstatin A) on ice for 15 minutes. Precipitated cellular debris was pelleted and removed, an aliquot of the supernatant was set aside to measure protein concentration, and DTT was added to the remaining samples to a final concentration of 1M. 6x Laemmli's loading dye was added at room temperature and samples were never boiled in order to limit the aggregation of membrane proteins. Blots were probed with anti-HMGCR (Cat. #07-572; Upstate), anti-tubulin (Santa Cruz), anti-actin (Sigma), or BCL2 (kindly provided by Dr. David Andrews, Hamilton, ON). Expression Vectors

[00174] Catalytic domain HMGCR cDNA was PCR-amplified from pHRed- 102 (ATCC) with primers (Table 2) to insert the catalytic domain downstream of a strong consensus Kozak sequence, and then sub-cloned into the pGEM-T Easy shuttle vector (Promega). The cHMGCR sequence was cut out of pGEM-T Easy with EcoRI and inserted into the EcoRI restriction site in the pBabeMN-ires-GFP retroviral vector, a kindly provided by Dr. Garry Nolan (Stanford University, Stanford, CA, USA). The CHMGCR-D13 construct was made by site-directed mutagenesis (Table 2) to remove nucleotides corresponding to exon 13 from cHMGCR-FL. All cloning was verified by sequencing. All retroviral particles, including pBabeMN-ires-GFP-BCL2, were produced and target cells infected as described previously.²¹ Approximately equal levels of GFP positive cells were obtained after infection with all viral constructs as determined by flow cytometry.

MTT and Fixed PI Assays

[00175] The MTT assays were conducted as previously described²⁵ except 5000 cells/well of a 96-well plate were plated and after 24 hours, cells were exposed to lovastatin (5 to 100 μΜ) for 48 hours. For fixed propidium iodide (PI) assays, 5 x 10⁵ cells were seeded sub-confluently in 6-well tissue culture plates overnight. Cells were treated as indicated, harvested, washed in cold PBS, and fixed in cold 80% ethanol. They were stained with PI and analyzed using a FACScalibur cytometer (Becton Dickinson, San Jose, CA, USA) to determine the proportion of cells in different phases of the cell cycle. Cell death was assessed by measurement of sub-diploid DNA content (% pre-G1).

Primary Patient Samples

[00176] Mononuclear cells freshly isolated from bone marrow aspirates were separated by Ficoll-Hypaque gradient sedimentation and plated at a cell density of 5 x 10⁵ cells/mL in IMDM supplemented with 20% FCS, 1% glutamine, and penicillin-streptomycin. Cells were cultured in the presence of vehicle control or 20 μΜ lovastatin or 20 μΜ atorvastatin. After 16 hours, a portion of the sample was sorted for the CD138 positive MM population using an EasySep CD138 kit (StemCell Technologies, Vancouver, Canada) and RNA was harvested for cDNA synthesis and real-time PCR as described above. The remainder was exposed to statin or vehicle control for a total of 48 hours prior to being labeled with anti- CD138-PE (Immunotech, Marseille, France) and FITC-conjugated annexin V (R & D Systems, Minneapolis, MN) for apoptosis analysis. Samples were analyzed by flow cytometry on a FACSCaliber flow cytometer and CellQuest software (BD Biosciences). Viable myeloma cells are defined as CD138 positive/annexin V negative. Apoptotic myeloma cells are fall within the CD138 negative/annexin V positive population. Bone marrow aspirates were obtained by consent under a protocol approved by the University Health Network Research Ethics Board (Toronto, Canada).

Orthotopic MM model

[00177] Atorvastatin was evaluated in a previously described orthotopic model of MM.⁴⁰ Whole-body irradiated (2.5 Gy) 7-week old female non-obese diabetic severe combined immunodeficient (NOD/SCID) mice (Ontario Cancer Institute) were inoculated intravenously via the tail vein with 8 x 10⁶ KMS11 cells stably expressing luciferase (KMS11-luc). Animals were housed in sterile filter-top cages with 12-hour light/dark cycles and fed sterile rodent chow and water containing neomycin (Sigma; 1mg/ml_). For early-stage disease treatment, dosing was initiated two days after KMS11-luc injection. Atorvastatin, suspended in PBS, was administered 3 times a week for 37 days by oral gavage at 10 mg/kg and 50 mg/kg. Control mice received PBS alone. Tumors were imaged on designated days by whole-body imaging using the MS imaging system (Xenogen Corporation, Alameda, CA). Briefly, mice were injected intraperitoneally with luciferin (150 mg/kg, Caliper Life Sciences) followed by anesthetization with isoflurane. Twelve minutes post-luciferin injection gray-scale images followed by bioluminescent maps of the mice were obtained using a charge coupled device camera. Signal intensity was quantified using Living Image Version 2.50.2 (Xenogen) by summing detected photon counts from dorsal and ventral images. P-values were calculated using 1-way ANOVA followed by a Tukey's post test (P < 0.05 was considered significant). Survival curves (Kaplan-Meier survival analysis) were determined based upon the time at which mice were humanely euthanized after the onset of hind-limb paralysis due to tumor burden. [00178] RESULTS

[00179] To better understand the determinants of sensitivity to statin-induced apoptosis, MM was exploited as a model system comprised of both sensitive and insensitive cell lines (detailed in ² and summarized in Figure 8). Microarray analysis was conducted to compare the mRNA levels of two sensitive (KMS11 and H929) and two insensitive (LP1 and SKMM1) MM cell lines. Cells were grown in the presence of 20 μΜ lovastatin or vehicle control for 16 hours, a time point that precedes the first indication of apoptosis in these cells and that is therefore useful for identifying mechanisms of action independent of general apoptosis-related changes.²¹ An unsupervised and unbiased clustering analysis indicated that global expression patterns of the sensitive cells were more similar to one another, in contrast to the insensitive cells (Figure 1A). Interestingly, one of the transcripts identified as being differentially regulated by statins (Table 3) was HMGCR, the rate-limiting enzyme in the MVA pathway and the molecular target of the statin family of inhibitors. While statin-insensitive cells upregulated the expression of HMGCR in response to lovastatin exposure, sensitive cells did not. The lack of a classic feedback response in sensitive MM cells led us to hypothesize that these sensitive cells may harbor a dysregulated MVA pathway.

[00180] To determine whether the entire MVA pathway was dysregulated specific molecular pathways were assessed to determine if they were differentially regulated in sensitive and insensitive MM cells (Table 1). Using GO pathway analysis, no pathways were found to be enriched for differentially regulated genes after lovastatin exposure in both sensitive cell lines. In contrast, 22 GO pathways were identified as significantly enriched in both insensitive MM cell lines. Notably, the GO terms for cholesterol, sterol, steroid, and isoprenoid metabolic and biosynthetic processes were all enriched, supporting the notion that a dysregulated MVA pathway exists exclusively in sensitive MM cells. Selecting 6 key MVA pathway genes (HMGCR, hydroxymethylglutaryl coenzyme A synthase 1 (HMGCS1), mevalonate diphosphate decarboxylase (MVD), farnesyl pyrophosphate synthase (FDPS), acetoacetyl-CoA thiolase 2 (ACAT2), and mevalonate kinase (MVK)) it was found that all were upregulated in response to lovastatin exposure from 3- to 10-fold in both statin-insensitive lines, but not substantially in the statin-sensitive lines (Figure 1 B). These findings were validated by real-time PCR for HMGCR and HMGCS1 (Figure 1C and 1 D) in cells that were exposed to 20 μΜ lovastatin for 16 hours. In agreement with the array results, it was observed that the insensitive MM cell lines were able to more robustly and significantly upregulate the expression of HMGCR and HMGCS1 , by approximately 5-fold, in response to lovastatin exposure. The differential was verified when HMGCR expression was assessed at the protein level in that insensitive MM cell lines upregulated HMGCR protein more substantially than sensitive MM cells (Figure 1 E).

[00181] Because the primary clinical function of statins is to reduce cellular cholesterol levels, it is possible that the observed differential in statin-sensitivity is a corresponding differential in cholesterol content of the cells. The enrichment for altered expression of cholesterol biosynthetic genes in our array analysis would seem to support this theory. To address this possibility, intracellular cholesterol content of representative statin-sensitive and -insensitive MM cells was measured. Remarkably, no striking differences were observed in the levels of either free cholesterol or cholesteryl esters (Figure 9), suggesting other factors are responsible for mediating differential gene expression and statin-sensitivity.

[00182] A novel array technology developed to discover alternatively spliced transcripts recently identified a novel human splice variant of HMGCR.⁴¹ The splicing event leads to the loss of catalytic domain residues encoded by exon 13, but little work has been done to characterize the role and regulation of this alternatively spliced HMGCR (HMGCR-D13). To analyze the expression of each isoform in sensitive and insensitive MM cells, exon junction-spanning real-time PCR primers were used (Figure 2A). Expression of the unspliced, full-length HMGCR (HMGCR-FL; Figure 2B) was first assessed in sensitive KMS11 and insensitive LP1 cells exposed to either a range of concentrations of lovastatin for 16 hours (left) or to 20 μΜ lovastatin for various lengths of time (middle). Only the statin-insensitive LP1 cells upregulated HMGCR expression. This was confirmed in a broader panel of sensitive and insensitive cells exposed to 20 μΜ lovastatin for 16 hours, where the insensitive cells were better able to upregulate HMGCR- FL expression in response to lovastatin (right). Interestingly, the same pattern was observed for HMGCR-D13 (Figure 2C). One apparent exception to the pattern was the OCIMY5 cell line which, while insensitive to lovastatin-induced apoptosis, did not upregulate expression of HMGCR transcripts in response to the drug. This cell line was able to upregulate expression at the protein level (Figure 1 E) and so appears to have developed a different mechanism to achieve the same result. These data suggest that sensitive MM cells are unable to respond to statin exposure and it is potentially this difference that ultimately results in the cells undergoing apoptosis.

[00183] It was next determined whether modulating expression of either HMGCR-FL or HMGCR-D13 could affect sensitivity to lovastatin-induced apoptosis. To date, studies of human HMGCR have been greatly limited by the lack of a successful overexpression strategy. As the attempts to express the full- length protein have been similarly unsuccessful, it was endeavored to overcome this challenge by expressing the catalytic region in the absence of its transmembrane domain which is known to harbor negative regulatory elements.⁴² Therefore catalytic domains of the two splice variants (cHMGCR-FL and cHMGCR-D13; Figure 3A) were expressed in representative sensitive and insensitive MM cell lines. Using real-time PCR to assess the degree of overexpression achieved (Figure 3B), substantial increases in both HMGCR-FL (left) and HMGCR-D13 (middle) expression were observed. The sum of both FL and D13 endogenous HMGCR (right) did not change significantly upon introduction of either ectopic construct. Ectopic expression was confirmed by immunoblotting (Figure 3C) and, in all cases, protein expression of the cHMGCR- FL construct was higher than cHMGCR-D13. Cells were then exposed to increasing concentrations of lovastatin and the antiproliferative effect was measured by MTT assay (Figure 3D). Interestingly, sensitive KMS11 cells expressing cHMGCR-FL had been rendered less sensitive than the parental cells and the MTT₅₀, the concentration required to reduce viability of the population by 50%, doubled from approximately 5 μΜ to 10 μΜ. No change in sensitivity was observed in KMS11 cells expressing cHMGCR-D13. Additionally, no changes in statin response were evident in insensitive LP1 cells expressing the ectopic cHMGCR constructs.

[00184] As MTT assays measure the activity of a mitochondrial enzyme, it is not clear whether a decrease in metabolism of the MTT substrate is indicative of growth arrest, apoptosis, or senescence. As previous work has indicated that sensitive MM cells undergo apoptosis in response to lovastatin²¹ a fixed PI (Figure 3E) analysis was performed as an assay for cell death after lovastatin exposure. In agreement with the MTT results, KMS11 cells expressing cHMGCR-FL, but not CHMGCR-D13, were less sensitive to statin-induced killing (left). This increase in resistance to lovastatin was specific as no added protection was conferred when cells were exposed to melphalan (middle) or bortezomib (right), two agents presently used in the management of MM. BCL2 was included as a positive control able to inhibit apoptosis induced by all agents.

[00185] While it could be possible to exploit an expression regulatory defect as a biomarker of statin sensitivity, a difference that can be observed at the basal level would be more clinically tractable. As the array analysis had indicated dysregulation of the MVA pathway at large (Figure 1 and Table 1) the study was extended to include other sterol-regulated gene products. To this end a publically available dataset was mined in which basal mRNA expression of a large panel of MM cell lines had been assayed using Affymetrix microarrays.³⁹ Many of the cell lines included in this panel had been previously characterized for their sensitivity to statin-induced apoptosis so it was sought to determine whether a subset of sterol-responsive genes were expressed differentially in sensitive cells compared to insensitive cells The log2-fold differences between these subsets of MM cells were then evaluated (Figure 4A) as well as the statistical significance of these differences (Figure 4B). Interestingly, one of the most significant differentially expressed gene products was HMGCS1 , the enzyme immediately upstream of HMGCR in the MVA pathway. It is also important to note that while the previous array experiment (Figure 1) was not sufficiently powered to detect significant differences in expression at the basal level, reproducibly higher basal HMGCR and HMGCS1 expression was detected by real-time PCR (Figure 1C, 1 D, 2B, and 2C). To validate this basal difference, mRNA from representative sensitive and insensitive MM cells was harvested and the basal expression of HMGCS1 determined (Figure 4C). LDLR was assayed as a negative control that did not appear to be differentially expressed in sensitive and insensitive MM cells (Figure 4A, B, and D). Indeed, while HMGCS1 was found to be expressed more highly in insensitive MM cells, LDLR expression was considerably more variable. [00186] To resolve how the regulation of HMGCS1 expression compared to that of HMGCR, representative sensitive and insensitive MM cells were exposed to a range of lovastatin concentrations for 16 hours (Figure 5A; left) and to 20 μΜ lovastatin for various lengths of time (middle). While expression of HMGCS1 in KMS1 1 cells remained steady throughout all conditions, it was upregulated substantially in both a dose- and time-dependent manner in insensitive MM cells. This expression pattern was consistent when additional cell lines were assayed (right), resulting in a pattern of expression very similar to that seen with both HMGCR variants. Consistently, the expression of LDLR was not consistently upregulated in either sensitive or insensitive MM cell lines (Figure 5B).

[00187] To establish whether these differences in expression could be extended to predicting which patients might benefit from using statins as anticancer agents, a series of primary MM cell experiments was conducted. Over the span of several months, a sufficient number of myeloma cells were obtained from the bone marrow of 5 patients. These primary cells were exposed to 20 μΜ lovastatin, 20 μΜ atorvastatin, or a vehicle control. Atorvastatin was included in these experiments because it is expected to have better performance in vivo due to both a higher plasma half life and higher physiologically achievable concentrations. Following incubation for 16 hours, patient material was sorted for the CD138+ (MM cell) population and harvested for mRNA expression analysis. The remainder was incubated with drug for a total of 48 hours prior to being labeled for both CD138 and annexin V, to assess the percent of viable myeloma cells by flow cytometry (Figure 6A). Two samples were identified as modestly sensitive with 8-40% reductions in the CD138 positive population and a reciprocal increase in annexin V positive cells compared to vehicle control. The other three samples were insensitive to statin-induced apoptosis as MM cell viability remained relatively unchanged in response to statin exposure. Interestingly, primary patient cells which were insensitive to statin-induced apoptosis also showed higher HMGCR mRNA levels, both basally and as induced by the statins (Figure 6B). While a differential was not as readily observed for HMGCS1 expression, possibly due to the small sample size, these data suggest that expression of MVA pathway genes may identify a subset of patients with statin-sensitive tumors. [00188] Finally, to demonstrate that the efficacy of statins observed in tissue culture with cell lines or primary cells can be recapitulated in vivo, we employed an orthotopic murine model of MM. Sublethally irradiated NOD/SCID mice were intravenously injected with a sensitive cell line, KMS1 1 , ectopically expressing luciferase. When subsequently injected with luciferin, the bioluminescent myeloma cells in these animals can be imaged and quantified. Importantly, the MM cells in this model colonize the bone marrow, a key feature of human disease.⁴⁰ These animals received 10 or 50 mg/kg of atorvastatin or a PBS vehicle control by oral gavage three times a week for 37 days, until the tumor bioluminescence in the control mice saturated the detectors. Impressively, tumor growth in the animals receiving atorvastatin was significantly lower than the control mice (Figure 7A and B) and there were no overt signs of toxicity in the statin- treated mice. The groups receiving 10 or 50 mg/kg atorvastatin were essentially indistinguishable, suggesting statin efficacy was maximized. After treatments ceased, survival of the animals was monitored over time. All mice that received PBS were sacrificed before any of the statin-treated mice showed significant signs of disease (Figure 7C). These results show that anti-cancer statin therapy was both safe and effective in an in vivo model of myeloma using cells that display a dysregulated mevalonate pathway (Figures 1 , 2, and 5), which we conclude is a defining characteristic of statin sensitivity.

Table 1. GO pathway enrichment of gene products differentially regulated by lovastatin exposure as determined by mRNA microarray analysis in two or more cell lines.

