WO2011068546A2 - Prognosis, diagnosis and identification of multiple myeloma based on global gene expression profiling - Google Patents

Abstract

The present invention provides a diagnostic or prognostic indicator of multiple myeloma that comprises a global expression profile of total miRNA in the subject with a pattern of 39 up-regulated and 1 down-regulated gene as listed in Table 1. Also provided are methods of diagnosing multiple myleoma in a subject and determining prognosis of a subject with multiple myeloma by demonstrating the subject shows the expression profile of the diagnostic or prognostic indicator and further calculating a risk score and/or a proliferation level based on a group of 70 genes and 11 genes, respectively, such that high scores and levels concomitant with the miRNA expression profile correlates to a diagnosis and/or a poor prognosis. In addition, methods of treating multiple myeloma with a therapeutic inhibitor of an miRNA maturation pathway in the subject and for screening for therapeutic inhibitors that inhibit AGO2 and/or DICER1 genes are provided.

Description

PROGNOSIS, DIAGNOSIS AND IDENTIFICATION OF MULTIPLE MYELOMA BASED ON GLOBAL GENE EXPRESSION PROFILING

Cross-Reference to Related Applications

This international application claims benefit of priority under 35 U.S.C.

§1 19(e) of provisional application U.S. Serial No. 61/283,521 , filed December 4, 2009, now abandoned, the entirety of which is hereby incorporated by reference.

Federal Funding Legend

This invention was produced in part using funds obtained through grant CA55819 from the National Cancer Institute. Consequently, the federal government has certain rights in this invention.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates generally to the field of oncology and therapies for multiple myeloma. More specifically, the present invention provides a method for diagnosing, determining the prognosis of and treatments for subjects having or suspected of having multiple myeloma.

Description of the Related Art

MicroRNAs (miRNAs) belong to a class of noncoding small RNAs with mature sequences that contain approximately 22 nucleotides (1 ). As repressors of gene expression, miRNAs can bind to the 3' untranslated region (3'UTR) of an mRNA and inhibit its translation or induce its degradation (1). Dysregulation of miRNA is involved in cancer initiation and progression (2, 3), and miRNA expression profiles have prognostic implications (4-6). Inhibiting miRNA proved effective in vivo (7) and therefore could be a novel therapeutic strategy for cancer (8, 9).

To date, few studies have investigated the roles of miRNA in multiple myeloma (MM), a plasma cell dyscrasia that homes to and expands in the bone marrow and produces disease manifestations that include osteolytic bone destruction with hypercalcemia, anemia, immunosuppression, and end-organ damage (10). Several miRNAs have been implicated in survival and growth of myeloma cells and myeloma tumor growth. For instance, miR-21 is a target of Stat3 and thus a critical component in IL- 6/Stat3-dependent survival and growth pathways of myeloma cells (11). In addition, IL-6 inhibitor SOCS1 and p53 pathway component p300-CBP-associated factor are targets of multiple miRNAs, including miR-106b-25 cluster, miR-32, miR-181a/b, and miR-19a/b (12); suppression of these miRNAs inhibited myeloma tumor growth in nude mice (12).

Applying miRNA expression profiles, Pichiorri et al. (12) identified miRNAs that were differentially expressed in plasma cells of healthy donors, subjects with a benign precursor to multiple myeloma (monoclonal gammopathy of undetermined significance), and patients with multiple myeloma. In other miRNA expression profiling studies, Roccaro et al. (13) determined that miR-15/16 were down-regulated in relapsed/refractory multiple myeloma and regulated tumor proliferation in multiple myeloma cell lines. Lionetti et al. (14) identified 16 miRNAs sensitive to DNA copy number.

The prior art is deficient in methods of diagnosing, determining prognosis and treating multiple myeloma. More specifically, the prior art is lacking in the use of miRNA expression profiles as diagnostic and prognostic indicators of multiple myeloma. The present invention fulfills this long-standing need and desire in the prior art. SUMMARY OF THE INVENTION

The present invention is directed to a diagnostic or prognostic indicator of multiple myeloma in a subject. The indicator comprises a global expression profile of total miRNA in the subject, wherein a pattern of 39 up-regulated and 1 down-regulated genes indicates a diagnosis of multiple myeloma in the patient or is determinative of the subject's prognosis.

The present invention is also directed to a method for diagnosing multiple myeloma in a subject. The method comprises obtaining a biological sample from the subject and determining a global expression profile of all miRNAs in the sample. A pattern of up-regulation and down-regulation of a group of 40 specific mRNAs as described herein is a diagnostic indicator of multiple myeloma. The present invention is directed to a related method further comprising comparing one or both of a risk score or proliferation index to the miRNAs expression profile as prognosis for survival of the subject, where a positive correlation of a high risk score and a high proliferation index with the expression profile is indicative of a poor prognosis for the patient.

The present invention is directed further to a method for determining prognosis of a subject with multiple myeloma. The method comprises obtaining a biological sample from the subject, determining an expression profile of 40 specific miRNAs within an expression profile of total mRNAs in the sample; and calculating a risk score and proliferation index based on the up- and down-regulation of a group of 70 genes described herein comprising the sample. Up-regulation of 39 mRNAs and down-regulation of 1 mRNA together with a high risk score and proliferation index in the subject compared to a healthy control is indicative of poor prognosis.

The present invention is directed further still to a method for treating a subject having multiple myeloma. The method comprises administering to the subject one or more therapeutic compounds that inhibits a miRNA maturation pathway in the subject.

The present invention is directed further still to a method for screening for therapeutic compounds useful in treating high-risk myeloma. The method comprises contacting a biological sample comprising one or both of AG02 or DICER1 with a potential inhibitory compound and determining the inhibitory effect of the compound on one or both of an expression or activity of AG02 or DICER1 or on miRNA maturation. A decrease in expression or maturation levels is indicative of an inhibitory activity of the therapeutic against high-risk multiple myeloma.

Other and further aspects, features, and advantages of the present invention will be apparent from the following description of the presently preferred embodiments of the invention. These embodiments are given for the purpose of disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS So that the matter in which the above-recited features, advantages and objects of the invention, as well as others which will become clear, are attained and can be understood in detail, more particular descriptions of the invention are briefly summarized. The above may be better understood by reference to certain embodiments thereof which are illustrated in the appended drawings. These drawings form a part of the specification. It is to be noted; however, that the appended drawings illustrate preferred embodiments of the invention and therefore are not to be considered limiting in their scope.

Figure 1 shows variations of miRNAs and positive controls (PCs) across 52 MM samples. Mean of PCs has the lowest standard deviation.

Figures. 2A-2B show expression levels of 95 expressed miRNAs in plasma cells from patients with multiple myeloma compared with those from healthy donors. Figure 2A shows that the gobal expression of miRNAs in normal samples (N = 2) was significantly lower (P = .01) than in multiple myeloma samples (N = 52). Figure 2B shows scatter plot of mean expression levels of miRNAs in multiple myeloma samples compared with normal samples. Red triangles mark nine miRNAs on chromosome 13. Red circles mark 39 miRNAs statistically significantly up-regulated in multiple myeloma. Blue circle marks one miRNA statistically significantly down-regulated in multiple myeloma. Dashed line represents y = x.