Enrichment¹

GO Sensitive Insensitive Term

H929 KMS11 LP1 SKMM1

GO:0006066 1.0 2.8 4.8 6.1 alcohol metabolic process

GO:0009058 1.2 1.2 2.3 2.8 biosynthetic process

GO:0003824 1.1 1.1 1.4 1.4 catalytic activity

GO:0044255 0.8 1.5 3.3 4.9 cellular lipid metabolic process

GO: 0006695 0.4 20.5 25.2 40.8 cholesterol biosynthetic process

GO:0008203 0.7 6.6 11.0 16.6 cholesterol metabolic process

GO:0005737 1.2 1.4 1.3 1.4 Cytoplasm GO:0044444 1.1 1.8 1.5 1.8 cytoplasmic part

GO:0012505 1.0 1.8 2.0 2.6 endomembrane system

GO:0005789 0.9 3.2 endoplasmic reticulum membrane

GO: 0044432 0.9 2.8 2.6 3.8 endoplasmic reticulum part

GO:0008299 0.7 17.1 30.5 43.2 isoprenoid biosynthetic process

GO:0006720 0.4 10.2 18.3 25.9 isoprenoid metabolic process

GO:0008610 1 .0 3.1 5.6 8.3 lipid biosynthetic process

GO:0006629 0.8 1.2 3.0 lipid metabolic process

GO:0042175 0.9 3.1 2.9 4.2 nuclear envelope-ER network

GO:0031090 1 .0 1.7 organelle membrane

GO:0016491 0.7 3.0 2.4 2.9 oxidoreductase activity

GO:0006694 0.4 10.7 12.7 20.6 steroid biosynthetic process

GO:0008202 0.6 4.7 7.3 11.2 steroid metabolic process

GO:0016126 0.3 24.0 25.0 40.5 sterol biosynthetic process

GO:0016125 0.6 9.1 12.3 18.8 sterol metabolic process

^TEnrichment scores c enote a fold-change in t ie number gene products found in each category compared to the number expected by chance alone. Shaded cells represent statistically significant enrichment at a false-discovery rate of 10%. Table 2. Primer sequences for real-time PCR and cloning.

Real-time PCR rimers

[00189] Following microarray quality-control and pre-processing general linear modeling was employed to identify genes whose mRNA abundances were altered in response to lovastatin treatment. Table 3 lists selected genes whose mRNA abundances were altered (p<0.005) in at least two cell-lines, giving their HGNC gene symbol, their Entrez Gene ID, and their full name. For each cell-line, the fold-change induced by lovastatin treatment is given in log₂-space, as is the multiple-testing-adjusted p-value.

[00190] Following quality-assurance, pre-processing, and statistical analysis of the microarray data, it was sought to identify dysregulated pathways or functional groups. Accordingly, gene ontology (GO) enrichment analysis was performed, as described in the Methods. This table lists every GO term analyzed in this study, along with the number of genes found relative to chance (Enrichment) and the false-discovery rate for each of the four cell-lines. GO terms are identified both by their official GO ID and by their full term name.

Table 3. Details characterizing the response of genes differentially expressed in two or more cell lines (p < 0.005).

Log2 (Fold change) Adjusted p-value

Entrez Gene ID Symbol H929 K S11 LP1 SKMM1 H929 KMS11 LP1 S MM1

19 ABCA1 .7 -0.5 .6 -0.6 1.51E-04 5.06E-01 6.75E-04 1.83E-01

39 ACAT2 -0.2 0.4 2 2 2.5 3.79E-01 1.81 E-01 1.21E-07 2.93E-08

47 ACLY -0.4 -0.1 1 1.2 4.30E-02 1.00E+00 2.11E-04 1.27E-04

71 ACTG1 -0.1 -1.6 •0.8 -0.4 7.02E-01 2.72E-06 7.62E-04 1.18E-01

100 ADA -0.4 -0.4 -1.0 -0.2 1.87E-03 2.28E-02 1.91 E-06 3.41 E-01

133 ADM -2.3 -2.5 -1.3 0.2 4.93E-04 2.93E-03 7.10E-02 1.00E+00

271 AMPD2 -0.7 -0.3 -0.1 -0.7 8.59E-04 2.84E-01 9.89E-01 2.89E-03

301 ANXA1 6.5 0.9 2.9 0.6 5.50E-O7 6.37E-01 3.68E-03 8.08E-01

388 RHOB 6.7 4.4 2.6 0.0 8.53E-12 2.40E-08 4.28E-06 1.00E+00

390 RND3 3.2 2.2 2.0 0.0 3.69E-08 2.60E-05 2.78E-05 1.00E+00

467 ATF3 2.5 2.9 1.2 0.6 7.26E-04 2.91 E-03 1.31 E-01 6.05E-01

506 ATP5B -0.3 -0.1 -0.2 -0.3 3.60E- 4 2.81 E-01 7.19E-03 7.47E-04

518 ATP5G3 -0.5 -0.2 -0.6 -0.5 1.64E-03 3.74E-01 7.68E-04 8.12E-03

533 ATP6V0B -0.5 -0.1 -0.2 -0.5 3.60E-05 4.75E-01 7.99E-02 1.96E-04

573 BAG1 -1.2 -0 3 -0.8 -0.4 1.56E-05 3.11 E-01 4.20E-03 1.79E-01

583 BBS2 1.7 04 1.2 0 9.91 E-06 4.63E-01 1.36E-03 1.00E+00

687 2.3 1.4 0.8 2.10E-07 4.08E-04 1.S0E-02 1.00E+00

688 KLF5 0.4 0.1 0.4 0.0 5.24E-04 8.24E-01 2.49E-03 1 OOE+00

744 MPPED2 -1.1 -02 -0.4 0.0 7.76E-09 6.30E-02 1.28E-03 1.00E+00

960 CD44 0.9 0.6 0.8 0.0 4.72E-06 2.18E-03 7.31 E-05 1. OOE+00

987 LRBA 1.4 0.1 0.9 -0.2 1.50E-06 1. OOE+00 4.14E-04 4.70E-01

1030 CDKN2B 0.3 2.2 0.9 0.0 1.99E-01 1.46E-06 2.49E-03 1. OOE+00

1040 CDS1 2.3 1.9 0.0 0.0 1.71E-05 1.20E-03 1.00E+00 1. OOE+00

1138 CHRNA5 -1.0 -0.1 -0.6 -1.0 9.52E-05 1.00E+00 2.76E-02 5.32E-04

1316 KLF6 3.1 2.0 1.9 3.7 1.26E-06 1.16E-03 6.11E-04 6.45E-07

1477 CSTF1 -0.4 -0.2 -0.4 -0.1 1.84E-04 8.09E-02 2.77E-03 5.89E-01

1537 CYC1 -0.7 -0.2 -0.2 -0.7 3.09E-05 4.98E-01 9.84E-02 1.52E-04

1595 CYP51A1 0.1 0.3 1.4 2.0 9.13E-01 3.53E-01 3.72E-06 6.42E-08

1717 DHCR7 -0.6 0.4 1.9 2.6 1.67E-02 2.49E-01 1.68E-06 5.22E-08

1718 DHCR24 -0.3 .6 1.2 .6 3.46E-01 1.81E-04 6.03E-04 5.01 E-05

1741 DLG3 0.8 0 7 0 1.29E-03 2.33E-02 3.90E-04 1. OOE+00

1808 DPYSL2 1.1 0.9 .5 0.7 6.66E-05 347E-03 7.51E-06 1.35E-02

1831 TSC22D3 5.1 2 7 3.2 4.0 7.48E-09 858E-05 5.52E-06 2.98E-07

1955 MEGF9 1.1 0.6 0.0 0.0 6.52E-08 6.30E-04 1.00E+00 1. OOE+00

1967 EIF2B1 -0.4 -0.3 -0.4 -0.3 9.19E-04 8.72E-02 3.20E-03 3.37E-02 2193 FARSA -1.2 -0.5 -0.7 -0.6 1.81E-06 2.41 E-02 1.90E-03 4.74E-03

2222 FDFT1 0.3 0.6 1.2 1.4 6.61 E-02 2.07E-02 7.51E-06 1.94E-06

2224 FDPS -0.1 0.3 1.3 1.7 8.85E-01 2.44E-01 6.97E-06 2.54E-07

2273 FHL1 0.8 0.4 0.3 0.0 8.75E-08 2.94E-03 3.53E-03 1.00E+00

2288 FKBP4 -1.0 -0.6 -0.8 -0.7 963E-05 5.22E-02 2.49E-03 8.83E-03

2591 GALNT3 2.0 2.1 2 3 0.1 1.63E-04 1.46E-03 2.41 E-04 1.00E+00

2643 GCH1 1.9 0.3 1.2 -0.2 3.44E-08 3.95E-01 1.35E-05 6.33E-01

2821 GPI -0.7 -0.2 -0.7 -0.5 5.71 E-05 2.62E-01 3 85E-04 7.25E-03

2936 GSR -0.3 -0.1 -0.5 -0.7 1.64E-02 1.00E+00 4.44E-03 1.99E-04

3070 HELLS -1.4 -0.5 ■0.6 -0.8 4.20E-06 8.18E-02 1 17E-02 2.84E-03

3096 HIVEP1 0.6 0.2 0.0 0.6 6.06E-04 3.87E-01 1.00E+00 2.99E-03

3156 HMGCR 0.2 0.9 2.3 2.5 484E-01 5.06E-04 3.76E-09 4.31 E-09

3157 HMGCS1 -0.4 0.7 3.3 3.4 4.79E-01 2.20E-01 4.90E-07 3.08E-07

3183 HNRNPC -1.3 -0.5 -0.7 -0.4 1.93E-06 4.05E-02 1.42E-03 9.97E-02

3275 PRMT2 1.4 0.4 1.1 0.4 6.26E-05 3.97E-01 2.37E-03 2.42E-01

3417 IDH1 0.6 0.5 1.6 0.8 6.41 E-03 1.23E-01 4 57E-06 3.32E-03

3422 IDI1 -0.3 0.3 1.7 1.7 2.65E-01 5.47E-01 3.58E-06 2.94E-06

3482 IGF2R 0.5 0.6 0.9 0.4 3.77E-03 1 42E-02 1.42E-04 7.97E-02

3638 INSIG1 0.1 0.9 1.9 2.7 1.00E+00 3.36E-03 6.92E-07 1.42E-08

3643 INSR 0.9 0.0 0.8 0.1 6.05E-07 1.00E+00 5.12E-06 9.92E-01

3685 ITGAV 1.2 0.3 0.8 0.0 1 46E-06 2.72E-01 2.30E-O4 1.00E+00

3695 ITGB7 0.2 0.3 0.9 -1.2 7.08E-01 7.97E-01 4.79E-03 7.91 E-04

3725 JUN 5.1 2.8 1.8 0.0 9.29E-14 1.95E-09 1.60E-07 1.00E+00

3727 JUND 1.9 1.9 1.5 1.1 3.05E-07 6.19E-06 1.73E-05 4.13E-04

3831 KLC1 0.4 0.4 0.4 -0.2 5.51E-05 5.06E-04 1.21 E-04 9.32E-02

3845 KRAS 24 1 2 0.9 0.4 1.75E-07 2.82E-03 6.76E-03 2.28E-01

3949 LDLR 1.1 1.1 1.4 1.8 4.78E-04 4.83E-03 1 48E-04 1.46E-05

3958 LGALS3 1.2 0.7 1.1 0.0 8.67E-05 3.59E-02 478E-04 1.00E+00

3995 FADS3 1.8 0.7 1.6 0.4 1.13E-05 7.11 E-02 1.09E-04 3.29E-01

4047 LSS 0.3 0.7 2.9 3.4 4.11E-01 3.75E-02 2.86E-08 9.49E-09

4171 MCM2 -1.2 -0.5 -0.6 -0.3 6.77E-08 3.65E-03 2.36E-04 2.59E-02

4173 MCM4 -1.4 -0.4 -0.9 -0.8 6.31 E-06 1.88E-01 1.70E-03 3.87E-03

4217 MAP3K5 0.8 -0.1 0.6 -0.1 4.90E-05 1.00E+00 3.79E-03 8.36E-01

4218 RAB8A -0.4 -0.2 -0.5 -0.5 4.29E-04 2.70E-01 9.86E-04 1.38E-03

4597 MVD -0.3 0.2 3.2 3.6 6.41 E-01 1.00E+00 2.82E-06 5.78E-07

4598 MVK -0 1 0.1 1.8 1 9 9.44E-01 1.00E+00 2.97E-06 9.09E-07

4602 MYB -1.9 0.1 -0.3 1.1 1.05E-05 1.00E+00 5.35E-01 4.85E-03

4627 MYH9 0.8 0.1 0.9 -0.3 2.73E-04 1.00E+00 7.68E-04 1.72E-01

4642 MY01D 1.8 1.1 0.2 0.0 1.05E-06 1.80E-03 8.79E-01 1.00E+00

4670 HNRNPM -0.7 -0.5 -0.5 -0.4 3.68E-05 1.36E-02 1.60E-03 1.33E-02

4686 NCBP1 -0.5 -0.2 -0.4 -0.7 3.36E-03 3.40E-01 4.60E-O2 8.78E-04

4706 NDUFAB1 -0.5 -0.1 -0.4 -0.4 2.27E-04 6.54E-01 3.05E-O3 2.83E-03

4716 NDUFB10 -0.4 0.0 -0.2 -0.6 9.57E-04 1.00E+00 3.04E-01 1.99E-04

4722 NDUFS3 -0.5 -0.2 -0.4 -0.6 2.79E-04 3.03E-01 3.77E-03 4.19E-04

4731 NDUFV3 -0.5 0.1 -0.1 -0.5 4.47E-04 1.00E+00 5.43E-01 4.74E-03

4758 NEU1 0.1 0.5 1.1 0.8 9.59E-01 3.48E-02 1.54E-05 2.00E-04

4779 NFE2L1 1.5 0.9 1.9 1.0 2.55E-04 7.05E-O2 1.42E-04 1.84E-02

4864 NPC1 0.7 0.4 1.3 1.2 1.29E-03 1.02E-01 2.18E-05 3.97E-05

5106 PCK2 2 4 1.9 2.5 1.3 9.79E-04 3.32E-02 2.55E-03 1.07E-01

5274 SERPINI1 0.7 0.2 1.2 0.6 4 59E-03 1.00E+00 2.12E-04 5.24E-02

5355 PLP2 0.5 0.4 0 5 0.3 7.92E-04 1.63E-02 324E-03 7.08E-02

5412 UBL3 ' 0.6 0.1 0.5 0.6 960E-05 1.00E+00 1.70E-03 388E-04

5433 P0LR2D -1.0 -0.4 -0.6 -0.2 3.55E-06 3.65E-02 2.62E-03 2.46E-01

5441 P0LR2L -0.8 0.0 -0.6 -0.2 2.77E-04 1.00E+00 4.52E-03 3.34E-01

5684 PSMA3 -0.4 -0.1 -0.3 -0.4 6.67E-04 6.92E-01 3.11 E-02 2.00E-03

5694 PSMB6 -0.3 0.1 0.0 -0.4 9.92E-05 8.73E-01 8.10E-01 2.28E-05

5704 PSMC4 -0.5 -0.2 -0.2 -0.6 6.04E-04 4.14E-01 1.58E-01 7.50E-04

5725 PTBP1 -0.8 •0.3 -0.5 -0.1 1.70E-06 6.28E-02 9.87E-04 9.75E-01

5869 RAB5B 0.7 0.4 0.9 0.5 2.95E-04 1.03E-01 4.53E-05 5.90E-03

5898 RALA 0.7 0.5 0.4 0.9 1.00E-03 6.41 E-02 8.01E-02 6.16E-04

5985 RFC5 -0.8 -0.4 -0.6 -0.5 9.54E-06 5.15E-02 1.18E-03 4.04E-03

6118 RPA2 -0.8 -0.2 -0.6 -0.2 1.62E-05 3.49E-01 1.05E-03 4.28E-01

6183 MRPS12 -1.4 -0.6 -0.8 -0.5 9.08E-06 6.42E-02 3.92E-03 8.82E-02

6301 SARS 1.1 0.5 1.4 0.9 3.78E-04 2.79E-01 1.67E-04 7.73E-03

6307 SC4M0L -0.2 0.8 2.0 2.0 4.72E-01 9.88E-04 2.93E-08 3.87E-08

6309 SC5DL 0 2 0.3 1 7 1.8 3.78E-01 3.15E-01 9.78E-07 2.54E-07 6319 SCD -0.5 0.3 1.0 1.6 1.48E-01 8.58E-01 3.60E-03 1.52E-04

6322 SCML1 -0.5 0.1 0.6 -0.1 8.87E-04 6.99E-01 4.21 E-04 1.00E+00

6509 SLC1A4 1.0 0.3 1 4 0.6 1.86E-03 6.78E-01 6.75E-04 7.59E-02

6536 SLC6A9 3.1 0.3 2.6 0.4 8.25E-05 1.00E+00 1.32E-03 8.81 E-01

6573 SLC19A1 -0.8 -0.2 -0.7 -0.1 2.91 E-06 1.75E-01 2.60E-05 7.95E-01

6625 SNRP70 -0.8 -0.2 -0.7 -0.6 8.41 E-04 8.08E-01 4.21 E-03 2.57E-02

6634 SNRPD3 -0.5 -0.1 -0.5 -0.3 6.38E-04 7.96E-01 4.81E-03 3.79E-02

6655 SOS2 1.3 0.8 1.0 0.6 1.18E-04 3.24E-02 4.61 E-03 7.12E-02

6709 SPTAN1 1.3 1.1 0.8 0.0 5.82E-06 4.09E-04 2.29E-03 1.00E+00

6713 SQLE •0.3 0.4 1.9 1.9 4.11 E-01 2.58E-01 4.40E-06 3.22E-06

6721 SREBF2 0.6 0.2 1.1 1.1 1.00E-02 6.75E-01 4.21 E-04 6.15E-04

6793 STK10 1.2 0.6 1.0 -0.1 4.19E-06 1.97E-02 1.14E-04 6.89E-01

6809 STX3 1.3 1.3 1.2 0.3 1.11E-04 1.13E-03 5.57E-04 5.64E-01

6901 TAZ 1.0 0.1 0.6 -0.3 2.70E-06 1.00E+00 8.15E-04 1.47E-01

6990 DYNLT3 0.9 0.5 1.0 0.2 1.00E-04 4.91 E-02 2.72E-04 6.85E-01

6993 DYNLT1 1.4 0.6 0.3 -0.1 5.38E-08 1.90E-03 769E-02 7.57E-01

7027 TFDP1 -0.8 -0.2 -0.4 -0.2 1.39E-07 1.43E-01 9.86E-04 7.54E-02

7073 TIAL1 -0.2 -0.2 0.1 0.0 1.90E-04 3.49E-03 4.12E-01 1.00E+00

70Θ2 TJP1 0.5 1.0 0.7 0.0 9.35E-05 1.46E-06 2.18E-05 1.00E+00

7167 TPI1 -0.5 -0.1 -0.7 -0.5 1.45E-03 1.00E+00 626E-04 7.87E-03

7203 CCT3 -0.4 -0.3 -0.2 -0.4 1.74E-04 6.30E-02 9.08E-02 4.38E-03

7220 TRPC1 1.8 0.0 0.8 0.0 6.68E-08 1.00E+00 5.23E-04 1.00E+00

7251 TSG101 0.6 0.2 0.3 0.2 2.33E-06 1.07E-01 1.50E-O3 9.98E-02

7277 TUBA4A -0.7 -0.4 -1.0 -0 8 1.71 E-03 1.43E-01 4.21 E-04 3.33E-03

7316 UBC 0.2 0 3 0.3 -0.1 4.19E-03 3.38E-03 7.02E-04 4.29E-01

7332 UBE2L3 -0 5 -02 -0.7 -0.4 2.20E-03 6.03E-01 466E-04 2.43E-02

7430 EZR 22 1.0 1.1 0.6 4.88E-09 2.84E-04 5.45E-05 5.98E-03

7453 WARS 2.7 0.3 1.7 1.4 2.06E-05 9.97E-01 3.93E-03 1.41 E-02

7456 WIPF1 -0.9 0.0 0.6 0.0 6.30E-06 1.00E+00 1.35E-03 1.00E+00

7846 TUBA1A 2.1 1.4 1.3 0.9 1.41 E-06 1.31 E-03 8.94E-04 1.72E-02

7903 ST8SIA4 -0.6 -0.1 1.2 0.6 2.61 E-03 1.00E+00 2.08E-05 1.17E-02

7942 TFEB 1.6 0.8 1.0 -0.4 1.03E-05 3.88E-02 2.36E-03 3.16E-01

8204 NRIP1 1.0 0.5 0.8 0.0 1.83E-06 1.06E-02 4.77E-05 1.00E+00

8411 EEA1 0.7 -0.2 0.4 0.9 1.18E-04 5.72E-01 3.00E-02 4.82E-05

8452 CUL3 0.0 -0.2 -0.2 0.0 1.00E+00 2.78E-03 2.22E-04 8.75E-01

8462 KLF11 0.6 1.7 1.4 -0.1 1.86E-02 2.60E-05 499E-05 1.00E+00

8480 RAE1 -0.9 -0.2 -0.4 -0.2 1.56E-07 8.50E-02 8.25E-04 8.73E-02

8507 ENC1 -1.7 -0.5 -0.9 -0.1 5.06E-07 1.33E-01 1.32E-03 1.00E+00

8535 CBX4 1.2 -0.1 1.2 0.1 1.53E-04 1.00E+00 6.83E-04 1.00E+00

8614 STC2 0.8 1.1 1.6 0.0 4.00E-03 4.56E-03 3.60E-05 1.00E+00

8662 EIF3B -0.9 -0.3 -0.4 -0.4 2.81 E-07 8.75E-02 3.34E-03 2.69E-03

8673 VAMP 8 0.9 0.1 1.0 -0.1 3.23E-05 1.00E+00 5.67E-05 7.81 E-01

8717 TRADD 0.7 0.3 0.5 -0.1 9.73E-06 6.13E-02 9.09E-04 8.54E-01

8754 ADAM9 0.7 0.5 0.7 0.1 9.52E-04 5.90E-02 1.99E-03 1.00E+00

8780 RI0K3 0.4 0.0 0.1 0.6 2.48E-04 1.00E+00 7.62E-01 4.71E-05

8799 PEX11B 0.5 0.1 0.4 0.1 1.89E-04 5.82E-01 2.65E-03 9.88E-01

8992 ATP6V0E1 0.5 0.3 0.4 -0.2 3.35E-05 1.74E-02 5.93E-04 9.11 E-02

9040 UBE2M -0.6 0.0 -0.2 -0.Θ 1.13E-03 1.00E+00 2.74E-01 3.33E-04

9063 PIAS2 -0.6 -0.2 -0.5 -0.4 4.53E-05 1.87E-01 1.27E-03 3.34E-03

9343 EFTUD2 -0.7 -0.3 -0.6 -0.4 1.23E-04 1.33E-01 2.09E-03 2.65E-02

9363 RAB33A 0.1 0.3 1.6 1.9 1.00E+00 1.85E-01 4.60E-07 5.22E-08

9416 DDX23 -0.6 -0.2 -0.5 -0.4 6.86E-04 6.33E-01 3.68E-03 2.25E-02

9446 GST01 -0.4 -0.3 -0.7 0.0 1.08E-03 1.10E-01 7.89E-05 1.00E+00

9451 EIF2AK3 0.8 1.4 1.1 1.2 2.18E-04 6.72E-06 3.55E-05 1.06E-05

9453 GGPS1 0.6 02 1.1 0.3 1.43E-03 7.10E-01 2.32E-05 1.64E-01

9466 IL27RA 0.8 0.4 -0.2 0.0 4.66E-07 1.83E-03 5.18E-02 1.00E+00

9517 SPTLC2 0.8 0.3 0.4 0.8 8.60E-O4 2.90E-01 1 68E-01 2.90E-03

9532 BAG2 -1.5 -1.1 -0.3 -0.6 1.25E-05 2.12E-03 3.56E-01 6.29E-02

9592 IER2 0.8 1.1 0.5 0.2 6.94E-04 7.33E-04 4.19E-02 7.56E-01

9653 HS2ST1 -0.4 -0.1 0.2 0.8 3.77E-03 7.92E-01 2.72E-01 1.94E-05

9711 KIAA0226 0.2 0.8 0.5 0.2 1.46E-01 3.30E-05 5.23E-04 2.05E-01

9778 IAA0232 0.7 0.7 0.1 0.4 7.41 E-04 3.33E-03 7.70E-01 7.82E-02

9832 JAKMIP2 0.2 0.9 2.3 2.8 5.49E-01 1.45E-02 1.16E-06 6.49E-08

9836 LCMT2 -0.8 -0.3 -0.4 0.0 3.00E-06 3.04E-02 3.68E-03 1.00E+00

9837 GINS1 -1.2 -0.3 -0.5 -0.3 5.80E-07 2.55E-01 4.44E-03 7.80E-02

9896 FIG4 1.6 0.4 1.1 -0.1 4.82E-06 2.55E-01 5 23E-04 9.51E-01 9948 WDR1 0.8 -0.2 0.2 -0.8 4.91 E-04 7.71 E-01 6.05E-01 2.29E-03

9989 PPP4R1 1.0 0.5 0.3 -0.1 3.40E-O7 2.22E-03 1.71 E-02 8.77E-01

10007 GNPDA1 -0.7 -0.3 -0.6 0.0 6.72E-05 2.17E-01 2.80E-03 1.00E+00

10087 C0L4A3BP 0.8 0.7 0.8 1.4 1.03E-05 3.10E-04 3.76E-05 5.22E-08

10105 PPIF -1.3 -0.4 -0.9 -0.3 1.52E-08 1.55E-02 8.29E-06 3.49E-02

10133 OPTN 2.5 0.8 1.5 0.1 7.81E-07 8.28E-02 5.96E-04 1.00E+00

10189 THOC4 -1.0 -0.3 -0.7 -0.6 3.08E-06 8.96E-02 5.23E-04 3.39E-03

10212 DDX39 -0.9 -0.1 -0.5 -0.2 7.70E-08 3.80E-01 1.53E-04 4.22E-02

10231 RCAN2 4.8 0.0 5.0 0.0 2.18E-13 1.00E+00 5.10E-13 1.00E+00

10248 POP7 -1.2 -0.2 -0.4 -0.7 6.33E-07 5.57E-01 4.08E-02 1.22E-03

10263 CDK2AP2 0.3 1.0 1.3 1.1 2 39E-01 2.82E-03 1.43E-04 7.47E-04

10287 RGS19 0.9 0.5 -0.8 -0.2 2.95E-04 7.28E-02 2.01E-03 6.44E-01

10328 COX4NB -0.7 -0 1 -0.3 -0.2 7.48E-09 2.79E-01 1.32E-03 3.65E-03

10365 KLF2 4.1 3.2 0.8 0.0 7.32E-11 2.15E-08 5.74E-03 1.00E+00

10413 YAP1 0.9 0.0 0.4 0.0 5.96E-08 1.00E+00 2.27E-04 1.00E+00

10431 TIMM23 -0.7 -0.3 -0.4 -0.6 1 74E-04 2.46E-01 2.04E-02 3.17E-03

10432 RBM14 -1.1 -0.7 -0.4 -0.1 3.48E-06 1.60E-03 2.24E-02 8.28E-01

10512 SEMA3C 1.4 0.0 1.9 0.0 2.80E-03 1.00E+00 6.96E-04 1.00E+00

10534 SSSCA1 -0.8 -0.2 -0.2 -0.8 2.45E-04 7.75E-01 3.64E-01 1.46E-03

10550 ARL6IP5 0.7 0.1 -0.2 -0.5 5.51 E-05 6.64E-01 3.79E-01 368E-03

10574 CCT7 -0.6 -0.3 -0.3 -0.4 1.67E-05 1.86E-02 1.03E-02 2.25E-03

10575 CCT4 -0.5 -0.3 -0.2 -0.2 4.73E-06 3.87E-03 8.77E-03 1 24E-02

10598 AHSA1 -1.1 -0.6 -0.8 -0.3 6.69E-05 4.37E-02 4.46E-03 2.49E-01

10682 EBP -0.2 0.2 1.0 1.4 4.15E-01 5.25E-01 7.91 E-05 2.94E-06

10713 USP39 -0.6 -0.2 -0.5 -0.3 2.99E-05 2.48E-01 9.30E-O4 2.03E-02

10761 PLAC1 2.1 0.0 1.6 0.4 1.00E-04 1.00E+00 4.00E-03 5.72E-01

10808 HSPH1 -1.3 -0.7 -0.7 -0.4 4.67E-06 1.04E-02 4.36E-03 7.49E-02

10884 MRPS30 -0.5 -0.5 -04 -0.3 2.96E-05 9.85E-04 1.22E-03 4.44E-03

10892 MALT1 0.7 0.3 0.3 0.2 3.40E-07 2.28E-02 2.54E-03 8.19E-02 0915 TCERG1 -0.7 -0.3 -0.5 -0.2 3.06E-05 1.71 E-01 3.10E-03 3.58E-01

10916 MAGED2 1.3 0.6 0.9 0.2 1.28E-05 3 88E-02 1.21E-03 4.26E-01

10955 SERINC3 0.5 0.1 0.4 0.4 4.98E-04 8.12E-01 4.44E-03 2.24E-02

10963 STIP1 -0.9 -0.4 -0.6 -0.6 1.43E-05 3.95E-02 3.10E-03 2.02E-03

10978 CLP1 -0.5 -0.1 -0.4 -0.2 9.37E-05 7.93E-01 2.66E-03 2.75E-01

11014 KDELR2 -1.2 -0.1 0.3 -0.5 1.43E-08 4.33E-01 2.98E-02 4.89E-04

11031 RAB31 2.0 0.0 3.9 0.0 9.95E-05 1.00E+00 3.29E-07 1.00E+00

11040 PIM2 -0.5 -0.1 -0.1 1.1 1.73E-03 8.76E-01 7.98E-01 6.96E-06

11161 C1 orf1 0.1 0.1 1.1 1.6 5.81E-01 1.00E+00 1.86E-05 4.77E-07

11164 NUDT5 -0.6 -0.1 -0.3 -0.6 1.13E-04 6.05E-01 5.17E-02 9.62E-04

11198 SUPT16H -0.8 -0.4 -0.6 -0.3 3.89E-05 8.70E-02 2.65E-03 6.22E-02

11335 CBX3 -0.6 -0.1 -0.3 0.0 3.73E-06 4.12E-01 3.60E-03 1.00E+00

22823 MTF2 -0.7 -0.3 -0.5 0.0 2.69E-06 2.75E-02 6.59E-04 1.00E+00

22824 HSPA4L -1.2 -1.3 -0.9 0.0 1.10E-04 7.11 E-04 4.79E-03 1.00E+00

22843 PPM1 E 2.9 0.0 1.4 0.0 8.51 E-07 1.00E+00 3.60E-O3 1.00E+00

22850 ADNP2 -0.4 -0.2 -0.5 0.0 9.52E-04 2.30E-O1 3.85E-04 1.00E+00

22870 SAPS1 -0.7 -0.1 -0.4 -0.6 1.00E-04 1.00E+00 2.74E-02 4.78E-03

22903 BTBD3 0.0 -0.8 -0.8 0.0 1.O0E+00 9.19E-04 2.70E-04 1.00E+00

22920 KIFAP3 1.4 0.4 1.0 0 3 2.82E-05 3.14E-01 1.68E-03 3.33E-01

23002 DAAM1 1.2 0.6 0.9 0.2 2.15E-05 6.41 E-02 1.39E-03 6.65E-01

23046 KIF21B 2.1 1.7 1.9 0.5 1.02E-04 8.40E-03 9.86E-04 4.56E-01

23062 GGA2 -1.1 -0.3 -0.8 -0.3 1.27E-05 3.39E-01 1.30E-03 2.73E-01

23158 TBC1D9 0.8 1.7 0.7 -1.2 2.60E-03 3.44E-05 2.43E-02 4.89E-04

23208 SYT11 1.4 2.2 0.0 0.0 1.87E-03 4.51 E-04 1.00E+00 1.00E+00

23214 XP06 -0.7 -0.2 -0.5 -0.3 1.88E-04 3.21 E-01 5.00E-03 1.98E-01

23221 RHOBTB2 0.7 0.2 0.2 1.2 1.74E-03 8.67E-01 8.29E-01 1.83E-04

23353 UNC84A 0.6 0.1 0.4 0.1 7.80E-O6 4.44E-01 1.21E-03 9.11E-01

23382 AHCYL2 1.0 0.8 0.5 -0.1 8.28E-05 3.87E-03 3.97E-02 1.00E+00

23401 FRAT2 -0.7 -0.1 -0.9 0.0 6.94E-06 1.00E+00 1.93E-06 1.00E+00

23433 RHOQ 1.5 0.9 0.9 0.2 5.82E-06 8.42E-03 2.33E-03 5.35E-01

23450 SF3B3 -0.9 -0.2 -0.4 -0.2 4.35E-07 1.25E-01 2.80E-03 2.56E-01

23646 PLD3 1.0 0.5 1.0 -0.4 1.37E-04 1.22E-01 3.62E-04 1.58E-01

23649 POLA2 -0.8 -0.2 -0.5 -0.4 1.05E-05 4.34E-01 2.79E-03 1.10E-02

23677 SH3BP4 1.1 0.2 1.0 0.1 9.63E-05 6.58E-01 1.02E-03 1.00E+00

25805 BAMBI 2.6 1.2 0.8 0.0 3 05E-O7 4.62E-03 2.47E-02 1.00E+00

25816 TNFAIP8 0.6 0.3 0.7 -0.2 5.93E-04 2.01 E-01 1.18E-03 5.10E-01

25824 PRDX5 0.8 0.2 0.6 0.1 5.24E-07 9.14E-02 1.09E-04 4.75E-01 25844 YIPF3 0.4 0.0 0.5 0.4 2.10E-03 1.00E+00 5.23E-04 7.39E-03