Figure 3 is a comparison of expression levels of 10 miRNAs from three healthy donors and 10 patients with newly diagnosed multiple myeloma, using qRT-PCR. Error bars indicate standard error.

Figures. 4A-4B show unsupervised hierarchical clustering of samples from 52 multiple myeloma patients based on miRNA expression profiles, b demonstrates that patients' samples were clearly grouped into two clusters. Colored bars indicate the patients' GEP-defined risk scores (see Table 4 for a list of miRNAs up- or down-regulated in the two clusters). Figure 4B demonstrates that GEP-defined risk scores were significantly different between the two clusters.

Figures 5A-5C show that p21 was repressed after mimics of miR-106a, miR-106b, miR-20b, and miR-17-5p in JJN3 MM cell line in a Western blot (Figure 5A) and in a luciferase. Assay (Figure 5B). Error bars indicate standard error. Figure 5C shows an alteration in CDKN1A/p21Waf1/Cip1 was significantly associated with survival in multiple myeloma. Kaplan-Meier survival curves of overall survival.

Figures 6A-6D show alterations in EIF2C2/AG02 were significantly associated with survival in multiple myeloma. In each panel, the left plot shows the log- rank P values at different cutoffs that go through 5^th to 95th percentiles of signals. The right plot represents Kaplan-Meier survival curves of overall survival using the optimal cutoff identified in the left panel. The blue curve in the left plot represents the density distribution of signals. Three horizontal lines indicate three different significance levels: black, P < .05; green, P < .01 ; and red, P < .001. The survival analyses were performed on (a) DNA copy numbers (b), mRNA expression levels in the same samples with DNA copy number data (Figure 6B), mRNA expression levels in a data set of patients in Total Therapy 2 (Figure 6C), and mRNA expression levels in a Total Therapy 3 data set (Figure 6D).

Figures 7A-7D show the effects of silencing of AG02 on myeloma cells. In Figure 7A all eight randomly selected miRNAs were down-regulated after AG02 knockdown. Error bars indicate standard error. Figure 7B shows proliferation and viability in OCI-My5 and H929 cells. Error bars indicate standard error, b shows that silencing of AG02 induced cell cycle arrest and apoptosis. b is a Western blot analysis of proliferation- and apoptosis-associated proteins.

Figures 8A-8C shows the effects of silencing DICER1 on multiple myeloma cells. Figure 8A shows DICER1 was knocked down in OCIMy5 by both shRNAs; Figure 8B shows cell proliferation and viability in OCI-My5 and Figure 8C shows that silencing of D/CEf?7-induced cell cycle arrest and apoptosis. DETAILED DESCRIPTION OF THE INVENTION

As used herein the specification, "a" or "an" may mean one or more. As used herein in the claim(s), when used in conjunction with the word "comprising", the words "a" or "an" may mean one or more than one. As used herein "another" may mean at least a second or more. Furthermore, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

As used herein, the term "or" in the claims is used to mean "and/or" unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and "and/or."

As used herein, "about" refers to numeric values, including whole numbers, fractions, percentages, etc., whether or not explicitly indicated. The term "about" generally refers to a range of numerical values, e.g., +/- 5-10% of the recited value, that one would consider equivalent to the recited value, e.g., having the same function or result. In some instances, the term "about" may include numerical values that are rounded to the nearest significant figure.

As used herein, the term "subject" refers to any individual having multiple myeloma or suspected of having multiple myeloma or at risk for multiple myeloma.

In one embodiment of the present invention there is provided a diagnostic or prognostic indicator of multiple myeloma in a subject, comprising a global expression profile of total miRNA in the subject, wherein a pattern of 39 up-regulated and 1 down- regulated genes indicates a diagnosis of multiple myeloma in the patient or is determinative of the subject's prognosis.

In this embodiment the group of 39 up-regulated miRNAs may be hsa-miR-

30b, hsa-miR-19, hsa-miR-22, hsa-miR-766, hsa-miR-513, hsa-miR-574, hsa-miR-197, hsa-miR-125b, hsa-miR-365, hsa-miR-487b, hsa-miR-67,1 hsa-miR-181 a, hsa-miR-765, hsa-miR-138, hsa-miR-30a-5p, hsa-miR-212, hsa-miR-99a, hsa-let-7c, hsa-miR-30d, hsa- miR-150, hsa-miR-181 b, hsa-miR-181 d, hsa-miR-195, hsa-miR-198, hsa-miR-324-3p, hsa-miR-23a, hsa-miR-188, hsa-miR-222, hsa-miR-509, hsa-miR-623, hsa-let-7e, hsa- miR-202, hsa-miR-57,5 hsa-let-7b, hsa-miR-25, hsa-miR-92, hsa-miR-15,b hsa-miR-328, and hsa-let-7d and the 1 down-regulated miRNA may be hsa-miR-370. Also, the global expression profile may comprise a microarray. In addition, a high mean expression level of the miRNAs compared to a control from a healthy subject is indicative of a poor prognosis. In another embodiment of the present invention there is provided a method for diagnosing multiple myeloma in a subject, comprising obtaining a biological sample from the subject; and determining a global expression profile of all miRNAs in the sample; wherein a pattern of up-regulation and down-regulation of a group of 40 specific mRNAs as described supra is a diagnostic indicator of multiple myeloma.

Further to this embodiment the method comprises comparing one or both of a risk score or proliferation index to the miRNAs expression profile as prognosis for survival of the subject, wherein a positive correlation of a high risk score and a high proliferation index with the expression profile is indicative of a poor prognosis for the patient. In both embodiments the biological sample may comprise bone marrow.

In this embodiment determining risk may comprise calculating the average log2-scale expression of 51 up-regulated genes minus (the average log2-scale expression of 19 down-regulated genes. The group of 51 up-regulated genes may comprise 202345_s_at, 1555864_s_at, 204033_at, 206513_at, 1555274_a_at, 21 1576_s_at, 204016_at, 1565951_s_at, 219918_s_at, 201947_s_at, 213535_s_at, 204092_s_at, 213607_x_at, 2081 17_s_at, 210334_x_at, 204023_at, 201897_s_at, 216194_s_at, 225834_at, 238952_x_at, 200634_at, 208931_s_at, 206332_s_at, 220789_s_at, 218947_s_at, 213310_at, 224523_s_at, 201231_s_at, 217901_at, 226936_at, 58696_at, 200916_at, 201614_s_at, 200966_x_at, 225082_at, 242488_at, 24301 1_at, 201 105_at, 224200_s_at, 222417_s_at, 210460_s_at, 200750_s_at, 206364_at, 201091_s_at, 203432_at, 221970_s_at, 212533_at, 213194_at, 244686_at, 200638_s_at, and 205235_s_at and the group of 19 down-regulated genes may comprise 201921_at, 227278_at, 209740_s_at, 227547_at, 225582_at, 200850_s_at, 213628_at, 209717_at, 222495_at, 1557277_a_at, 1554736_at, 218924_s_at, 226954_at, 202838_at, 230192_at, 48106_at, 237964_at, 202729_s_at, and 212435_at. Also, the proliferation index may be based on the up-regulation of the 11 genes TOP2A, BIRC5, CCNB2, NEK2, ANAPC7, STK6, BUB1, CDC2, C10orf3, ASPM, and CDCA1.