25893 TRIM58 1.0 0.8 -0.2 0.0 9.88E-05 4.95E-03 7.11 E-01 1.00E+00

26003 GORASP2 -0.4 0.0 0.5 0.1 2.77E-04 1.00E+00 2.70E-04 9.46E-01

26018 LRIG1 0.6 -0.5 -0.5 0.8 9.17E-04 3.32E-02 6.76E-03 1.75E-04

26073 POLDIP2 -0.4 -0.2 -0.1 -0.3 7.17E-04 1.56E-01 2.37E-01 3.42E-03

26121 PRPF31 -0.7 -0.3 -0.5 -0.5 2.80E-04 2.61 E-0 4.85E-03 7.08E-03

26277 TINF2 0.5 0.5 0.3 0.5 3.32E-04 3.47E-03 2.21 E-02 4.38E-03

26750 RPS6KC1 1.4 0.3 1.0 -0.1 7.68E-05 7.85E-01 3.60E-03 1.00E+00

26995 TRUB2 -0.9 -0.4 -0.5 -0.3 3.05E-06 3.48E-02 4.31 E-03 6.19E-02

26996 GPR160 0.9 0.1 1.2 0.0 1.61 E-03 1.00E+00 2.63E-04 1.00E+00

26999 CYFIP2 0.5 0.3 2.0 1.1 1.21 E-02 5.20E-01 2.20E-07 1.77E-04

27013 C2orf24 0.8 0.4 0.9 0.1 1.05E-04 9 16E-02 3.90E-04 1.00E+00

27069 GHIT 0.5 0.2 0.6 0.0 9.34E-04 5.85E-01 6.53E-04 1.00E+00

27242 TNFRSF21 -1.7 -1.0 -1.4 0.0 1.75E-04 4.37E-02 3.35E-03 1.00E+00

27327 TNRC6A -0.3 -0.2 0.0 0.0 6.35E-07 2.45E-03 1.00E+00 1.00E+00

27346 T EM97 -1 0 -0.2 1 4 2 2 1.11E-04 5.79E-01 7.12E-06 5.22E-08

28512 NKIRAS1 1.0 -0.2 0.9 -0.4 5.91E-04 9.93E-01 3.53E-03 2.28E-01

28974 C19orf53 -0.5 -0.2 -0.5 -0.3 3.58E-04 2.16E-01 4.20E-03 3.34E-02

28989 METTL11A -1.0 -0.3 -0.6 -0.4 7.80E-07 1.53E-01 6.75E-04 2.31 E-02

29015 SLC43A3 -0.9 0.1 0.0 -0.9 2.42E-04 1.00E+00 1.00E+00 1.53E-03

29960 FTSJ2 -1.1 -0.2 -0.4 -0.2 1.22E-07 2.13E-01 3.60E-03 1.47E-01

29969 MDFIC 0.3 0.4 0.5 0.0 1.41E-03 5.19E-03 2.09E-04 1.00E+00

49854 ZNF295 0.5 0.6 0.8 0.7 2.77E-03 5.70E-03 1.09E-04 7.59E-04

50615 IL21R 1.5 -0.4 2.6 1.3 4.23E-04 6.62E-01 9.08E-O6 7.40E-03

50814 NSDHL -0.5 -0.1 1.2 1.5 5.73E-03 1.00E+00 8.29E-06 8.22E-07

51006 SLC35C2 0.4 0.0 0.2 0.4 3.39E-04 1.00E+00 2.69E-02 2.08E-03

51063 CALH 2 2.1 1.7 0.0 0.0 5.18E-05 4.27E-03 1.00E+00 1.00E+00

51100 SH3GLB1 0.9 0.5 0.6 0.4 1.54E-05 2.93E-02 1.79E-03 5.79E-02

51110 LACTB2 -0.4 -0.3 -0.4 0.0 9.76E-04 3.28E-02 4.31 E-03 1.00E+00

51144 HSD17B12 0.7 -0.1 1.1 0.8 6.73E-05 1.00E+00 5.03E-06 2.42E-04

51155 HN1 -0.7 -0.2 -0.6 -0.2 1.40E-05 3.40E-01 1.34E-04 1.81 E-01

51174 TUBD1 -0.7 -0.2 -0.7 0.0 7.46E-04 7.95E-01 2.62E-03 1.00E+00

51 82 HSPA14 -1.0 -0.3 -0.6 -0.7 9.69E-06 1.68E-01 5.07E-03 2.69E-03

51202 DDX47 -0.8 -0.3 -0.7 -0.2 2.25E-05 1.52E-01 5.57E-04 2.21 E-01

51218 GLRX5 -0.6 -0.2 -0.3 -0.1 1.18E-05 1.10E-01 4.38E-03 3.13E-01

51274 KLF3 3.9 1.0 1.0 0.0 9.22E- 3 2.60E-04 3.47E-05 1.00E+00

51315 KRCC1 1.3 0.3 0.9 0.5 2.89E-05 4.14E-01 2.44E-03 6.08E-02

51321 AMZ2 0.4 0.1 0.6 0.0 1.99E-03 6.26E-01 1.25E-04 1.00E+00

51347 TA0K3 0.9 0.5 0.6 0.3 2.15E-05 4.12E-02 2.82E-03 1.93E-01

51390 AIG1 1.0 0.0 1.2 -0.2 5.02E-05 1.00E+00 2.29E-05 5.13E-01

51493 C22orf28 -0.7 -0.2 -0.1 -0.6 3.64E-07 1.94E-01 1.41 E-01 6.24E-06

51495 PTPLAD1 -0.6 -0.3 -0.7 -0.1 9.82E-05 1.15E-01 5.66E-05 4.04E-01

51593 - -1.1 -0.2 -1.2 -0.5 3.49E-06 5.19E-01 5.96E-06 7.62E-03

51805 C0Q3 -1.1 -0.4 -0.9 -0.7 4.72E-06 7.22E-02 1.60E-04 1.47E-03

51808 PHAX -0.7 -0.2 -0.5 -0.1 1.83E-06 8 74E-02 3.27E-04 6.11E-01

53916 RAB4B 1.3 0.4 1.0 0.5 9.54E-05 5.04E-01 4.44E-03 1.38E-01

54462 KIAA1 28 0.9 0.5 1.4 0.9 2 09E-03 1.89E-01 1.09E-04 6.93E-03

54464 XRN1 0.8 0.3 0.8 0.1 1.64E-04 3.33E-01 2.21 E-04 8.94E-01

54471 SMCR7L -1.1 -0.3 -0.4 -0.2 1.72E-08 1.82E-02 2.58E-03 1.69E-01

54522 ANKRD16 -1.3 -0.5 -1.2 -0.3 366E-06 8.75E-02 3.76E-05 3.05E-01

54676 GTPBP2 4.0 1.3 1.9 0.8 4.45E-07 5.30E-02 2.18E-03 2.11E-01

54815 GATAD2A -0.6 -0.2 -0.5 -0.1 2.86E-05 2.40E-01 1.78E-03 8.44E-01

54823 C1orf26 1.0 0.7 1.1 0.2 1.74E-04 2.22E-02 1.56E-04 5.00E-01

54838 C10orf26 0.9 0.7 0.1 0.4 7.52E-05 4.31 E-03 8.01 E-01 3.57E-02

54861 SNRK 0.6 0.0 0.5 -0.1 2.06E-04 1.00E+00 2.58E-03 8.18E-01

55038 CDCA4 -0.9 -0.2 -0.5 -0.2 6.43E-06 3.15E-01 4.31 E-03 2.83E-01

55108 BSDC1 0.6 0.6 0.1 0.4 2.49E-04 2.88E-03 5.54E-01 1.08E-02

55129 AN010 1.3 0.1 1.0 0.1 1.32E-05 1.00E+00 6.11 E-04 8.97E-01

55135 WDR79 -0.7 -0.1 -0.4 -0.3 5.32E-06 5.49E-01 2.58E-03 1.89E-02

55159 RFWD3 -1.0 -0 2 -0.6 -0.2 834E-06 3.83E-01 4.25E-03 4.50E-01

55218 EXDL2 0.8 0.4 0.7 0.0 2.07E-05 6.40E-02 4.78E-04 1 OOE+00

55255 WDR41 1.2 0.5 0.9 0.3 2.30E-05 8.14E-02 9.87E-04 3.59E-01

55281 TMEM140 2.6 1.6 2.5 0.6 1.49E-04 4.64E-02 7.88E-04 5.18E-01

55687 TRMU -1.1 -0.5 -0.8 -0.3 4.54E-06 1.53E-02 5.28E-04 7.55E-02

55706 TMEM48 -1.0 -0.3 -0.5 -0.2 6.31E-06 1.54E-01 4.81 E-03 5.28E-01

55737 VPS35 0.3 0.2 0.1 0.3 5.52E-05 1.40E-02 6.93E-02 9.88E-04 55848 C9orf46 0.9 0.1 0.5 -0.1 3.68E-06 1.00E+00 4.18E-03 1.00E+00

55860 ACTR10 0.5 0.2 0.4 0.2 2.35E-05 6.89E-02 5.23E-04 3.83E-02

55969 C20orf24 -0.2 0.1 -0.2 -0.5 1.63E-03 9.09E-01 2.57E-02 2.94E-06

56271 BEX4 1.4 0.4 1.9 0.1 1.21 E-03 6.32E-01 2.49E-04 1.00E+00

56882 CDC42SE1 1.0 0.4 0.2 0.1 5.47E-08 2.91 E-03 1.38E-01 8.23E-01

56906 THAP10 0.8 0 1 1.6 -0.1 2.02E-03 1.00E+00 5.52E-06 1.00E+00

56993 TO M22 -0.9 -0.4 -0.8 ^•0.6 1 64E-04 1.09E-01 1.39E-03 7.38E-03

57062 DDX24 -0.7 -0.4 -0.6 -0.4 2.06E-04 1.06E-01 3.67E-03 2.66E-02

57409 MIF4GD 0.8 0.1 0.7 0.0 5.05E-04 1.00E+00 3.46E-03 1.00E+00

57493 HEG1 1.5 0.0 1.3 0.1 3.49E-05 1.00E+00 3.61 E-04 1.00E+00

57630 SH3RF1 2.0 0.0 1.1 0.0 1.24E-08 1.00E+00 3.60E-05 1.00E+00

57696 DDX55 -1.5 -0.7 -0.7 -0.3 6.43E-07 9.04E-03 3.92E-03 1.86E-01

57761 TRIB3 2.6 1.5 2.6 2.1 2.94E-04 6.82E-02 1.00E-03 7.51E-03

57820 CCNB11P1 1.0 06 0.5 1.2 2.33E-04 548E-02 7 39E-02 2.26E-04

58487 CREBZF -0.6 0.0 0.0 0.4 1.19E-06 1.00E+00 1.00E+00 1.41 E-03

59274 MESDC1 0.6 0.6 0.8 0.5 2.03E-03 9.15E-03 4.43E-04 1.81E-02

60481 ELOVL5 -0.1 0.0 0.5 0.4 2.15E-01 1.00E+00 1.63E-05 7.52E-05

63826 SRR 1.1 0.2 0.7 0.3 1.57E-06 3.43E-01 5.42E-04 6.76E-02

64093 SMOC1 -1.2 0.0 -1.9 0.0 7.70E-04 1.00E+00 6.15E-05 1.00E+00

64224 HERPUD2 1 5 02 0.6 0.5 1 79E-07 3.74E-01 2.71 E-03 8.16E-03

64645 HIAT1 -0.2 0.1 0.6 06 1.12E-02 2.01E-01 6.15E-06 5.27E-06

64743 WDR13 1.0 0.4 0.8 0.4 6.83E-05 1.61E-01 2.41 E-03 1.40E-01

64785 GINS3 -1.4 -0.3 -0.4 0.0 5.16E- 1 8.95E-03 1.09E-04 1.00E+00

64860 ARMCX5 -0.6 -0.7 0.2 0.0 4.80E-06 7.34E-06 1.68E-01 1.00E+00

64979 MRPL36 -0.7 -0.3 -0.4 -0.6 1.84E-05 8.90E-02 4.18E-03 3.88E-04

65084 T EM135 0.1 0.1 0.7 0.6 2.18E-01 6.72E-01 3.72E-06 4.04E-05

65258 MPPE1 0.5 0.1 0.4 0.3 3.54E-04 8.90E-01 4.43E-03 3.51 E-02

65985 AACS -0.5 -0.1 1.0 -0.1 4.56E-03 1.00E+00 1.58E-05 9.57E-01

65993 MRPS34 -1.0 -0.3 -0.4 -0.5 1.93E-06 1.10E-01 2.15E-02 4.95E-03

79071 EL0VL6 0.2 0.7 1.4 1.8 5.19E-01 3.66E-02 5.59E-05 4.32E-06

79144 C20orf1 9 1.0 0.1 0.2 0.6 1.38E-05 9.52E-01 3.09E-01 4.42E-03

79180 EFHD2 -0.6 -0.2 -0.7 -0.7 7.92E-04 4.70E-01 1.94E-03 2.14E-03

79364 ZXDC 0 5 0 1 0.0 0.7 1.84E-04 9.26E-01 1.00E+00 2.98E-05

79612 NARG1L -1.0 -0.6 -0.6 -0.4 3.40E-O6 6.25E-03 1.31 E-03 2.04E-02

79639 TMEM53 1 3 0 0 0.0 1 3 7 60E-O5 1.00E+00 1.00E+00 5.92E-04

79647 AKIRIN1 -0.2 -0.2 -0.3 -0.4 8.45E-03 6.37E-02 2.58E-03 2.10E-04

79668 PARP8 1.5 0.8 1.3 0.1 4.81 E-05 6.32E-02 7.40E-04 1.00E+00

79675 FASTKD1 -1.2 -0.5 -0.7 -0.5 2.21 E-05 7.99E-02 3.59E-03 3.48E-02

79693 YRDC -1.3 -0.6 -0.6 ■0.3 9.52E-07 7.49E-03 3.46E-03 2.38E-01

79710 M0RC4 1.1 0.5 1.3 0.1 2.31 E-04 1.58E-01 1.46E-04 1.00E+00

79723 SUV39H2 -1.0 -0.5 -0.8 -04 6.52E-05 6.56E-02 2.19E-03 7.94E-02

79800 ALS2CR8 1.8 0.1 1.1 0.8 9.80E-06 1.00E+00 3.92E-03 2.85E-02

80267 EDEM3 -0.9 -0.4 -0.4 0.2 9.53E-08 8.69E-03 1.90E-03 1.56E-01

80308 FLAD1 -0.7 -0.5 -0.4 -0.9 8.79E-04 1.04E-01 6.10E-02 1.47E-03

80777 CYB5B 0.0 0.1 0.4 0.8 1.00E+00 1.00E+00 2.82E-03 9.68E-06

81537 SGPP1 2.2 0.4 0.3 1.0 6.61E-08 1.86E-01 2.43E-01 1.25E-03

81566 FAM130A1 2.2 0.9 1.0 0.7 3.48E-07 1.78E-02 3.29E-03 2.86E-02

81619 TSPAN14 1.7 0.0 0.0 0.8 1.19E-07 1.00E+00 1.00E+00 1.40E-03

83468 GLT8D2 1.2 0.0 1.7 0.0 9.63E-05 1.00E+00 1.07E-05 1.00E+00

83593 RASSF5 -07 -02 0.0 -0.5 2.49E-05 4.44E-01 1.00E+00 1.09E-03

83729 INHBE 3.4 4.5 3.1 3.2 9.96E-04 1.05E-03 6.33E-03 7.22E-03

84247 LD0C1L 1.0 0.5 0.5 0.0 2.10E-06 8.96E-03 2.76E-03 1.00E+00

84329 HVCN1 2.4 0.4 2.3 0.0 9.72E-05 8.47E-01 5.23E-04 1.00E+00

84678 FBXL10 -0.5 0.3 -0.5 -0.2 2.00E-04 7.48E-02 2.72E-03 3.27E-01

84707 BEX2 1.5 0.4 2.1 0.0 4.29E-04 7.34E-01 8.52E-05 1.00E+00

84817 TXNDC17 -0.4 0.0 -0.4 -1.0 2.04E-03 1.00E+00 8.21 E-03 9.09E-07

84842 HPDL -2.6 -1.7 0.0 0.0 4.33E-06 2.43E-03 1.00E+00 1.00E+00

84934 C12orf52 -1.1 -0.2 -0.8 -0.4 3.09E-06 4.84E-01 2.78E-04 4.11 E-02

85007 AGXT2L2 1.4 0.5 1.0 0.4 2.43E-06 5.90E-02 1.37E-04 8.82E-02

85415 RHPN2 -05 -0.4 -0.7 0.1 4 87E-04 3.45E-02 7 94E-05 1 OOE+OO

90416 C15orf57 1.2 -0.1 0.5 0.0 2.79E-07 1.00E+00 3.06E-03 1.00E+00

91012 LASS5 0.7 0.1 0.5 0.1 3.87E-05 1.00E+00 2.17E-03 9.27E-01

91694 L0NRF1 1.4 0.9 1.7 0.5 2.10E-06 1.25E-03 9.57E-07 3.57E-02

91860 CALML4 09 0.1 1.1 -0 3 7.60E-05 1.00E+00 2.56E-05 1.91E-01

93627 - 0 7 0.0 0.5 •0.1 1.65E-05 1.00E+00 1.70E-03 1.00E+00

96764 TGS1 -0.6 -0.3 -0.4 -0.1 2.58E-06 4.95E-03 6.68E-04 4.01 E-01 114044 MC 3APAS -0.5 -0.1 1.5 2.1 1.88E-01 1.00E+00 5.08E-04 1.91 E-05