In yet another embodiment of the present invention there is provided a method for determining prognosis of a subject with multiple myeloma, comprising obtaining a biological sample from the subject; determining an expression profile of 40 specific miRNAs within an expression profile of total mRNAs in the sample; and calculating a risk score and proliferation index based on the up- and down-regulation of a group of 70 genes comprising the sample: where up-regulation of 39 mRNAs and down-regulation of 1 mRNA and a high risk score and proliferation index in the subject compared to a healthy control is indicative of poor prognosis. In this embodiment the 39 up-regulated miRNAs may be hsa-miR-30b, hsa- miR-19, hsa-miR-22, hsa-miR-766, hsa-miR-513, hsa-miR-574, hsa-miR-197, hsa-miR- 125b, hsa-miR-365, hsa-miR-487b, hsa-miR-67,1 hsa-miR-181 a, hsa-miR-765, hsa-miR- 138, hsa-miR-30a-5p, hsa-miR-212, hsa-miR-99a, hsa-let-7c, hsa-miR-30d, hsa-miR-150, hsa-miR-181 b, hsa-miR-181d, hsa-miR-195, hsa-miR-198, hsa-miR-324-3p, hsa-miR-23a, hsa-miR-188, hsa-miR-222, hsa-miR-509, hsa-miR-623, hsa-let-7e, hsa-miR-202, hsa- miR-57,5 hsa-let-7b, hsa-miR-25, hsa-miR-92, hsa-miR-15,b hsa-miR-328, and hsa-let-7d and the 1 down-regulated miRNA may be hsa-miR-370. Furthermore, in this embodiment determining risk may comprise calculating the average log2-scale expression of 51 up- regulated genes minus the average log2-scale expression of 19 down-regulated genes. The groups of 51 up-regulated genes and 19 down-regulated genes for calculating risk scores are as described supra. The group of 1 1 up-regulated genes for determining a proliferation level also are as described supra. A representative biological sample includes but is not limited to bone marrow.

In yet another embodiment of the present invention, there is provided a method for treating a subject having multiple myeloma, comprising administering to the subject one or more therapeutic compounds that inhibits a miRNA maturation pathway in the subject. In this embodiment inhibition of miRNA maturation may comprise inhibiting expression of one or both of AG02 or DICER1 genes at the nucleic acid or protein level. Represenative examples of a therapeutic compound include but are not limited to a shRNA, an antibody or other small molecule inhibitor.

In yet another embodiment of the present invention there is provided a method for screening for therapeutic compounds useful in treating multiple myeloma, comprising contacting a biological sample comprising one or both of AG02 or DICER1 with a potential inhibitory compound; and determining the inhibitory effect of the compound on one or both of an expression or activity of AG02 or DICER1 or on miRNA maturation, wherein a decrease in expression or maturation levels is indicative of an inhibitory activity of the therapeutic against high-risk multiple myeloma. In this embodiment the biological sample may be bone marrow. Also, examples of a therapeutic inhibitor are a shRNA, an antibody or other small molecule inhibitor.

In the present invention whole-genome microarray analyses of CD138- enriched plasma cells from 52 newly diagnosed cases of multiple myeloma were used to correlate miRNA expression profiles with a validated mRNA-based risk stratification score, proliferation index, and predefined gene sets. In stark contrast to mRNAs, all the tested miRNAs were significantly up-regulated in high-risk disease as defined by a validated 70- gene risk score (P <.01) and proliferation index (P < .05). Increased expression of EIF2C2/AG02, a master regulator of the maturation and function of miRNAs and a component of the 70-gene mRNA risk model, is driven by DNA copy number gains in multiple myeloma. Silencing of AG02 dramatically decreased viability in multiple myeloma cell lines. Genome-wide elevated expression of miRNAs in high-risk multiple myeloma may be secondary to deregulation of AG02 and the enzyme complexes that regulate miRNA maturation and function. It is contemplated that all expressed miRNAs, instead of selected miRNAs, synergistically function together to regulate multiple myeloma disease progression.

The present invention provides a diagnostic or prognostic indicator of multiple myeloma. The indicator may be an expression profile of total miRNA, such as on a microarray or similar device. Microarray technology is well-known in the art. The global expression profile may be that of 95 miRNAs, such as listed in Table 1 , of which 39 are up- regulated, that is, compared to a profile of a healthy subject, a group of miRNAs comprising hsa-miR-30b, hsa-miR-19, hsa-miR-22, hsa-miR-766, hsa-miR-513, hsa-miR- 574, hsa-miR-197, hsa-miR-125b, hsa-miR-365, hsa-miR-487b, hsa-miR-67,1 hsa-miR- 181a, hsa-miR-765, hsa-miR-138, hsa-miR-30a-5p, hsa-miR-212, hsa-miR-99a, hsa-let- 7c, hsa-miR-30d, hsa-miR-150, hsa-miR-181 b, hsa-miR-181d, hsa-miR-195, hsa-miR- 198, hsa-miR-324-3p, hsa-miR-23a, hsa-miR-188, hsa-miR-222, hsa-miR-509, hsa-miR- 623, hsa-let-7e, hsa-miR-202, hsa-miR-57,5 hsa-let-7b, hsa-miR-25, hsa-miR-92, hsa- miR-15,b hsa-miR-328, and hsa-let-7d are up-regulated and 1 miRNA, hsa-miR-370, is down-regulated.

Thus, this indicator is useful in the diagnosis of multiple myeloma. In addition, this indicator may be used to determine the prognosis of a subject having multiple myeloma, such as, high-risk multiple myeloma. A further calculation of a high risk score based on expression levels of a group of 70 genes and/or a high proliferation index based on expression levels of a group of 11 genes using the formula described herein in Example 1 compared to a healthy subject together with the diagnostic miRNA expression profile is indicative of a poor prognosis or poor survival for the subject. Biological samples, such as bone marrow, may be obtained from the subjects using well-known and standard methods.

The present invention also provides a method for treating multiple myeloma in a subject. One or more therapeutic compounds are administered which are effective to inhibit or interfere with miRNA maturation. For example, therapeutic compounds effective to inhibit expression of one or both of AG02 or DICER1 genes negatively affect the miRNA maturation pathway. Inhibition of AG02 or DICER1 may be effected at the nucleic acid level or at the protein level. Such compounds may be, but not limited to, antisense oligonucleotides, short hairpin (sh) RNAs, antibodies, or other small molecule inhibitors.