114926 C8orf40 0.9 0.0 0.7 0.4 9.91 E-06 1. OOE+00 4.08E-04 3.92E-02

115294 PCMTD1 0.6 0.1 0.8 0.5 1.46E-03 1. OOE+00 6.35E-04 1.66E-02

116442 RAB39B 1.4 0.9 2.0 0.0 2.00E-04 3.35E-02 2.34E-05 1.00E+00

116496 FA 29A 1.7 0.8 1.8 0.0 1.38E-05 4.27E-02 2.18E-05 1.00E+00

122769 PPIL5 -0.8 -0.3 -0.4 -0.4 449E-06 1.33E-01 4.61 E-03 5.30E-03

130535 KCTD18 0.9 0.2 0.3 0.7 2.30E-05 4.40E-01 8.34E-02 1.04E-O3

130814 PQLC3 1.0 0.3 0.3 1.2 5.57E-04 4.95E-01 3.24E-01 9.86E-04

134111 0.9 0.5 1.2 0.0 2.45E-06 8.50E-03 4.81 E-07 1 OOE+00

140545 RNF32 1.4 0.2 1.6 1.4 6.38E-04 1.00E+00 7.22E-04 3.52E-03

150275 CCDC117 -0.7 -0.4 -0.5 -0.2 1.52E-05 1.18E-02 368E-03 1.18E-01

152926 PPM1K 0.0 0.0 0.9 0.7 1.00E+00 1.00E+00 2.36E-04 2.83E-03

153222 C5orf41 2.1 0.2 0.6 1.8 2.51 E-05 1.00E+00 2.29E-01 9.22E-04

157570 ESC02 -0.9 -0.5 -0.8 -0.3 1.75E-04 6.85E-02 1.85E-03 1.64E-01

171568 POLR3H -0.9 -0.2 -0.5 -0.1 2.15E-06 3.25E-01 3.04E-03 5.76E-01

196743 PAOX 1.7 0.1 1.8 0.4 2.16E-04 1. OOE+00 5.63E-04 4.61 E-01

199953 TMEM201 -1.7 -0.7 -1.6 -0.5 2.77E-05 1.26E-01 2.39E-04 2.29E-01

201161 PRR6 -1.7 -1.1 0.0 -0.2 2.69E-06 1.76E-03 1.00E+00 7.05E-01

221336 BEND6 1.8 0.4 1.6 0.0 4.29E-05 5.14E-01 6.71E-04 1. OOE+00

221491 C6orf1 1.3 0.9 1.4 0.3 1.69E-04 2.02E-02 3.27E-04 5.34E-01

222229 LRWD1 -0.6 -0.4 -0.6 -0.2 9.94E-04 5.51 E-02 2.32E-03 2.25E-01

283078 MKX 2.0 2.5 2.1 0.0 6.28E-06 6.72E-06 1.29E-05 1. OOE+00

284459 HKR1 -0.9 -0 1 0.1 0.7 9.00E-O5 1. OOE+00 1.00E+00 3.84E-03

317649 EIF4E3 1 3 0 0 09 0.0 1.95E-07 1.00E+00 6.75E-05 1. OOE+00

326625 MMAB 0.1 0.1 1.7 2.5 1. OOE+00 1. OOE+00 4.80E-05 7.55E-07

353189 SLC04C1 1.7 1.4 3.9 -0.1 2.15E-04 7.22E-03 1.36E-07 1. OOE+00

403313 PPAPDC2 1.0 0.2 0.5 0.9 4.29E-04 7.71E-01 7.75E-02 3.00E-03

677824 SNORA43 -1.8 -0.2 -1.1 -0.2 1.79E-05 9.73E-01 4.15E-03 9.51E-01

100128062 SLC2A3P1 0.1 0.0 1.0 1.1 8.36E-01 1. OOE+00 8.61 E-05 5.01 E-05

DISCUSSION

[00191] It is critical to reliably identify subsets of patients who can benefit from anti-cancer statin therapy. To attain this goal it is necessary to better understand the molecular mechanisms of statin-induced apoptosis and delineate specific determinants of sensitivity. It is well known that statins trigger tumor- specific apoptosis by inhibiting HMGCR, the rate-limiting enzyme of the MVA pathway.² For many years it was believed that the anti-cancer activity of statins was mediated by disrupting the signaling cascade downstream of Ras, an oncogenic protein that requires MVA-dependent isoprenylation, and that this would prove to be the key determinant of statin sensitivity. However, previous work indicates this is likely to be an over-simplification and suggested that statins trigger apoptosis in sensitive tumor cells via the cumulative loss of all isoprenylation-dependent signaling cascades.^21,43 As it appears that the isoprenylation status of Ras and any other individual isoprenylated proteins are not useful indicators of sensitivity, new options are required.

[00192] These results suggest that a key element of tumor cell sensitivity to statin-induced apoptosis lies in the feedback regulation of the MVA pathway. Microarray data revealed that lovastatin exposure did not induce the expected feedback response in statin-sensitive MM cells, although this response was intact in statin-insensitive cells (Figure 1 and Table 1). Without wishing to be bound to theory it is hypothesized that this deficiency in upregulating MVA pathway gene products, such as HMGCR (Figure 2), ultimately leads to tumor cell death. It is also of particular note that cholesterol metabolism genes have been previously linked to general chemotherapy resistance in MM.⁴⁴ This subset of genes, including HMGCR, was identified as being significantly increased in cells that had acquired melphalan drug resistance either by cell-adhesion or by selection, though statin resistance was not assessed. It is possible that this drug resistance does not correlate with statin sensitivity or insensitivity because overexpression of HMGCR-FL in statin-sensitive KMS11 cells did not modulate sensitivity to melphalan (Figure 3E). This theory is also corroborated by findings disclosed in PCT application filed January 13, 2011 (Linda Penn et al) which discloses that a combination treatment comprising a statin and a compound that inhibits statin induced HMGCR expression upregulation, renders statin insensitive cancer cells, sensitive to the combination treatment.

[00193] Further analysis of a publically available microarray dataset revealed that certain genes of the sterol response pathway are differentially expressed in statin-sensitive MM cells compared to insensitive cells at a basal level (Figure 4). Cumulatively, these differences serve as statin-sensitivity determinants in MM and in other tumors. While HMGCR and HMGCS1 are both sterol-responsive genes with SRE transcriptional regions within their promoters, other transcription factors have been found to have important roles in mediating their expression as well, either acting as co-activators or repressors.^45"47 This may explain differences in the basal mRNA levels of SRE-regulated genes and further suggests that a unique combination of transcriptional regulators may drive the differential expression. It is also possible that specific oncogenic mutations underlie the observed dysregulation of MVA pathway gene expression. The expression of LDLR, another canonical SRE-regulated gene product, was not consistently upregulated in either sensitive or insensitive MM cell lines (Figure 5B). In fact, its variable expression suggests that LDLR regulation could be more universally aberrant, an observation that has been made in both AML and prostate cancer previously.^48"50 It was also recently reported that MM patients generally present with hypocholesterolemia.⁵¹

[00194] A novel splice variant of HMGCR, HMGCR-D13, has not yet been fully characterized. While it has been shown to be widely expressed in a panel of normal tissues,⁴¹ little is known about the role and regulation of HMGCR-D13 in human cancer. Interestingly, direct evidence has shown that a SNP (rs3846662) in intron 13 regulates the alternative splicing of HMGCR.⁵² HMGCR-D13 has also recently been associated with a decreased cholesterol-lowering response in lymphocytes exposed to simvastatin.³¹ Differential expression of HMGCR-FL and -D13 may impact both tumor etiology and statin sensitivity, and thus it will be critical to further evaluate. If HMGCR-D13 has enzymatic activity refractory to statin inhibition, it would predict that cancers which elevate its expression will also be refractory to statins. Conversely, loss of HMGCR-D13 could sensitize cells to the anti-proliferative activity of statins. In the present study, expression of both HMGCR-FL and -D13 was monitored. While HMGCR-FL mRNA levels are about 10-fold higher than HMGCR-D13, they are both upregulated 2-4 fold in response to lovastatin exposure and largely appear to be co-regulated (Figure 2). Furthermore, ectopic expression cHMGCR-FL, but not cHMGCR-D13, decreased lovastatin-induced apoptosis of sensitive MM cells. The decreased sensitivity conferred by cHMGCR-FL was statin-specific as cells exposed to melphalan and bortezomib, agents commonly used in the clinical management of MM, did not display a differential in sensitivity. Cells expressing the cHMGCR-D13 construct were just as sensitive to statin-induced apoptosis as cells expressing the empty vector (Figure 3C and D), however, it is possible that this was due to the expression of cHMGCR-D13 being considerably lower than that of cHMGCR-FL. Because control elements would be identical within each vector, this expression difference suggests that cHMGCR-FL could be more stable than cHMGCR-D13. This observation could have interesting implications on the nature and regulation of a cell's total HMGCR activity and warrants future investigation comparing the two isoforms. Nonetheless, the results do agree with one of the few previous studies on HMGCR-D13 in which HMGCR-D13 was unable to restore HMGCR activity when expressed in an HMGCR-deficient CHO cell line.⁵² The results suggest that while the expression of HMGCR-D13 may have diagnostic or prognostic potential as a determinant of statin sensitivity, it does not appear to have enzymatic activity equivalent to HMGCR-FL.

[00195] The results show that normal feedback regulation of the MVA pathway is compromised in a subset of MM tumors. In non-transformed cells this feedback response allows statins to work as cholesterol-lowering agents. It may be this very same feedback response that also prevents a cell, normal or statin- sensitive tumor, from undergoing statin-induced apoptosis. Formulated from the results of tissue culture experiments, this model was further supported by analysis of primary patient cells (Figure 6). Interestingly, deficient feedback control or increased expression and activity of HMGCR have been reported in some tumors.^53,54 While these observations suggest a more global dysregulation of the pathway occurs in cancer, the results show that there are likely unique subsets of tumors with dysregulated MVA pathways. In fact, dysregulation of the mevalonate pathway provides a molecular rationale for the significant therapeutic index of statins observed in sensitive tumor cells. When such sensitive cells are specifically targeted in an orthotopic murine model of MM, statin therapy is very effective and well tolerated (Figure 7). While it should be noted that statins will likely be more effective when combined with other agents, the importance of selecting an appropriate group of patients to treat will be critical to the successful use of statins as anti-cancer agents.

Example 2

Predicting Statin Sensitivity based on a mRNA-abundance signature Resources

[00196] The cell-lines used were:

7 Sensitive: 8226, OCI_MY7, H929, KHM11 , KHMM, KMS11 , OPM2

9 Resistant: SKMM1 , ANBL6, U266, ARK, EJM, JJN3, LP1 , MM1J44, OCI_MY5 [00197] Basal mRNA abundances were profiled on U133 Plus 2.0 arrays normalized via MAS5 ⁵⁵ and annotated using the Affymetrix NetAffx annotations (version na30, which incorporates Entrez Gene annotations of 2009-09-03). All analyses were implemented in the R statistical environment (v2.10.1) using the randomForest (v4.5-34), cluster (v1.12.1), lattice (vO.17-26), and latticeExtra (v0.6- 4) packages, along with custom code. Random Forests contained 10,000 trees. Clustering was performed using the DIANA algorithm using Pearson's correlation as a similarity metric. An additional 50 cell-lines with no statin-sensitivity information are available and were used to test the ability of the signatures identified to predict statin sensitivity. Several distinct feature-selection methods were employed to identify predictive biomarkers capable of determining patients who might benefit from statin therapy, or from other therapies that target MVA metabolism or HMGCR activity.

Signature #1 : 4-Genes

[00198] The p-value for differential mRNA abundance between sensitive (n=7) and resistant (n=9) cell-lines was calculated (Student's t-test, two-tailed, unpaired, with heteroscedasticity adjustment). Genes were ranked in ascending order of this p-value, and the top 10 genes were selected in an initial screen for reproducible differences. Next, the coefficient of variation (CV = standard-deviation / mean) was calculated for each gene. This is a measure of the magnitude of the effect-size. The four genes with maximal effect sizes were selected for use in machine-learning.

[00199] The signal intensities of these four genes were then incorporated into a Random Forest containing 10,000 trees. A Random Forest is a collection of unpruned decision tree classifiers derived using bootstrap sampling. This use of bootstrap-sampling allows an unbiased internal estimate of classifier accuracy ⁵⁶. Additionally, the classifier was subjected to a full leave-one-out cross-validation (LOOCV). One sample was removed from the analysis, p-values were calculated on the remaining 15 samples, the 10 genes with the smallest p-values were ranked in descending order of CV, and a 10,000 tree Random Forest was developed from the 4 highest CV genes. This Random Forest was then used to classify the held-out sample. This procedure was repeated for all samples, creating a completely LOOCV prediction of accuracy. Probe importance was then ranked by the mean decrease in accuracy resulting from their omission during Random Forest bootstrapping. This overall procedure is analogous to the reported mSD technique, but with a switch from unsupervised-classification to supervised- classification and feature-selection modifications to increase sensitivity in this smaller-sized dataset ⁵⁷.

Prediction Accuracy (OOB): 81.25%

Prediction Accuracy (LOOCV): 81.25%

Misclassified Cell-Lines: KMM 1 , U266, ARK

Table 4

Entrez

Rank ProbeSet Gene Symbol Name

ID

1 232 08 at 79634 SCRN3 secernin 3

phosphofurin

acidic cluster

220557 s at 55690 PACS1 sorting protein

1

acyl-

Coenzyme A

204242 s at 8310 ACOX3

oxidase 3, pristanoyl

zinc finger,

233060 at 57178 ZMIZ1 MIZ-type

containing 1

Figure 10 is a heatmap demonstrating the relevant abundance of the four genes sensitive and insensitive cell lines.

The individual genes are each univariate predictors.

Table 4 continued

Univariate analysis

n P M CV

232108_at 0.000226696 1.419444856 56.40324557

220557_s at 9.40E-05 1.715979317 62.86536505 204242_s_at 0.000311532 1.685410945 65.56776193

233060_at 0.000283293 1.324203445 59.20904878

Probe sequences

Array values

Signature #2: 20-Genes

[00200] Signature #2 was developed and validated identically to Signature #1 , with the exception that the secondary feature-selection using the coefficient of variation was omitted. The motivation for this was to determine if using a larger signature that includes low-information-content genes would still be efficacious. The top 20 genes, as determined by t-test analysis, were included in a 10,000 tree Random Forest, with OOB, LOOCV, and variable-importance analysis as described above. Genes in bold in the table below are those overlapping between the 4- and 20-gene signatures

Prediction Accuracy (OOB): 100%

Prediction Accuracy (LOOCV): 100%

Mis-Classified Cell-Lines: All classifications were correct

Table 5

Entrez

Rank ProbeSet Gene Symbol Name

ID

myosin, light chain 4,

210395_x_at 4635 MYL4

alkali; atrial, embryonic

GOLGA golgi autoantigen,

226949_at 2802

3 golgin subfamily a, 3 leucine rich repeat

3 216164_at 10446 LRRN2

neuronal 2

4 231454_at 191585 PLAC4 placenta-specific 4

LOC284 hypothetical

5 1562716_at 284632

632 LOC284632

6 232108_at 79634 SCRN3 secernin 3

7 215875_at N/A N/A N/A

trichoplein, keratin

8 223456_s_at 84260 TCHP

filament binding chromosome 9 open 9 2191 7_s_at 54981 C9orf95

reading frame 95 additional sex combs

10 226251_at 55252 ASXL2

like 2 (Drosophila) phosphofurin acidic

220557_s_a

11 55690 PACS1 cluster sorting t

protein 1

prostaglandin E

12 200627_at 10728 PTGES3

synthase 3 (cytosolic) 13 236465 at 285533 RNF175 ring finger protein 175 14 1558444_at N/A N/A N/A

15 562472_at N/A N/A N/A

solute carrier family 25

SLC25A

16 218725_at 79751

22 (mitochondrial carrier:

glutamate), member 22

204242_s_a

17 acyl-Coenzyme A

8310 AC0X3

t oxidase 3, pristanoyl

KIAA036 KIAA0368

18 212428_at 23392

8

zinc finger, MIZ-type 19 233060_at 57178 ZMIZ1

containing 1

1556221_a_ N/A

20 N/A N/A

at

Figure 11 is a heatmap demonstrating the relevant abundance of the 20 genes in sensitive and insensitive cell lines.

Individual genes also predict statin sensitivity.

Table 5 continued Univariate analysis

ProbeSet P M cv

210395_x_at 0.000234441 0.990398843 41.3198097

226949_at 8.73E-05 -0.553758053 24.49672132

216164_at 0.000662369 1.602668361 65.25179313

231454_at 0.000582835 -1.782999491 73.93489205

1562716_at 0.000641204 -0.834961561 39.06013787

232108_at 0.000226696 1.419444856 56.40324557

215875_at 0.000441487 1.150461515 47.1285297

223456_s_at 0.000364889 -1.174620197 52.98893769

219147 _at 0.000581761 1.510485224 61.74461397

226251_at 0.000503777 0.654447238 29.50597577

220557_s_at 9.40E-05 1.715979317 62 86536505

200627_at 0000166483 -0400033176 18 10721922

236465_at 0.000241278 -1.053270591 47,98623666

1558444_at 0.000361995 -0.804880075 37.57255042

1562472_at 0.000483119 1.222392411 5141351954

218725_at 0.000566499 -0.434873786 20.83941315

204242_s_at 0.000311532 1.685410945 65.56776193

212428_at 0.000306659 0.545142297 23.62903406

233060_al 0.000283293 -1.324203445 59.20904878

1556221_a_at 0.000572282 -1.084506286 51.24440968

Probe sequences

ProbeSet Probe 1 Seq lO # Probe 2 Seq lD # Probe 3 Seq lD #

CTACCAATGCCGAGGTGC TGGACTTTGAGACGTTCTT GCAGGGCACCTATGAG

210395_x_at TGCGTGT 57 GCCCAT 58 GACTTCGTG 59

GACAGGCAGGCACCGTGA CTCTCCCAGGTGTCTCAG ATGTGTGAGATCTTCTG

226949_al GGAAGGT 60 AGACAGA 61 TTTCCTAC 62

GGCTGCTCTCTGGGCTAA CAGAGAGTGGATCTTCTC GGCTGGGCAAACCTCA

216164_at GAAGGAC 63 CTCAGGG 64 TGGAGTGGC 65

TGCCAGCTACAGGTGCTC AAGCAAGCCAGAC CATATT GGAGAAGTTGGGGCTC

231454_at ACCTGAA 66 AACCCT 67 TGAAGTGGG 68

TCCAAGCTGCAAGATCTGA GATCTGACCAAATCACTTC TGGCTACACCAACGCT

1562716_at CCAAAT 69 TCCAGG 70 CATGAATCT 71 ACCCAGCACCAGAATGGA AAAATGACATGTAACCACC AACCACCATTATAGTAC

232108_at ACATTTG 72 ATTATA 73 TATACAGA 74

CCCAAGGCTGTACACTGG GGCACTAAGATGAGACCG AGCCCAGGGTTCTGGT

215875_at CTGAGGT 75 CAGCACC 76 TCCTAGAGC 77

GGCGGAGACTATGGCTGA AATTGCTTGGAACTGACTT GCCGCCAGGCAGTTTT

223456_s_at GCAGGGC 78 CATGGG 79 ACAGGGCTC 80

GACTCTCCGGGATACTTTG GGCCATGTGTGGCCCATG TGGGAAGTTGTGTACC

2191 7_s_at ATGGCC 81 TATCTAA 82 TGGATGGAA 83

GACAGTGGAAGACTTTGA GAATTTCTCATATATGGTA GTATGTTTGATGTAGGA

226251_a( ATTTCTC 84 TGTTTG 85 CCTCACTG 86

CACCATGGCCATGACTGT GGCCATGACTGTGGTCAC AGAAC AAGAAAGTTC C

220557_s_at GGTCACC 87 CAAAGAA 88 CACCATCTT 89

CCTAAGTCTCCTTTTCTTC TGACATGATAGGAAGCTCT GGAACAAATTGGCTGA

200627_at ATAACA 90 CAGCTT 91 CACCTTACT 92

GAATTCTGCATCCGAGGTT AAAAGCAGACTTGCCCTTA GATGATCAGTAATCCCT

236465_at GGTGTA 93 CTGCAA 94 GGGAGCGC 95

CAGGACACTGCTATGAGA TCTTAATGTTTGTC CTTCA GATTCTAGAACACTCCA

1558444_at GTCTTAA 96 CATAGA 97 GCTGGCTT 98

TTACTCTTTTACTCTTTTCT CACATTTTCTGTTATTCGG AATGATCTCATTCTATT

1562472_at GTATG 99 TTTTCA 100 TGCCAATT 101

AACTAACTTACCCAGCTTG CACCCTGGCCTGGGATTG ATTGGCCAGGGAGCAG

218725_at GTCTCT 102 GCCAGGG 103 GGCGGGCAT 104

GCCCTGCTCTACCGAGGA ATACTTCTCCGGTGAGCA GCAGTTGCCCTGGTAG

204242_s_at GGATACT 105 GGCGGGA 106 ACGTGATCG 107

GCCTGGCCGCGAAACGCG GCGGAGACCCAACGTTGT GTTATCGTCAGGAGCT

212428_at GAGACCC 108 TATCGTC 109 GTGCAAACT 110

CAAATGAGGAGAGGC GGG CCATCCCGTTGTTCAGGT GGTGACCTGGCTATGT

233060_at CCCCATC 111 GACCTGG 112 GACGTTGGC 113

CATATTAGTGTTGCCAGGA ATCTTGATTGTGTTAGTGC GTGCCCACATGACTGT

1556221_a_at GCAAAA 114 CCACAT 115 ATGCATTTC 116

ProbeSet Probe 4 Seq ID # Probe 5 Seq ID # Probe 6 Seq ID #

GGTCATGGGTGCTGAGCT GTCCTTGCCACCCTGGGA GAAGCCTTTGTCAAGC

210395_x_at TCGGCAC 117 GAGAAGA 118 ACATCATGT 119

GCAGACACTGGCTGACTG GTGGGGTCTGTAAGTTGT TAAGTTGTCCCCTAGTT

226949_at GGAGAGG 120 CCCCTAG 121 TGCTAAGA 122

GGCACAGGAAGGGTAGCC GGACCCCAGCAGATGTGG TCATGGAATAGCCTGAT

216164_at ACTGCAG 123 AGTTTCA 124 CCCTCTGC 125

AAGTGGGAACACAAGGCT TGCCACTTTCAGCCTAGC CAGATGGAGTGTGCAT

231454_at GCCTTTT 126 GTGAAAC 127 TCCCACTTC 128

GGGTGGTTCCACATGCAA GCAAAGCCAAAACCTCTG TCTGAACTTCTTCCAGG

1562716_at AGCCAAA 129 AACTTCT 130 TTTCTGGA 131

TGTCATACCATTGGAATCA TACAGT ATGTGACCTCTTA ATGTGACCTCTTAAGAT

232108_at TACAGT 132 AGATTG 133 TGTCCTCT 134

TTCCTGAGACGGGGACCA CCTCTCTCGGCCATAATG AATGGGCTCAATCCTG

215875_at GGGCCTC 135 GGCTCAA 136 AACTCAGGT 137

GCTCTGTTAACAGTAAGTG AAGTGCCCGGGGCACTGT GTCAGATGGCTCAGCA

223456_s_at CCCGGG 138 CAGATGG 139 GTGCCTGCT 140

GGACCTC I I I I I GCAAGTA GAACACAACAAATCCTTCC AAGAACCTTCACCAAG

219147_s_at TATGAA 141 TGAAGT 142 ATACAATGT 143

GTAGGACCTCACTGTTAAG GGACCTCACTGTTAAGGC GGAAGTTACTCTAAAGA

226251_at GCACAA 144 ACAAATT 145 CACTCAGA 146

TCCTGAGCAAGAAACCCC GGAGGTGGATTCTAAGAG GGATTCTAAGAGCCAG

220557_s_at GAGAAAA 147 CCAGGTC 148 GTCATTGAA 149

GACACCTTACTGGTAACCT GGTAACCTGCTATTCTTCT GATGACTTGTCAGTGTT

200627_at GCTATT 150 GGTGTT 151 CCAGGTGT 152

CTGGGAGCGCACACATTT TCCTGGATTGGCTTCGTTA GGCTTCGTTATTTGGTG

236465_at TCTGTAT 153 TTTGGT 154 GCCTGGCA 155

GATTCCTGAAGACTGCTG GCTGCTGTCGCTATAGTC GAGTGTCTGAATGTTTG

1558444_at CTGTCGC 156 GTTGAGA 157 ACCTTCAC 158

AGTTGTCAGTAGCAGTCAC GTGAATTGCCGCTTACTG ACTGGTGTTATAACTTC

1562472_at TTAAAA 159 GTGTTAT 160 TCTGGGTA 161

GCGGGCATTGGGACCAGT TGGGACCAGTGTGGAGCC TCC I I I I I CAGCCTCTG

218725_at GTGGAGC 162 TGAGGGT 163 CATAAGGC 164

CTCACCGATTGGCAGAGC GACGGCGAGCTCTACAAA GCAAGGTGTTGGAGCG

204242_s_at CGACGGC 165 AACCTCT 166 GGCATCCTG 167

GTGCAAACTGATGTGTGAA GAACGGCTAAAACTCAGC GTGGAAAGTGCAGCTA

212428_at CGGCTA 168 ACGTGGA 169 GGAGTCCTG 170

GGCAACATTGAACGTGTTT TATTACAAGGTGAGTCCCA GAGGGGTGGGTGATTT

233060_at CCCCTC 171 GCCTCG 172 CTGAACGTC 173

GTATGCATTTCTCATAATT ATAAGGGTGCACTTCACTG AAGAAATCTCGGGACA

1556221_a_at CACAGA 174 CACATA 175 GGAATCATG 176

ProbeSet Probe 7 Seq ID # Probe 8 Seq ID # Probe 9 Seq ID #

TGAAGCAGAGTCTTCCAG CTGGCCCTTGGCTTTAGC TAAAGAGAGGCCCCGG

210395_x_at GTGCCTG 177 CATACCA 178 CTGGGTGAG 179

AGAGAGAGAATGGGTCCG TGAAATGAGGTTCTGAGTC CAGATGTGCTCATGTC

226949_at CGTGGCC 180 ACTGGC 181 GGTGTCTGG 182

GATCCCTCTGCAGGGTCT AGGCACCTTCTGTCCAACA GACAACGTGGCACTTC

216164_at GGAGACT 183 GGAAGA 184 CATAGACAA 185

CTTCCCTTTATGGTACCCT GTACCCTGGAATGATGGA CACGTTACTCTCTAGAC

231454_at GGAATG 186 GCTGCCC 187 AGTCTCTT 188

GC CAATACTAGATAGC ACT GCACTGGAGCAAACAACG ATAGCTCTGCTCTTATT

1562716_at GCCTGT 189 AGCACAT 190 CCCAATTT 191

GACCTCTTAAGATTGTCCT GATTGTCCTCTTTAACTTA TAGTAATATCCATTTAA

232108_at CTTTAA 192 CTTAGT 193 CGCGCCCC 194

GAACTCAGGTATGCTGAT GATGCTCTGGGATTGTCC GCCACGGCACCCAAGG

215875_at GCTCTGG 195 AGGCCAC 196 GGGATGGGG 197

AAGGTGTCGTGAGAGTCC AAGGGTCTTTGATGGGCA GC ACGTGTTTAC AGC G

223456_s_at CTTTCAT 198 CGTGTTT 199 CTGCTGCAT 200

GACAGCCATTGTTTCATAT GGACTGTACCTTAGCAAGT AGGGAGCGCATAGATA

219147_s_at GTTTGA 201 TGCTCC 202 CAGCAGAGC 203

TCTAAAGACACTCAGATTA GTAAATGCTAAACTTTTGT GCTAAACTTTTGTGTCT

226251_at TTTCAG 204 GTCTTT 205 TTTGCCTT 206

CCAGGTCATTGAAGGCAT TCATTGAAGGCATCAGCC TCAGGAGTGGTTGAGG

220557_s_at CAGCCGC 207 GCCTCAT 208 ACACAGGGC 209

GTGTTCCAGGTGTATCTTA TTACATCTCTTAGCATGGA GCTGTACATTGTTGCTT

200627_at GCTAAA 210 GGACGA 211 GAGAGTCT 212

236465_at GGCCTGGCAACCTGTGGT 213 GGCATTAACTATTCACTAG 214 GAATAGGACAGCAGAC 215 GATAGGA GGCTGG CCCAGAAGC

TGACCTTCACCAAACACCA AACACCAAATATCCTGGCC GGCCCTTTAGTCTTGTA

1558444_at AATATC 216 CTTTAG 217 CTTTCCAG 218

AGAATAATTATATCTTCCC ACCTCACAGGCTTGTTTCA GCTGAACATACGTAAG

1562472_at CGCCCA 219 GAAATC 220 GTGGCATTA 221

TGCATAAGGCCCCTGGGT CTGGGTACACTGCAGAAG CCTGGCCATGTGATCG

218725_at ACACTGC 222 CCCCATC 223 TGTTGGTGA 224

CGTGGACCTCAACTTCTGA CCTGTGCGCCTAAACTGC AACTGCTGATTGGCCT

204242_s_at TTCCAG 225 TGATTGG 226 CAACTTGCC 227

GGAGTCCTGCAATCAATG GAATGCC I I I I I I CAGGGG ACATGCCGATCCTGAG

212428_at AATGCCT 228 TTAATG 229 GCTTTGGCT 230

ATTTCTGAACGTCCCCATG CCATGTGTTCAGGGCGAA GAAGTTCGGTGGCCAG

233060_at TGTTCA 231 GTTCGGT 232 AATGTCAGA 233

CATGGATTTTTGTATTATC TCCTTGTATTTGATGGTTG ATTCCTTTAACAAGTAT

1556221_a_at CTTGTA 234 TTTCCA 235 GTGTCGAG 236

ProbeSet Probe 10 Seq lD # Probe 11 Seq ID #

GAGATGGAGTCCTCGACT TAAACAGCTCTAACACGGC

210395_x_at TATCACC 237 CAGGCT 238

GTATGGTGTGTTTTAAGCT AATATATCCTAC I Ti l IATC

226949_at ATTTGT 239 TTAAG 240

GACTTGAGCTGTCCTTGAC AAAGGGGCAGGGGCATCT

216164_at TTGAGG 241 ACCATAT 242

TTTGTCTTCCTGCAATGGC GGCAGCGCCGAGGTTGTA

231454_at AGCGCC 243 TATTTCT 244

GCTTCAGAACTGTGCTACC AGCCCTGAGTGACCATTG

1562716_at CAGGAG 245 AGCACTA 246

CATTTCTTTTAGTGCTAAG GATTGTCTGAATATACTAC

232108_at TAATAT 247 TACTGA 248

GTGCCAATACCACCCTGT GGGCCTGAGCCTAATGGC

215875_at GATGAAG 249 TGTGCCA 250

GTTGTACCTGTGATGGGG GTCTCACCTTTAATTGTCA

223456_s_at CGTGTGG 251 ACCTCC 252

GTCTCCACTTTCTGAACAT GTCAAACTTTTCTAACTGG

2191 7_s_at AGCTCT 253 AGCTCA 254

GAGGGTATGTACAGAGAA GGTGTCAACACCATTTAAA

226251_at GTTGGTG 255 AAGTCA 256

GCGTCTCCTGGGCCAGAG GAGGGACTGAGTTGATGT

220557_s_at AGAGGAG 257 TGGGTTT 258

I I I I I TGATTCAGCTTATAC TATACCCGGGCTGAAAAC

200627_al CCGGG 259 CTCAATT 260

ACTGCATGGTTGAAATCCT GTTGTCAAGCAATTTGTCC

236465_at TGGTCC 261 ATGTGG 262

AGACTAAGAGAGCATCAC GTCACTTTTAGTGGGTACT

1558444_at CTGCCCT 263 TCATGC 264

TACTTCTATGTTAGGGCTA GGGCTATTTCCCTATAATA

1562472_at TTTCCC 265 TTGAGC 266

GATCGTGTTGGTGACAGA ACAGACCCTGATGTGCTG

218725_at CCCTGAT 267 GTGCTGT 268

ACTTGCCCAGGCGGACGG GCTGGACCTAATCTGGGA

204242_s_at GAGGGAG 269 TCGCGGT 270

GGCTGAAATTCTGCTTGAA AAAATAAGACCTACTCATC

212428_at ACTTGT 271 TGTGAG 272

AGAATGTCAGAGTGGCTTT TCCTATTCTCATTCGTCTC

233060_at TCTCAG 273 GGATGA 274

AGTATGTGTCGAGTGCCTA CTGGGCACTGTAGGTTCA

1556221_a_at CTATGT 275 ATGGTAA 276

Array values

ProbeSet ARK 8226.00 ARP1 Dp6p43 UCLA.1 KHM-1B K S12BM K S28BM KMS28PE K S34

210395_x_at 278.50 183.80 153.80 261.80 114.60 96.80 127.80 137.80 123.10 127.70

226949_at 717.40 911.10 992.10 618.40 572.20 1391.30 562.40 891.40 596.90 805.30

216164_at 271.90 123.40 18.30 86.00 175.90 198.00 184.20 241.90 143.40 383.80

231454_at 29.80 166.50 261.00 44.30 77.80 87.10 63.30 10.90 101.50 131.50

1562716_at 192.00 152.80 200.00 29.60 132.90 106.30 145.90 20.00 83.20 90.90

232108_at 174.00 87.50 96.90 240.80 100.40 43.70 104.90 141.00 103.70 50.80

215875_at 114.20 63.00 100.50 68.30 108.10 151.20 78.10 155.90 121.60 156.50

223456_s_at 261.40 340.60 67.70 63.60 198.00 648.20 102.70 253.20 239.90 146.20

219147_s_at 1935.60 1226.80 2021.20 4998.80 870.30 543.20 1137.40 1182.20 1877.90 3874.80