Determining an effective dosage and dosing schedule is easily determined by one of ordinary skill in the art. An effective dose will depend on several factors, such as, the type of multiple myeloma, the progression or remission of the disease, the sex and age of the subject and their overall health, any other pharmaceuticals or chemotherapeutics currently being described, etc. The therapeutic compounds may be administered sequentially or concurrently in single or multiple doses.

As such, the present invention further provides a method for screening for potential inhibitor or therapeutic compounds useful to treat multiple myeloma. A biological sample, such as, but not limited to bone marrow, containing AG02 and/or DICER1 genes is contacted with a potential inhibitory compound. The inhibitor effect of the potential inhibitor on an expression level or activity of AG02 and/or DICER1 or on miRNA maturation is determined. A decrease in expression or maturation levels is indicative of an inhibitory activity of the therapeutic against multiple myeloma. Potential inhibitor compounds may be found within libraries, may be synthesized using standard chemical and/or molecular biological techniques or may be commercially available as is known in the art.

The examples are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion.

EXAMPLE 1

Materials and Methods

Study subjects

Bone marrow aspirates were obtained from 52 patients newly diagnosed with multiple myeloma. All subjects provided written informed consent, acknowledging the investigational nature of the protocol and the availability of other treatment options, as required by the Institutional Review Board and the Food and Drug Administration and in line with the Helsinki Declaration. Each sample was split into two, one for mRNA expression profiling and the other for miRNA expression profiling.

MicroRNA purification and microarrav hybridization

All miRNA samples were prepared from CD138-selected plasma cells that had been snapfrozen and stored in liquid nitrogen. TRIzol reagent (Invitrogen, Carlsbad, CA) (500 μΙ) was added to dry cell pellets, which were vigorously pipetted. After incubation at room temperature (5 minutes), chloroform (100 μΙ) was added, and the sample was briefly vortexed and again incubated at room temperature (5 minutes). After centrifugation (5 minutes, 12,000g, 4°C), the upper aqueous phase of samples was transferred to a new RNase-free microcentrifuge tube containing 250 μΙ of isopropanol. Following pulse- vortexing and incubation at room temperature (5 minutes), total RNA was precipitated (20 minutes, x 14,000g, 4°C). Following removal of the supernatant, the RNA pellets were washed in 500 μΙ of 70% ethanol (30 minutes, 14,000g, 4°C), allowed to air-dry (10 minutes), and then resuspended in 20 μΙ of RNase-free water. Total RNA samples were evaluated spectrophotometrically with a Nanodrop spectrophotometer. A260 values were used to quantify the samples, and A260/A280 ratios were used to determine relative purity; generally, RNA samples with A260/A280≥ 1.6 were routed to analysis. Purified miRNA was hybridized to the Human miRNA Microarray platform (Agilent, Santa Clara, CA) according to the Agilent version 2.0 protocol explicitly for target labeling, hybridization, washing, scanning, and image analysis. Data are accessible through NCBI GEO with accession number GSE17306.

Normalization of miRNA expression intensity

Normalization is an essential and fundamental preprocessing step in the analysis of any microarray experiment. Its primary purpose is to ensure that the observed interarray differences are due to biological phenomena rather than artifacts arising from differences in sample handling or processing. Methods employed for mRNA expression may not be suited for miRNA expression arrays because only about 1 ,000 human miRNA are known, while the number of mRNAs exceeds 20,000. Therefore, the assumption that most mRNAs are not differentially expressed across samples is unlikely to hold true for miRNAs, and while upregulated and downregulated mRNA transcripts are roughly equal, this is likely different for miRNAs. Several recent studies (41-45) have addressed the issue of miRNA normalization. As suggested in (41 ), the spiked-in controls spotted in Agilent's platform, which were external references and served as microarray controls in the hybridization protocol were utilized. The intensities of positive controls on each array were averaged and then were normalized miRNA signals by equalizing these averages among all arrays. Positive spike-in controls were eligible as references because their standard deviations were lowest in all miRNAs across all 52 samples (Fig. 1), suggesting that the positive controls are the most invariant across samples. Furthermore, the standard deviation of average intensities of all positive controls, which was the actual reference applied, was even lower, i.e., more invariant. Because they were independent of the samples of interest, the spiked-in positive controls were also used to normalize the miRNA signals of samples from healthy donors. Microarrav data analyses

The miRNA expression data were normalized so that the average values of positive controls (N=15) on each array were equal. Affymetrix U133Plus 2.0 gene expression data were normalized with MAS5 using default parameters in Affymetrix GeneChip operating software. All statistical analyses were performed with the statistics software R (Version 2.6.2; available from www.r-project.org) and R packages developed by BioConductor project (available from www.bioconductor.org). SAM algorithm (20) (a variant of the r test that adds a constant to stabilize variation of genes expressed at low levels) from R package siggenes was used to determine miRNAs with differential expression in multiple myeloma samples and normal samples. GSEA (24) was used to identify gene sets that were significantly associated with total miRNA expression level (see Supporting Information for details). Analysis of miRNA bv aPCR

Total RNA was extracted by TRIzol (Invitrogen) and reverse-transcribed in 10-μΙ reactions in an Applied Biosystems 9700 thermocycler (Applied Biosystems, Foster City, CA). TaqMan miRNA assays were used to detect and quantify mature miRNAs by ABI PRISM 7900 analytical thermocycler (Applied Biosystems) according to the manufacturer's recommendations. Normalization was performed with mean values of RNU43, RNU44, RNU48, and Z30.

Analysis of miRNA bv gRT-PCR

Total RNA was extracted by TRIzol (Invitrogen) and reverse-transcribed in 10-μΙ reactions (10 ng total RNA, 50 nM stem-loop RT primer, 1A-RT buffer, 0.25 mM each dNTPs, 3.33 U/ml MultiScribe reverse transcriptase, 0.25 U/ml RNase inhibitor [Applied Biosystems, Carlsbad, CA] in an Applied Biosystems 9700 thermocycler (30 minutes at 16°C, 30 minutes at 42°C, 5 minutes at 85°C; held at 4°C). All reverse- transcriptase reactions were run in duplicate, including controls with no template or with no reverse transcriptase. TaqMan miRNA assays were used to detect and quantify mature miRNAs by ABI PRISM 7900 analytical thermocycler (Applied Biosystems) according to the manufacturer's recommendations. Normalization was performed with means of RNU43, RNU44, RNU48, and Z30. Comparative qRT-PCR was performed in triplicate, including controls with no template. Relative expression was calculated by the comparative Ct method. Construction of luciferase reporter vector with 3' UTR of p21

Plasmids were constructed according to standard techniques. The 3' UTR of p21 was amplified from its cDNA (primers: 5'-AGAGCTCTCCGCCCACAGGAAGCCT- 3' (SEQ ID NO: 1 ), where the Sad site is underlined, and 5'- GCAAGCTTTGAGCACCTGCTGTATATTCAGC-3' (SEQ ID NO: 2), where the Hind\\\ site is underlined). After restriction enzyme reaction, the PCR fragment was then directly ligated into the Sacl and Hind\\\ cloning sites of pGL4.75 Luciferase Reporter Vectors (Promega). Clones were selected after colony PCR and restriction enzyme digestion. The clones were verified by sequencing.