226251_at 765.10 538.00 912.10 654.90 556.80 525.00 757.90 556.60 683.70 786.70

220557_s_at 51.10 92.40 104.20 134.70 194.40 267.00 107.60 94.90 66.70 108.00

200627_at 16010.00 20722.20 17077.50 16454.90 16003.10 10912.60 22250.30 14018.50 16035.30 19973.60

236465_at 132.00 223.90 142.30 84.60 97.80 228.20 100.00 54.30 88.40 81.60

1558444_at 233.40 681 80 26500 600.30 583.30 1068.30 1345.90 190.70 267.00 258.30

1562472_at 89.50 23.50 176 80 99 80 33.60 92.90 61.30 82.80 40.90 59.00

218725_at 756.60 1060.30 1061 90 1044 80 394 50 846.40 771.80 484.60 720.50 679.50

204242_s_at 121.40 23 00 29 90 65 10 417 70 90.80 141.30 164.30 165.40 43.60

212428_at 3149 80 1567.70 2219.20 3328 70 1757 20 2713 80 2442.50 1823.00 2405.10 3094.40

233060_at 216 50 369 40 318.80 275.60 99.00 327 70 188.60 179.10 74.50 144.80

1556221_a_at 159.30 16060 14840 49.40 89.70 175.30 59.40 935.70 171.90 79.00

ProbeSet Kas6-p11p2 OCI_MY7 SACHI XG2 Delta47 EJM FLA 76 FR4 H1112 H929

210395_x_at 124.30 89.30 132.70 136.80 153.00 344.70 308.60 134.90 166.10 197.90

226949_at 602.20 917.50 908.60 707.00 312.10 496.90 1111.90 788.30 691.60 815.00

216164_at 224.50 81.30 348.20 129.50 277.90 251.20 15.30 284.40 345.20 42.40

231454_at 122.80 141.50 115.20 136.20 28.20 21.20 120.20 73.40 152.40 182.40

1562716_at 24.80 145.70 25.00 97.10 144.90 102.00 183.50 87.40 73.20 158.70

232108_at 201.90 71.40 24.70 70.50 30.10 267.40 182.00 191.40 97.20 91.80

215875_at 101.00 33.40 127.40 57.30 54.40 142.70 19.00 104.90 62.70 83.30

223456_s_at 191.60 370.80 219.50 298.10 266.70 128.10 58.80 82.40 187.10 193.00

2191 7_s_at 4471.30 580.50 2987.90 913.30 1700.70 4215.50 3747.50 2205.50 693.20 1311.40 226251_at 551.10 388.80 250.30 479.60 328.70 783.60 977.20 924.40 379.10 518.50

220557_s_at 46.40 57.60 310.80 58.70 88.80 220.10 75.10 146.60 241.40 85.00

200627_at 20315.40 17835.10 19317.80 12554.60 28670.60 13558.50 10487.00 19796.20 14174.30 20171.60

236465_at 61.30 136.40 79.20 2940 193.30 100.90 45.80 61.70 111.90 183.60

1558444_at 613.70 574.60 338.40 673.80 492.00 457.00 862.10 1117.20 860.90 876.90

1562472_at 54.70 33.70 41.00 27.60 59.30 42.60 71.20 60.00 117.80 15.20

218725_at 545.60 905.40 944.20 981.50 831.10 885.30 993.80 456.80 1134.00 998.40

204242_s_at 66.90 20.60 153.20 21.80 63.90 131.50 80.00 120.40 240.70 29.20

212428_at 3242.70 1781.20 2343.50 2341.80 1651.20 3002.20 2365.40 3013.00 3597.30 1743.30

233060_at 192.80 228.10 150.90 77.50 66.80 55.70 271.00 115.70 100.10 314.60

1556221_a_at 102.20 177.60 134.70 201.50 179.40 62.10 156.10 3.80 8.80 142.80

ProbeSet INA6 JIM3 JJN3 KARPAS620 KHM11 KMM1 KMS11 KMS12PE K S18 L363

210395_x_at 211.30 254.40 224.10 191.60 183.70 83.80 180.10 192.70 327.20 201.80

226949_at 739.20 653.90 518.80 732.00 900.90 728.90 819.80 886.60 709.60 829.50

216164_at 51.20 33.80 132.30 103.80 88.80 68.10 20.50 193.70 25.00 28.60

231 54_at 68.60 73.80 13.80 57.20 45.60 124.00 157.80 23.20 213.80 364.50

1562716_at 59.20 106.40 63.20 159.10 234.70 193.80 193.70 164.80 39.10 142.90

232108_at 31 60 184.70 251.70 179.30 12 30 121.40 85.20 180 30 166.40 109.70

215875_at 103.20 95.40 117.10 99.70 94.80 20.30 25.50 100.40 64.80 177.50

223456_s_at 3590 223.50 39.70 35.60 274.60 209.60 308.60 190.20 163.40 341.00

219147_s_at 3781.10 797.90 3125.40 782.20 620.40 1049.70 2150.00 1520.50 563.30 768.90

226251_at 522.10 640.60 1110.30 703.20 684.90 386.70 505.70 1463.30 329.60 558.70

220557_s_at 50.50 232.60 181.00 89.60 10.00 33.50 58.60 147.20 386.70 227.20

200627_at 19162.30 14084.60 13967.70 13570.30 21369.00 21237.90 17710.70 15514.70 17924.80 13132.90

236465_at 45.90 200.20 53.70 124.90 236.70 174.10 186.80 60.30 48.70 393.90

1558444_at 168.40 809.00 246.60 359.80 780.90 934.00 767.10 1451.20 354.30 632.80

1562472_at 29.50 41.70 53.40 65.00 22.60 57.80 27.60 14.00 94.40 119.40

218725_at 465.20 1064.90 784.40 299.10 971.10 1027.40 798.80 433.80 1224.20 793.20

204242_s_at 31.10 175.50 162.80 76.80 29.80 112.60 53.80 197.10 66.50 265.10

212428_at 2291.90 1773.80 2682.30 1006.90 1509.80 2128.40 2523.00 5369.70 2462.70 1376.60

233060_at 174.90 179.20 41.50 148.10 206.10 225.40 280.20 85.10 393.20 747.30

1556221_a_at 222 80 194.20 67.80 67.70 227.50 146.90 237.20 74.50 154.40 261.00

ProbeSet LP1 MM_M1 MM1J144 OCI_ Y5 OPM1 OPM2 PE SK M1 SKMM2 UTMC2

210395_x_at 214.90 181 70 322 10 376.70 191 20 95.70 203.80 221.70 199.50 225.00

226949_at 680 00 609 10 483 50 613 40 310.40 802 10 896.90 721.90 65940 790.60

216164_al 205 40 151 40 338.00 137 80 23.00 28 10 33.20 109.60 36.40 158.30

231 54_al 94.80 19.80 25.50 56.90 30 90 138 30 47.60 18.20 26.10 13.40

1562716_at 87.00 122.60 108.10 115.10 105.50 231.70 109.80 94.80 165.10 60.50

232108_at 75.10 133.40 155.20 198.90 19.10 54.70 92.90 223.80 91.20 10.00

215875_at 147.00 161.60 144.70 105.10 12.80 68.30 74.80 65.50 40.80 75.50

223456_s_at 119.60 145.40 45.40 86.60 295.10 318.70 208.90 145.10 64.30 265.00

219147_s_at 2705.20 1115.60 4565.30 1 79.10 1352.10 829.70 7204.00 1945.10 1725.10 3567.00

226251_at 536.90 604.50 872.40 754.60 480.40 542.40 367.80 723.50 762.80 526.00

220557_s_at 175.30 54.60 153.40 181.30 14.00 51.60 83.40 205.00 84.00 35.10

200627_at 19948.70 14444.70 14467.20 14245.30 24442.90 18860.60 13721.00 12856.50 17428.40 18 08.40

236465_at 73.50 141.20 34.20 92.60 220.90 187.20 94.40 32.30 167.60 19.70

1558444_at 573.90 1065.20 527.30 667.90 1044.50 785.30 1075.80 309.70 552.40 209.70

1562472_at 73.10 21.80 62.40 84.20 107.10 16.90 112.90 46.30 53.60 6.00

218725_at 788.00 271.00 532.00 803.30 1091.30 1040.70 756.20 608.00 333.40 889.60

204242_s_at 167.10 154.80 196.10 149.50 205.30 75.50 151.90 238.90 117.20 126.90

212428_at 2572.50 3134 30 3019.30 2021.80 3488.20 1945.00 3088.90 2785.00 3301.20 3160.60

233060_at 78.10 9960 44.30 154.80 232 20 353.90 79.40 149.50 75.50 100.40

1556221_a_at 40 50 104 20 7060 163.60 352.90 187.00 121.80 75 30 158.50 114.80

ProbeSet XG1 XG7 ANBL6 CAG U266 KMS26 OCI_ Y1 PE2 J 6L XG6

210395_x_at 133.50 310.40 246.90 72.00 361.30 150.50 97.10 118.60 109.20 150.30

226949_at 1071.60 895.50 568.10 485.80 363.60 791.90 569.40 749.00 648.50 693.40

216164_at 44.80 19.40 155.50 17.10 165.60 106.50 166.70 11.20 144.70 333.70

231454_at 169.90 25.80 72.00 24.50 25.00 27.50 43.20 23.70 17.80 11.00

1562716_at 76.10 198.70 74.70 19.60 108.10 40.90 55.70 253.10 43.40 91.80

232108_at 172.90 120.90 182.30 96.60 274.70 29.10 119.50 48.80 28.10 125.80

215875_at 28.10 36.70 121.60 49.50 151.20 9.70 87.30 11.20 101.50 118.30

223456_s_at 141.20 210.00 147.60 452.30 174.70 132.50 320.40 316.70 195.50 138.20

219147_s_al 1209.20 2292.60 4826.20 2134.00 335920 2879.20 522.30 953.20 1111.30 4233.50

226251_at 541.90 624.70 909.60 684.50 758.60 721.70 883.30 561.30 241.60 856.10

220557_s_at 99.60 102.40 261.40 278.30 213.20 168.30 148.70 85.60 190.30 177.80

200627_at 17421.20 16100.10 13252.40 14900.80 16065.80 18625.90 13012.50 10795.70 13565.90 10921.30

236465_at 217.80 340.20 140.00 4.70 164.00 63.80 64.70 83.30 85.20 122.90

1558444_at 439.00 380.30 613.50 499.60 345 30 530.80 558.70 1099.40 262.70 1115.70

1562472_at 223.60 124 70 89.60 27.00 50 80 44.80 41.50 26.50 55.20 62.90

218725_at 1197.60 1206.50 806 40 778 30 505 60 353.20 1177.30 320.10 596.20 745.50

204242_s_at 62 10 58.90 209.40 209.10 47 90 84.80 75.50 150.00 113.80 19.20

212428_at 2002 70 2581 30 3043.90 1830.40 2484,30 2003.90 1300.90 1641.60 2322.40 2650.70

233060_at 335.80 428 50 49.30 75.30 225.80 55.50 44.40 41.90 121.90 99.60

1556221_a_at 95.40 132.30 20.70 62.60 115.90 95.90 256.10 127.00 89.70 104.10 Signature #3: 5-Gene Signature

[00201] The third signature was derived in a much simpler way than the first two. The following four quantities were calculated for every gene:

Sensitive_minimum minimum intensity in any sensitive cell-line Sensitive_maXimum maximum intensity in any sensitive cell-line

Resistantminimum minimum intensity in any resistant cell-line

Resistantmaximum maximum intensity in any resistant cell-line

The signature included every gene for which either:

a) Sensitive_minimum > Resistantmaximum

b) Resistantminimum > Sensitivemaximum

[00202] Condition a) identifies those genes whose mRNA levels were higher in every sensitive cell-line than they were in any resistant cell-line. Condition b) identifies those genes whose mRNA levels were higher in every resistant cell-line than in any sensitive one.

[00203] Then, any genes lacking sufficient evidence of mRNA levels above background (arbitrarily set to 500, as is common in the literature) in either sensitive or resistant lines were excluded. This reduced the 17 initial genes to 5 genes. These genes were then incorporated into a 10,000 tree Random Forest with OOB and variable-importance ranking as before. Because the set of genes selected by this technique is sample-invariant in the cell-lines dimension, LOOCV cannot be used with this feature-selection technique. This approach is the small- sample analog to the C-lndex approach described earlier ⁵⁸. The bolded gene is common with the 20-gene signature.

Prediction Accuracy (OOB): 87.5%

Table 6

Entrez

Rank ProbeSet Gene Symbol Name

ID

1 226949 at 2802 GOLGA3 golgi autoantigen, golgin subfamily a, 3

SP100 nuclear

202863_at SP100

antigen

226397_s_at N/A N/A

200829_x_at ZNF207 zinc finger protein 207

SP100 nuclear

202864 s at SP100

antigen

Figure 12 is a heatmap demonstrating the relevant abundance of the 5 genes in sensitive and insensitive cell lines.

Individual genes can are also univariate predictors. Table 6 continued Univariate analysis

ProbeSet M CV

226949_at 8.73E-05 -0.553758053 24.49672132

202863_at 0.001048294 1.442220094 60.25409459

226397_s_at 0.010599314 2.47230926 107.0664715

200829_x_at 0.000751379 -0.479931856 23.16030558

202864 s at 0.004154745 0.904862263 45.22854517

Probe sequences

ProbeSet Probe 1 Seq ID # Probe 2 Seq ID # Probe 3 Seq ID #

GACAGGCAGGCACCGTGA CTCTCCCAGGTGTCTCAG ATGTGTGAGATCTTCTG

226949_at GGAAGGT 277 AGACAGA 278 TTTCCTAC 279

GCCAAGACTTGGCCTGCA GTGAATTAAAAGCTGCTGT CCAGACGCTTTTTATTC

202863_at GAATGTC 280 TTCCAG 281 TGAGCACC 282

CCATCAGCCAACTAGCTTT TATATTCATCTCTAAAGGC AAGGCCCTCAAAGCAC

226397_s_at TTTTAA 283 CCTCAA 284 TGTAAAACT 285

ATTATCTTCCCACATACCA TGGCTTCTTTTTCATGTTT AAAGTTTGCCTTCACAG

200829_x_at GGAACT 286 CATCTA 287 CATTTCAG 288

GCGGGGCACTGAGAAGCA GCAAGCATGGTGAGAAGG GCATGGTGAGAAGGCT

202864_s_at AGCATGG 289 CTCCTAT 290 CCTATGACT 291

ProbeSet Probe 4 Seq ID * Probe 5 Seq ID # Probe 6 Seq ID #

GCAGACACTGGCTGACTG GTGGGGTCTGTAAGTTGT TAAGTTGTCCCCTAGTT

226949_at GGAGAGG 292 CCCCTAG 293 TGCTAAGA 294

TTCACTACCTTGTATCCAG GTATCCAGTTCATCTGGGA CTGGGAACTCCTTTTTG

202863_at TTCATC 295 ACTCCT 296 CATTTTAG 297

ATTTTTCTCCCGCTTATGA AATGGAATTTCAGGCTCTC CCTGTGCACAGCCGGT

226397_s_al ACATGT 298 CCTGTG 299 GGGCAAAGG 300

GATGCTGTTGGACTTCATG GTCCCCAACCTAGCTTGG GAGGGCTGTAACTGTT

200829_x_at TCCCCA 301 TGAGGGC 302 TCCAAGTAC 303

AAGGCTCCTATGACTTCTA AGCAGGAAGAGACGTTTC GAGACGTTTCAGCAGT

202864_s_at GAAGTA 304 AGCAGTA 305 AGTGACTTT 306

ProbeSet Probe 7 Seq ID # Probe 8 Seq ID # Probe 9 Seq ID #

AGAGAGAGAATGGGTCCG TGAAATGAGGTTCTGAGTC CAGATGTGCTCATGTC

226949_at CGTGGCC 307 ACTGGC 308 GGTGTCTGG 309

GAGTTTCACTGACTAAATG I I I I I TAAACCTGCTCTCAT CTGCTCTCATTCCTATT

202863_at TATGTA 310 TCCTA 311 AACACTAA 312

CTGTCTGTTAATCCCCAGA AGACCGGTTGCATTTTCCA GGGTGTCTGTACATAG

226397_s_al CCGGTT 313 GTTGCT 314 TTTGTCTTT 315

GAGTTCCTCATGTTGCAG GGTCATGTGACTTTTCTGT ATGCC I I I I lATTCATA

200829_x_at GGTTTAA 316 ACTGTT 317 ACCCAGCT 318

AGTGACTTTTC AGAC CTGA AATGGAGAAGAGCTTCAG ACCTGCAGCTCATCCC

202864_s_at GTAATG 319 GAAACCT 320 TAAGAAGAG 321

ProbeSet Probe 10 Seq ID # Probe 11 Seq ID #

226949_at GTATGGTGTGTTTTAAGCT 322 AATATATCCTAC I I I I IATC 323 ATTTGT TTAAG

GACATCATTGTCGTTTGTA GCCTCAAAAGACAACTGTT

202863. at ATTGTA 324 CCTACT 325

TAAACTGGATCTCTGTGGC GTGGCCTAGGTTTTGTACA

226397. _s_ at CTAGGT 326 TACAGA 327

AGCTGTGGACCACTGCCT GATGCATGCCACAGTAGA

200829. x_ at GAAAGGT 328 TGTCCAC 329

GCAGCTCATCCCTAAGAA GCTCATCCCTAAGAAGAG

202864. _s_ at GAGGGTC 330 GGTCAGG 331

Array values

ProbeSet ARK 8226.00 ARP1 Dp6p43 UCLA.1 KHM-1B KMS12BM KMS28BM KMS28PE KMS34

202863_al 1112.50 754 00 531.60 1200.20 3988.30 1535.70 1070.20 835.60 1495.80 1081.10