Transfection

To transfect the miRNA precursors (Ambion) and reporter vectors, 2 A~ 105 cells were plated in 24-well plates. Next, 2 μΙ of Lipofectamine and each vector (control and experimental vector) and 1 μΙ of each precursor were added into the transfection agent complex and incubated at room temperature for 25 minures. Luciferase reporter activity was measured at 48 hours after transfection.

Luciferase reporter assays

Reporter activity was assayed with the Dual-Luciferase Reporter 1000 Assay System (Promega) according to the manufacturer's instructions. Luminescent signal was quantified by the Veritas Microplate Luminometer (Turner BioSystems, CA). All reporter assays shown in this study are based on data averaged from at three replicates.

Western blotting

Western blotting was carried out with the Western Breeze

Chemiluminescent Immunodetection protocol (Invitrogen). The following primary antibodies were used: anti-AG02, anti-DICER1 , anti-p-actin; anti-caspase-3, -8, and -9; anti-p21Waf1/Cip1 ; anti-p27Kip1 ; anti-CDK2; and anti-CCND1 (Cell Signaling Technology, Danvers, MA).

EIF2C2/AGQ2 and DICER1 knockdown

Two synthetic double-stranded oligonucleotides specific for AG02 (5'- GATCCCCGCAAGGATCGCATCTTCAAGGTTCAAGAGACCTTGAAGATGCGATCCTTG CTTTTTA-3' (SEQ ID NO: 3) and 5'- GATCCCCGCAGGACAAAGATGTATTAAATTCAAGAGATTTAATACATCTTTGTC

CTGCTTTTTA-3' (SEQ ID NO: 4)) and two synthetic double-stranded oligonucleotides specific for DICER1 (5'-GATCCCCAGAGGTACTTAGGAA

ATTTTTCAAGAGAAAATTTCCTAAGTACCTCTTTTTTA-3' (SEQ ID NO: 5) and 5'- GATCCCCAAGAATCAGCCTCGCAACAAATTCAAGAGATTTGTTGCGAGGCTGATTCTT TTTTTA-3' (SEQ ID NO: 6) were synthesized; a nonsense scrambled oligonucleotide (5'- GATCCCCGACACGCGACTTGTACCACTTCAAGAGAGTGG

TACAAGTCGCGTGTCTTTTTA-3' (SEQ ID NO: 7)) was obtained from OligoEngine (Seattle, WA). Double-stranded shRNA oligonucleotides were cloned into lentiviral pLVTH vectors (kindly provided by Didier Trono, MD, National Center for Competence in Research, Lausanne, Switzerland) that allow doxycycline-inducible expression of the cloned fragment. Recombinant lentivirus was produced by transient transfection of 293T myeloma cells following a standard protocol. Briefly, crude virus was concentrated by ultracentrifugation (90 minutes, 9,000g). Viral titers were determined by measuring the amount of HIV-1 p24 antigen by enzyme-linked immunosorbent assay (NEN Life Sciences, Boston, MA). A 99% transduction efficiency of myeloma cells was achieved with 3A~103 ng lentiviral p24 particles/106 cells. Production and titration of lentiviral stocks were performed according to the protocols outlined above.

Cell cycle and apoptosis analysis

Cells (1A-106) of each sample were fixed in 75% ethanol at -20°C overnight. The following day, cells were washed with cold PBS, treated with 100 μg RNase A (Qiagen, Valencia, CA), and stained with 50 g of propidium iodide (Roche, Mannheim, Germany). Flow cytometric acquisition was performed with a three-color FACScan flow cytometer and CellQuest software (Becton Dickinson, San Jose, CA). For each sample, 10,000 events were gated. Data analysis was performed with Modfit LT software (Verity Software House, Topsham, ME). The apoptotic cell fraction was determined as the percentage of cells with apoptotic DNA. For the cell proliferation assay, H929 and OCI- My5 myeloma cell lines infected with AG02 or DICER1 shRNAs and controls were seeded at a density of 3x10⁵ cells/ml. Cell number and viability were determined by trypan blue exclusion at various time intervals.

Gene set enrichment analysis (GSEA)

Gene set enrichment analysis (6) was used to identify gene sets that were significantly associated with total miRNA expression level. The gene set enrichment analysis method requires two inputs: (1) a master list of genes ranked according to expression differences between two states and (2) a priori defined gene sets, e.g., pathways that consist of genes. In this study, input #1 is a list of all genes presented on the Affymetrix U133Plus 2.0 platform, ranked according to their associations (Pearson's correlation coefficients) with total miRNA expression levels across 52 patient samples; input #2 is MSigDB (Molecular Signatures Database), a collection of gene sets for use with gene set enrichment analysis software (www.broadinstitute.org/gsea/msigdb/index.jsp). MSigDB contained the following five categories: 386 positional gene sets (genes clustered according to genomic loci), 1 ,892 curated gene sets (compiled from pathway databases, literature, and knowledge-of-domain experts), 837 motif gene sets (genes clustered according to cis- regulatory motifs at promoter region and 3'-UTR), 883 computational gene sets (cancerassociated modules identified by mining cancer-oriented microarray data), and 1 ,454 GO gene sets (genes clustered according to Gene Ontology annotations). Gene set enrichment analysis software can be downloaded from www.broadinstitute.org/gsea/. The enrichment scores (ES) were calculated with default parameters. Gene expression profile-defined risk score and proliferation index

In the MM patients with shorter survival time, 70 genes were used to define risk scores (47). Of these genes, 19 were down-regulated (201921_at, 227278_at, 209740_s_at, 227547_at, 225582_at, 200850_s_at, 213628_at, 209717_at, 222495_at, 1557277_a_at, 1554736_at, 218924_s_at, 226954_at, 202838_at, 230192_at, 48106_at, 237964_at, 202729_s_at, 212435_at), and 51 were up-regulated (202345_s_at, 1555864_s_at, 204033_at, 206513_at, 1555274_a_at, 21 1576_s_at, 204016_at, 1565951_s_at, 219918_s_at, 201947_s_at, 213535_s_at, 204092_s_at, 213607_x_at, 208117_s_at, 210334_x_at, 204023_at, 201897_s_at, 216194_s_at, 225834_at, 238952_x_at, 200634_at, 208931_s_at, 206332_s_at, 220789_s_at, 218947_s_at, 213310_at, 224523_s_at, 201231_s_at, 217901_at, 226936_at, 58696_at, 200916_at, 201614_s_at, 200966_x_at, 225082_at, 242488_at, 243011_at, 201 105_at, 224200_s_at, 222417_s_at, 210460_s_at, 200750_s_at, 206364_at, 201091_s_at, 203432_at, 221970_s_at, 212533_at, 213194_at, 244686_at, 200638_s_at, 205235_s_at).

The risk score was calculated as follows:

(the average log2-scale expression of the 51 up-regulated genes) - (the average log2- scale expression of the 19 down-regulated genes).

This simple, univariate summary of the 70-gene expression profile for each patient may enhance robustness to residual array effects. Proliferation index was calculated using the normalized value of 1 1 genes associated with proliferation: TOP2A, BIRC5, CCNB2, NEK2, ANAPC7, STK6, BUB1, CDC2, C10orf3, ASPM, and CDCA 1 (48). EXAMPLE 2

Overview of miRNA expression profiles

The miRNA expression profiles were normalized based on spiked-in controls spotted in Agilent's platform. Briefly, the intensities of positive controls on each array were averaged, and then miRNA signals were normalized by equalizing these averages among all arrays. Six miRNAs presented in Agilent's miRNA microarray— hsa- miR-560, hsa-miR-565, hsa-miR-768-3p, hsa-miR-768-5p, hsa-miR-801 , and hsa-miR- 128b— were not in miRBase (19) release 12.0 and were discarded; 464 human miRNAs remained. Ninety-five human miRNAs were identified as expressed, which we defined as an intensity >log₂100 in at least one sample (from patients with multiple myeloma or healthy donors) (Table 1). Most human miRNAs were absent or expressed at very low levels in the samples. Table 1 specifically compares expression levels of miRNAs between multiple myeloma samples and normal samples. MiRNAs up-regulated in multiple myeloma appear in italics and miRNAs down-regulated in multiple myeloma are in bold.

Table S1

ID P value FDR* MM sample mean Normal sample mean Fold change

hsa-miR-30b 0.00000 0.00000 4.7755 0.0000 NA hsa-miR-191† 0.00011 0.00333 5.1095 0.3099 16.4900 hsa-miR-221† 0.00011 0.00333 4.8484 0.0000 NA hsa-miR-766 0.00042 0.01000 4.8181 0.3093 15.5791 hsa-miR-513 0.00084 0.01333 8.3642 5.1019 1.6394 hsa-miR-574 0.00084 0.01333 6.5661 2.8406 2.3115 hsa-miR-197 0.00116 0.01571 6.3594 2.1498 2.9582 hsa-miR-125b† 0.00274 0.02545 4.3502 0.3018 14.4125 hsa-miR-365 0.00274 0.02545 5.2386 0.6070 8.6296 hsa-miR-487b 0.00274 0.02545 4.8547 1.9351 2.5088 hsa-miR-671 0.00295 0.02545 5.2533 2.9582 1.7758 hsa-miR-181a† 0.00347 0.02750 4.3968 1.2636 3.4795 hsa-miR-765 0.00526 0.03643 5.2179 2.6441 1.9734 hsa-miR-138† 0.00537 0.03643 5.2211 0.5888 8.8679 hsa-miR-30a-5pf 0.00579 0.03667 2.7550 0.0000 NA hsa-miR-212 0.00695 0.04125 4.8057 2.2781 2.1096 hsa-miR-99a 0.00789 0.04278 3.3004 0.1096 30.1024 hsa-let-7c 0.00811 0.04278 5.4500 3.2259 1.6895 hsa-miR-30d† 0.00916 0.04391 5.1334 1.8250 2.8129 hsa-miR-150 0.00979 0.04391 6.2599 3.7937 1.6501 hsa-miR-181b† 0.01042 0.04391 5.0914 2.3230 2.1918 hsa-miR-181d 0.01042 0.04391 6.8904 4.6169 1.4924 hsa-miR-195 0.01063 0.04391 4.5123 0.7672 5.8814 hsa-miR-198 0.01347 0.05333 4.9714 2.7509 1.8072 hsa-miR-370 0.01442 0.05480 8.3868 10.5108 0.7979 hsa-miR-324-3p 0.01505 0.05500 8.5232 6.7656 1.2598 hsa-miR-23a† 0.01579 0.05556 5.7038 1.6241 3.5119 hsa-miR-188 0.01663 0.05643 5.9867 4.1334 1 .4484 hsa-miR-222 0.01821 0.05966 6.2106 1.3868 4.4783 hsa-miR-509 0.02179 0.06257 5.3542 3.1979 1.6743 hsa-miR-623 0.02179 0.06257 4.2000 1.9652 2.1372 hsa-let-7e 0.02200 0.06257 6.1739 3.1071 1.9870 hsa-miR-202 0.02242 0.06257 7.3390 5.6786 1.2924 hsa-miR-575 0.02242 0.06257 7.7008 6.1056 1.2613 hsa-let-7b 0.02305 0.06257 7.8735 6.0993 1.2909 hsa-miR-25† 0.02389 0.06306 6.1750 4.1263 1.4965 hsa-miR-92† 0.02716 0.06973 6.1168 4.4610 1.3712 hsa-miR-15b† 0.03063 0.07658 8.3403 6.4942 1.2843 hsa-miR-328 0.03432 0.08325 1.6705 0.0000 NA hsa-let-7d 0.03505 0.08325 7.6713 5.8381 1.3140 hsa-miR-320 0.04674 0.10581 6.9536 5.5299 1.2574 hsa-miR-34a 0.04747 0.10581 4.9393 2.0840 2.3701 hsa-miR-223 0.04789 0.10581 5.4373 3.8878 1.3985 hsa-miR-572 0.04958 0.10705 5.5529 3.3794 1.6432 hsa-miR-424 0.05295 0.11178 2.4786 0.4365 5.6788 hsa-miR-483 0.05663 0.11489 1.3481 0.0000 NA hsa-miR-432 0.05684 0.11489 4.3565 1.9474 2.2371 hsa-let-7a 0.07863 0.15490 8.6706 6.1034 1.4206 hsa-miR-103 0.07989 0.15490 5.6803 4.0652 1.3973 hsa-miR-204 0.08337 0.15654 1.2559 0.0000 NA hsa-miR-193b 0.08495 0.15654 7.3256 6.1666 1.1879 hsa-miR-24 0.08568 0.15654 6.4594 5.1744 1.2483 hsa-miR-26b 0.10505 0.18830 6.6827 3.3050 2.0220 hsa-miR-494 0.11579 0.20370 8.6661 6.9712 1.2431 hsa-miR-20b 0.12116 0.20714 4.5552 2.5890 1.7595 hsa-miR-142-5p 0.12211 0.20714 7.6415 3.4417 2.2203 hsa-miR-650 0.13053 0.21754 4.3337 5.4444 0.7960 hsa-miR-27a 0.13453 0.21831 5.0203 2.5361 1.9795 hsa-miR-18a 0.13558 0.21831 3.6547 1.2055 3.0318 hsa-let-7f 0.14442 0.22867 8.8764 6.3561 1.3965 hsa-miR-186 0.14895 0.23197 4.5463 2.0708 2.1955 hsa-miR-106b 0.15411 0.23613 7.0216 4.7032 1.4929 hsa-miR-17-5p 0.16674 0.25078 5.0316 3.7703 1.3346 hsa-miR-155 0.16895 0.25078 4.6982 6.2329 0.7538 hsa-miR-101 0.17726 0.25908 6.6514 3.6453 1.8246 hsa-miR-15a 0.19232 0.27682 4.5104 2.2706 1.9865 hsa-miR-125a 0.21684 0.30746 1.8266 2.6764 0.6825 hsa-miR-1 6a 0.22411 0.31309 3.8992 5.1927 0.7509 hsa-miR-135a 0.25284 0.34812 1.4390 0.6757 2.1295 hsa-miR-107 0.25705 0.34886 6.9383 5.9734 1.1615 hsa-miR-374 0.27000 0.36127 5.5224 2.8606 1.9306 hsa-miR-20a 0.29505 0.38931 6.2937 5.0303 1.2511 hsa-miR-142-3p 0.30726 0.39986 9.3740 7.1454 1.3119 hsa-miR-26a 0.31874 0.40919 8.6723 7.6560 1.1327 hsa-miR-100 0.32358 0.40987 2.9157 1.3507 2.1587 hsa-miR-19a 0.33547 0.41934 6.5906 4.7391 1.3907 hsa-miR-30e-5p 0.37863 0.46714 7.1608 5.6006 1.2786 hsa-miR-19b 0.39863 0.48551 8.3200 7.1050 1.1710 hsa-miR-21 0.43211 0.51962 7.8836 6.5762 1.1988 hsa-let-7i 0.44400 0.52725 6.8954 5.9651 1.1560 hsa-miR-106a 0.47179 0.55333 5.2371 4.6752 1.1202 hsa-miR-375 0.48600 0.56305 0.3150 0.0000 NA hsa-miR-1 0.50421 0.5771 1 0.3044 0.0000 NA hsa-miR-148a 0.52032 0.58845 10.5630 9.7733 1.0808 hsa-let-7g 0.55232 0.61729 8.9648 8.0704 1.1108 hsa-miR-182 0.63600 0.69318 3.0343 3.5442 0.8561 hsa-miR-152 0.63737 0.69318 3.6555 4.2967 0.8508 hsa-miR-29b 0.64211 0.69318 8.6899 7.7678 1.1187 hsa-miR-22 0.66105 0.70562 7.4342 7.8774 0 .9437 hsa-miR-638 0.68347 0.72144 8.4945 8.2868 1.0251 hsa-miR-16 0.70937 0.74055 7.9783 7.5778 1.0529 hsa-miR-29a 0.75547 0.78011 10.0783 9.7263 1.0362 hsa-miR-29c 0.77579 0.79247 10.9884 11.4096 0.9631 hsa-miR-630 0.78463 0.79298 7.0455 6.8895 1.0226 hsa-miR-331 0.98189 0.98189 5.7152 5.7296 0.9975