226397_s_at 456.30 248.90 72.60 1581.10 4024.80 1647.70 410.50 137.00 161.60 170.40

200829_x_at 4822.60 5508.30 7620.00 4025.40 5408.50 4109.30 4622.60 3969.60 4043.60 4978.50

202864_s_at 735.50 638.00 429.30 817.40 2797.60 1107.50 624.40 574.70 899.90 805.50

ProbeSet Kas6-p11p2 OCI_MY7 SACHI XG2 Delta47 EJ FLA 76 FR4 H1112 H929

202863_at 1355.50 356.30 711.50 1418.40 264.80 2547.60 1489.40 2064.20 479.90 822.30

226397_s_at 1281.20 350.30 1527.00 694.00 168.80 2718.50 3098.90 138.50 298.60 167.40

200829_x_at 4159.90 5574.80 5248.10 6115.20 5630.50 5052.40 3744.30 6911.50 5712.70 5801.30

202864_s_at 1049.20 350.70 606.10 581.60 431.20 844.10 1042.50 1405.30 423.40 601.30

ProbeSet INA6 JIM3 JJN3 KARPAS620 KH 11 KMM1 K S11 K S12PE K S18 L363

226949_al 739.20 653.90 518.80 732.00 900.90 728.90 819.80 886.60 709.60 829.50

202863_at 1661.10 765.00 1277.70 843.40 457.30 71 .40 519.90 4879.00 1425.60 957.00

226397_s_at 160.70 343.20 677.80 2085.10 180.60 333.20 201.10 698.90 3918.60 87.30

200829_x_at 7333.80 5107.30 3512.00 470580 6387.30 6626.80 7321.80 5610.00 7383.90 5019.70

202864_s_at 923 90 539.30 655.50 484.90 456.80 608.00 417.80 3761.40 801.50 787.40

ProbeSet LP1 _M1 M 1_144 OCI_ Y5 OPM1 OPM2 PE SKM 1 SKMM2 UTMC2

226949_at 680.00 609.10 483.50 613.40 310.40 802.10 896.90 721.90 659.40 790.60

202863_a( 208940 897.30 2064.00 1147.60 1915.00 585.30 749.40 610.60 2706.30 1065.80

226397_s_at 685.00 273.70 2363.90 713.70 75.70 165.00 528.50 10.80 184.70 147.90

200829_x_at 5371.10 5737.40 4274.00 4011.60 5210.50 5376.40 5338.30 5594.70 4160.90 4554.50

202864_s_at 1410.70 833.70 1316.80 840.50 1261.20 535.80 542.30 460.50 1545.30 671.00

ProbeSet XG1 XG7 ANBL6 CAG U266 KMS26 OCI_MY1 PE2 JK6L XG6

202863_at 964.70 1181.50 1642.40 649.30 2204.80 705.40 1441.60 754.80 776.30 1207.60

226397_s_at 418.90 157.10 1770.70 234.80 2250.80 113.40 213.70 122.30 780.80 581.00

200829_x_at 7461.80 6397.70 3201.30 6349.10 3429.00 5623.00 4588.90 7198.00 3733.40 4999.80

202864_s_at 750.60 851.10 1028.30 745.60 1394.70 740.90 1273.10 505.90 853.50 1071.60

Signature Comparison

[00204] The signatures have partial overlap (see Venn diagram, Figure 13), but each contains unique genes. The 50 uncharacterized cell-lines were predicted separately be each of the three signatures. There were:

24 cell-lines predicted resistant by all 3 signatures

10 cell-lines predicted sensitive by all 3 signatures

The results are summarized in Table 7. Table 7

4- 20- 5-

Cell. Line Gene Gene Gene Counts

1 ARK R R R 0

2 Dp6p43 R R R 0

3 UCLA.1 R R R 0

4 KMS28PE R R R 0

5 Kas6.p11 p2 R R R 0

6 EJM R R R 0

7 JIM3 R R R 0

8 JJN3 R R R 0

9 KMS12PE R R R 0

10 LP1 R R R 0

11 MM M1 R R R 0

12 MM1 144 R R R 0

13 OCI_MY5 R R R 0

14 SKMM1 R R R 0

15 SKMM2 R R R 0

16 ANBL6 R R R 0

17 CAG R R R 0

18 U266 R R R 0

19 JK6L R R R 0

20 XG6 R R R 0

21 KMS12BM R R R 0

22 KARPAS620 R R R 0

23 KMS18 R R R 0

24 OCI_MY1 R R R 0

25 H1112 R R S 1

26 KMS28BM R R S 1

27 KMS34 S R R 1

28 FR4 R R S 1

29 SAC HI R R S 1

30 PE R R S 1

31 UTMC2 R R S 1

32 KMS26 R R S 1

33 FLAM76 S S R 2

34 INA6 S R S 2

35 XG2 S S R 2

36 Delta47 S S R 2

37 L363 R S S 2

38 KHM.1 B S S R 2

39 OPM1 S S R 2

40 PE2 R S S 2

41 XG1 S S S 3

42 X8226 S S S 3

43 ARP1 S S S 3

44 OCI MY7 S S S 3

45 H929 S S S 3

46 KHM1 1 S S S 3

47 KMM1 S S S 3

48 KMS1 1 S S S 3

49 OPM2 S S S 3

50 XG7 S S S 3 EXAMPLE 3

[00205] Cells known to be sensitive or not sensitive to a treatment that depletes mevalonate, for example the cells described in Example 1 , are exposed to a siRNA molecule that targets HMGCR. Expression levels and/or activity levels of genes for example one or more genes listed in Tables 3-6 are assessed and correlated with the known sensitivity or insensitivity to treatment.

Genes that are differentially expressed or have differential activity in the two populations are used to assess whether subjects are likely to respond to a treatment that depletes mevalonate such as a HMGCR inhibitor treatment. EXAMPLE 4

[00206] In a clinical setting, cells of unknown sensitivity are derived from a patient sample. RNA is extracted from the cells and reverse transcribed into cDNA and transcript levels are analyzed, for example by realtime PCR or mRNA microarray. In an alternative or an additional step, the cells are exposed to a statin or to a siRNA or shRNA corresponding to HMGCR prior to RNA extraction. The results from the transcript level analysis are compared to the data in Tables 3- 6 and Figure 4 to determine whether the transcript levels of the unknown sample correspond to the transcript levels of known sensitive or known insensitive cell lines.

[00207] While the present disclosure has been described with reference to what are presently considered to be the preferred examples, it is to be understood that the disclosure is not limited to the disclosed examples. To the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. [00208] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. Nucleic acid probes referred to by ID number are specifically incorporated by reference and genes referred to by Gene ID number, for example, genes listed in Figure 4, Tables 3, 4, 5 and 6, are specifically incorporated herein by reference. FULL CITATIONS FOR REFERENCES REFERRED TO IN THE SPECIFICATION 1. Sassano A, Platanias LC. Statins in tumor suppression. Cancer Lett. 2008;260: 11-9.

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Claims

Claims:

1. A method of determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to a treatment that depletes levels of mevalonate comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to the treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to the treatment.

2. The method of claim 1 wherein the treatment that depletes mevalonate is an HMGCR inhibitor such as a statin.

3. The method of claim 1 for determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to a HMGCR inhibitor treatment comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to the treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to the treatment.

4. The method of claim 2 or 3 wherein the HMGCR inhibitor is an antisense molecule, optionally a siRNA molecule, that targets HMGCR or HMGCS1. 5. The method of claim 2 or 3, wherein the HMGCR inhibitor is a statin.

6. The method of claim 1 for determining whether a cancer cell and/or cancer from a subject is likely to be sensitive to a statin treatment comprising: determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway, wherein dysregulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely sensitive to the treatment and regulation of the mevalonate pathway is indicative that the cancer cell and/or cancer is likely insensitive to the treatment.

7. The method of claim 5 or 6, wherein the statin is comprises a moiety of formula la or formula 1b.

8. The method of claim 7, wherein the statin is selected from a statin iin the form of a neutral compound or as pharmaceutically acceptable salt, in the form of a solvate or prodrug thereof, a mixture of two or more statins, or pharmaceutically acceptable salts, solvates or prodrugs thereof. 9. The method of claim 8, wherein the statin is selected from lovastatin, simvastatin, atorvastatin, fluvastatin, rosuvastatin, pravastatin, cerivastatin or pitavastatin, or a pharmaceutically acceptable salt, solvate or prodrugs thereof, or a mixture thereof.

10. The method of any one of claims 1 to 9, wherein the step of determining whether the cancer cell and/or cancer has a dysregulated mevalonate pathway comprises determining a level of gene expression, level of polypeptide activity or gene copy number of one or more genes, selected from the genes included in Figure 4 and/or Tables 3-6, in a sample from the subject; and comparing the level or gene copy number to a control, wherein an altered level of gene expression, polypeptide activity or gene copy number in the sample of at least one of the one or more genes compared to the control is indicative the cancer cell and/or cancer has a dysregulated mevalonate pathway.

11.The method of claim 10, wherein the altered level is an increased level.

12. The method of claim 10, wherein the altered level is a decreased level. 13. The method of claim 10, wherein the control is a positive control.

14. The method of claim 10 wherein the control is a negative control.

15. The method of claim 10, wherein the control is a numerical value indicative of the level or gene copy number in a sample corresponding to a regulated mevalonate pathway. 16. The method of any one of claims 1 to 15, wherein the step of determining whether the cancer cell or cancer has a dysregulated mevalonate pathway comprises determining a level of gene expression, a level of polypeptide activity or gene copy number of one or more genes selected from the genes included in Figure 4 and/or Tables 3-6 in a sample from the subject; and comparing the level to a control, wherein a decreased level of gene expression activity or gene copy number in the sample of at least one of the one or more genes compared to the control is indicative is likely sensitive to statin treatment and wherein a level of gene expression, activity or gene copy number of at least one of the one or more genes comparable to or greater than the control is indicative the cancer cell is likely insensitive to the treatment that depletes mevalonate for example statin treatment.

17. The method of claim 16 wherein the step of determining whether the cancer cell or cancer has a dysregulated mevalonate pathway comprises determining a level of gene expression of one or more, and optionally one, gene selected from Tables 4, 5 or 6. 8. The method of any one of claims 2 to 17, wherein the one or more genes comprises HMGCS1.

19. The method of any one of claims 2 to 17, wherein the one or more genes comprises any isoform or variant of HMGCR, such as HMGCR-FL or HMGCR-

D13.

20. The method of any one of claims 2 to 17, wherein the one or more genes includes and/or is HMGCS1.

21.The method of any one of claims 2 to 17, wherein the one or more genes includes and/or is HMGCR, for example HMGCR-FL and/or HMGCR-D13 and/or any other RNA product of the HMGCR gene locus.

22. The method of claim 1 , wherein the step of determining whether the cancer cell has a dysregulated mevalonate pathway comprises determining a profile such as an expression profile by measuring the gene expression levels of a plurality of genes selected from genes included in Figure 4 and/or Tables 3-6 in a sample of a subject; and classifying the cancer cell as likely sensitive or likely insensitive to statin treatment based on the expression profile.

23. The method of claim 22, wherein the determining step comprises comparing optionally on a computer a profile such as an expression profile of a sample of a subject, the expression profile comprising measurements of expression levels of a plurality of genes, to one or more reference profiles comprising measurements of expression levels of the plurality of genes associated with statin treatment sensitivity or statin treatment insensitivity, the plurality of genes selected from genes included in Figure 4 and/or Tables 3-6; and classifying, optionally on a computer the cancer cell as sensitive to statin treatment or insensitive to statin treatment according to the similarity of the profile such as the expression profile to one of the reference profiles.

24. The method of any one of claims 2 to 23 wherein the altered level or activity, for example an increase or decrease is at least 2 fold, at least 3 fold, or at least 4 fold.

25. The method of any one of claims 1 to 24 wherein the cancer is selected from breast, prostate, colon, lung, liver, brain, or hematological cancer such as MM, AML, CML, or lymphoma. 26. The method of claim 25 wherein the cancer is a hematological cancer.

27. The method of claim 26 wherein the hematological cancer is MM.

28. A method according to claim 1 for determining whether a cancer cell, for example, from a subject, is likely to be sensitive to a treatment that depletes mevalonate such as a statin treatment comprising determining a level of gene expression or activity or gene copy number of one or more genes, selected from the genes in Tables 4, 5 and/or 6, in a sample from the subject; and comparing the level to a control, wherein an increased or decreased level of gene expression activity or gene copy number in the sample of at least one of the one or more genes compared to the control is indicative of whether the cancer cell is likely insensitive or sensitive to the treatment for example statin treatment.

29. A method according to claim 1 for determining whether a cancer cell is likely to be sensitive to a treatment that depletes mevalonate such as a statin treatment comprising determining a profile such as an expression profile by measuring the gene expression levels or activity or gene copy number of a plurality of genes selected from genes listed in Tables 4, 5, and/or 6; comparing the profile to a reference profile, for example a reference profile of a cell sensitive to a treatment that depletes mevalonate such as a statin treatment, and/or a reference profile of a cell insensitive to such treatment for example statin treatment, and classifying the cancer cell as likely sensitive or likely insensitive to statin treatment based on similarity of the profile to the reference profile.

30. The method of claim 28 or 29, wherein the genes comprise and/or are the genes listed in Table 4.

31. The method of claim 28 or 29, wherein the genes comprise and/or are the genes listed in Table 5. 32. The method of claim 28 or 29, wherein the genes comprise and/or are the genes listed in Table 6.

33. The method of claim 29, wherein the determining step comprises comparing, optionally on a computer an expression profile of a sample of a subject, the expression profile comprising measurements of expression levels of a plurality of genes, to one or more reference profiles comprising measurements of expression levels of the plurality of genes associated with statin treatment sensitivity or statin treatment insensitivity, the plurality of genes selected from genes listed in Tables 3, 4, 5 and/or 6; and classifying, optionally on the computer, the cancer cell as sensitive to statin treatment or insensitive to statin treatment according to the similarity of the expression profile to one of the reference profiles.

34. The method of any one of claims 1 to 33, wherein the level is a nucleic acid level, for example corresponding to a mRNA level or a gene copy number.

35. The method of any one of claims 1 to 33, wherein the level is a polypeptide level or polypeptide activity. 36. The method of any one of claims 28 to 33, wherein the cancer is selected from breast, prostate, colon, lung, liver, brain, or hematological cancer such as MM, AML, CML, or lymphoma.

37. The method of claim 36, wherein the hematological cancer is multiple myeloma.

38. A method of treating a subject with cancer or reducing tumor burden in the subject comprising: identifying a subject with a cancer sensitive to a treatment that depletes mevalonate for example a statin treatment according to a method of any one of claims 1 to 37; and administering the treatment such as a statin or a composition comprising a statin to the subject.

39. The method of any one of claims 1 to 38 wherein the level of gene expression is determined by assaying nucleic acid expression products, for example mRNA, using for example an ASR, such as one or more probes, one or more probe sets or one or more primers. 40. The method of claim 39, wherein the gene expression level is determined using a microarray chip and/or PCR, optionally multiplex PCR, or altervatively direct sequencing of RNA, direct sequencing of enzymatically altered/converted/selected RNA (i.e. cDNA), direct quantitation of RNA(e.g. Nanostring). 41. The method of any one of claims 1 to 33 wherein the level of gene expression is determined by assaying polypeptide expression products, using for example an ASR.

42. The method of claim 41 , wherein the gene expression level is determined using an immunoassay such as flow cytometry, Western blot, ELISA, and immunoprecipitation followed by SDS-PAGE, immunocytochemistry or immunohistochemistry.

43. The method of any one of claims 1 to 38, wherein the polypeptide activity is determined by enzyme assay and/or the copy number is determined by fluorescence in situ hybridization, quantitative real-time PCR, comparative genomic hybridization or chromosomal microarray analysis, full or partial genome sequencing, etc.

44. A composition comprising two or more analyte specific reagents (ASR) for detecting a gene expression product of one or more genes listed in Tables 3-6.

45. The composition of claim 44, wherein the two ore more ASRs comprise a set of at least two probes or at least two primers for determining the expression (e.g. mRNA levels) of one or more genes listed for example in Figure 4, and/or in Tables 3, 4, 5 and/or 6. 46. An array comprising for each gene in a plurality of genes, the plurality of genes comprising at least 2 of the genes listed in Figure 4, Table 3, 4, 5 and/or 6, one or more nucleic acid probes complementary and hybridizable to a coding sequence in the gene.

47. A kit for determining statin sensitivity of a cancer cell and/or for treating a statin sensitive cancer comprising a composition of claim 44 and/or an array of claim 45, and optionally one or more specimen collectors, and/or RNA preservation solution and/or one or more statins for treating a statin sensitive cancer.