* FDR, false discovery rate.

† Common miRNAs reported as significantly up-regulated in both our study and that of Pichiorri, F., et al. (2008). Proc Natl Acad Sci U S A 105: 12885-12890. There were no common miRNAs down-regulated in both studies Total miRNA expression levels were higher in myeloma cells than in normal plasma cells

The total expression levels of miRNAs in CD138⁺ plasma cell samples of 52 patients newly diagnosed with multiple myeloma (Table 2) were compared to those in samples from two healthy donors. Table 2 shows clinical features of 52 patients with multiple myeloma. The total miRNA expression level, which was determined by the mean expression levels of 95 expressed miRNAs, was higher in samples from patients with multiple myeloma than in those from healthy donors (Fig. 2A; P = .01 , one-sided Wilcoxon test). Among 40 miRNAs whose expression levels were significantly different (statistical analysis of microarray [SAM] (20); false discovery rate [FDR] < .1) in myeloma cells than in healthy cells, 39 were consistently expressed at higher levels in samples from patients newly diagnosed with multiple myeloma than in those from healthy donors; only 1 of the 40 miRNAs was expressed at lower levels in samples from patients than in those from healthy donors (Fig. 2B and Table 1).

TABLE 2

Clinical Features n/N (%)

Age≥ 65 years 17/52 (32.7)

Albumin≥ 3.5 g/dL 38/52 (73.1)

B2M≥ 3.5 mg/dL 29/51 (56.9)

B2M≥ 5.5 mg/dL 17/51 (33.3)

CRP > 8 mg/L 1/52 (1.9)

Hemoglobin≥ 10 g/dL 36/51 (70.6)

Creatinine≥ 2 mg/dL 5/51 (9.8)

LDH≥ 190 U/L 8/51 Π5.7)

B2M, β2 microglobulin; CRP, C-reactive protein; LDH, lactate dehydrogenase Furthermore, with quantitative PCR (qPCR), 10 randomly selected miRNAs (miR-15b, miR-16, miR-17-5p, miR-19b, miR-21 , miR-22, miR-29c, let-7a, let-7d, and let- 7f) were measured in purified plasma cells from three healthy donors and 10 patients with multiple myeloma (miR-19b and let-7f were analyzed in cells from two healthy donors due to limited cells for all 10 analyses). All miRNAs except one were expressed at higher levels in plasma cells from patients than from healthy donors (Fig. 3). These data consistently suggested that higher total expression levels of miRNAs might be associated with multiple myeloma disease initiation.

Surprisingly, although chromosome 13 is deleted in approximately 50% of patients with multiple myeloma (21 , 22), two of the nine expressed miRNAs mapping to chromosome 13 were expressed at significantly higher levels in myeloma samples than in normal samples (FDR < .1), and the other seven were expressed at marginally higher levels in myeloma samples than in normal samples (Fig. 2B and Table 1). This observation was consistent with a recent study reporting that four miRNAs from chromosome 13 (miR- 15a, miR-19b, miR-20a, and miR-92a) were expressed at higher levels in samples from multiple myeloma patients than in those from healthy donors; none were expressed at lower levels (12). Of note, this list of differentially expressed miRNAs did not completely overlap with Pichiorri et al.'s (12) (see Table 1 for a comparison). This discrepancy may be due to use of different statistical methods (SAM here, and t test in Pichiorri's study), different sample sizes, and different experimental platforms.

Total miRNA expression level was associated with GEP-defined risk score and proliferation index

GEPs were used previously to define risk scores and proliferation indexes for multiple myeloma disease prognosis according to expression levels of 70 and 11 genes, respectively (23). Higher risk scores and higher proliferation indexes were associated with shorter survival of multiple myeloma patients. Taking advantage of paired miRNA and GEPs for each of 52 patients, the potential association between global miRNA expression levels and prognosis was investigated by linking total miRNA expression in an individual patient's sample with the risk score and proliferation index defined by the GEP of the same sample. Total miRNA expression level was significantly associated with risk score (P = .003) and proliferation index (P = .03). A high risk score and a high proliferation index were both significantly associated with an unfavorable clinical outcome. This observation suggests that high expression levels of total miRNA potentially confers an inferior clinical outcome. Total miRNA expression level was associated with high-risk cancer gene sets

Gene set enrichment analysis (GSEA) (24) was used to identify the gene sets that were significantly associated with total miRNA expression level. The correlations between total miRNA expression level and expression levels of individual protein-coding genes were calculated and then gene set enrichment analysis were used to associate the correlations with gene sets. Gene set enrichment analysis used the Molecular Signatures Database (MSigDB; www.broad.mit.edu/gsea/msigdb) containing five categories: positional gene sets, curated gene sets, motif gene sets, computational gene sets, and gene ontology gene. Table 3 lists some of the most significant and interesting gene sets in each category (Table 4 includes the full list of all miRNAs up- or down-regulated in the two clusters defined in Figs. 7A-7D).

Table 3

P value ES* FDR† Rank c2: curated gene sets

CANCER_UNDIFFERENTIATED_META_UP < 1 E-16 0.57 < 1E-14 1

HUMAN_MITODB_6_2002 < 1 E-16 0.25 < 1E-14 3

MITOCHONDRIA < 1 E-16 0.24 < 1 E-14 4

STEMCELL_EMBRYONIC_UP < 1 E-16 0.20 < 1 E-14 7

STEMCELL_NEURAL_UP < 1 E-16 0.20 < 1 E-14 8

TARTE_PLASMA_BLASTIC < 1 E-16 0.41 < : 1 E-14 9

ZHAN_MM_CD138_PR_VS_REST 1. 45E-14 0.66 1. 62E-12 14

STEMCELL_HEMATOPOIETIC_UP 1. 13E-07 0.09 3. 27E-06 54 c4: computational gene sets

GNF2_CCNA2 < 1 E-16 0.60 < 1 E-15 1

GNF2_CCNB2 < 1 E-16 0.62 < 1 E-15 2

GNF2_CDC2 < 1E-16 0.59 < 1 E-15 3

GNF2_CDC20 < 1 E-16 0.65 < 1 E-15 4

GNF2_CENPF < 1 E-16 0.59 < 1 E-15 5

GNF2_PCNA < 1 E-16 0.62 < 1 E-15 6

GNF2_RRM1 < 1 E-16 0.56 < 1 E-15 7

GNF2_SMC4L1 < 1 E-16 0.52 < 1 E-15 8

MORF_AATF < 1 E-16 0.34 < 1 E-15 9

MORF AP2M1 < 1 E-16 0.32 < 1 E-15 10

* ES, enrichment score, is a statistic generated by GSEA to measure an overlap between two gene sets.† FDR, false discovery rate. Table 4

Cluster 1 Cluster 2

Probe Id P value FDR* mean mean hsa-miR-29b 3.74E-10 3.56E-08 9.996630323 7.731632333 hsa-miR-572 4.76E-08 2.26E-06 4.553701807 6.285723668 hsa-miR-509 1.27E-07 4.01 E-06 4.176824914 6.217632263 hsa-miR-202 3.87E-07 6.84E-06 6.137843353 8.219893543 hsa-miR-16 4.25E-07 6.84E-06 9.389209022 6.943705944 hsa-miR-29a 4.32E-07 6.84E-06 10.934191 9.450710862 hsa-miR-638 1.01 E-06 1.37E-05 7.984650288 8.868388169 hsa-miR-106a 3.50E-06 4.15E-05 6.25143927 4.493168224 hsa-miR-575 7.12E-06 7.52E-05 6.875289744 8.306090913 hsa-miR-188 8.32E-06 7.91 E-05 5.069172858 6.659636568 hsa-miR-494 2.47E-05 0.000212975 8.103207658 9.078839107 hsa-miR-29c 3.94E-05 0.000311634 11.6480916 10.50457345 hsa-miR-17-5p 4.93E-05 0.00036045 5.885707372 4.405319003 hsa-miR-324-3p 8.53E-05 0.000579124 7.70893677 9.120358853 hsa-let-7i 0.000103449 0.000655176 7.758331806 6.262539895 hsa-miR-15a 0.000241949 0.001436571 5.438855309 3.829523267 hsa-miR-198 0.000343683 0.00192058 4.202315454 5.5354392 hsa-miR-212 0.000448618 0.002367705 3.908545189 5.463601169 hsa-miR-148a 0.000519196 0.002595979 11.04127173 10.21221082 hsa-miR-20a 0.000772004 0.003667018 6.941583507 5.818516921 hsa-miR-331 0.000890851 0.004030041 6.188715202 5.367918995 hsa-miR-22 0.001237385 0.005343254 8.080209753 6.960443481 hsa-miR-671 0.001645704 0.006797473 4.770701818 5.607204473 hsa-miR-193b 0.003668704 0.013983542 7.875185889 6.92250943 hsa-miR-142-3p 0.003679879 0.013983542 10.01776911 8.901882687 hsa-miR-195 0.003950704 0.014435264 3.815864492 5.023082583 hsa-miR-513 0.007292487 0.02565875 7.643571002 8.892576649 hsa-miR-100 0.008362898 0.028374117 3.871338846 2.214968721 hsa-miR-152 0.009419338 0.03042717 4.516650141 3.023964472 hsa-miR-18a 0.009702094 0.03042717 4.32911546 3.160135007 hsa-miR-24 0.009928866 0.03042717 6.921294694 6.120705923 hsa-miR-135a 0.016378796 0.048624551 2.269470233 0.829984885

hsa-miR-623 0.019252292 0.055423264 3.633589086 4.615442003

hsa-miR-107 0.021 120883 0.059014233 7.240878657 6.716415713

hsa-miR-21 0.023543662 0.063904225 8.367367885 7.528783815

hsa-miR-106b 0.027634428 0.072924184 7.292868217 6.822667292

hsa-miR-20b 0.029359171 0.075381654 5.01861328 4.215377839

hsa-miR-101 0.0312596 0.076508756 6.907498354 6.463613544

hsa-miR-25 0.031408858 0.076508756 6.536964165 5.90954814

hsa-miR-125a 0.037437422 0.087092554 2.639979978 1.230101205

hsa-miR-487b 0.03836926 0.087092554 4.238410029 5.30667262

hsa-miR-650 0.038504076 0.087092554 3.775844471 4.742748644

hsa-miR-181 b 0.044591491 0.096929407 5.514545434 4.781 1 10612

hsa-miR-181a 0.04489362 0.096929407 5.028250666 3.933765996

hsa-miR-765 0.046105025 0.09733283 4.837362856 5.496916883

* FDR, false discovery rate.

Remarkably, the gene set "CANCER_UNDIFFERENTIATED_META_UP" was the most highly enriched in the category of curated gene sets. This gene set contained genes up-regulated in multiple types of undifferentiated cancers (Table 3) (25). Consistent with this gene set, the 9^th most enriched gene set was TARTE_PLASMA_BLASTIC, which is overexpressed in plasmablasts (a type of undifferentiated plasma cell) (Table 3) (26). Further supporting the association of up- regulated total miRNA level and undifferentiated cells, a few gene sets related to stem cells (the most undifferentiated cells) were significantly enriched. Specifically, STEMCELL_EMBRYONIC_UP, STEMCELL_NEURAL_UP, STEMCELL_HEMATOPOIETIC_UP, and STEMCELL_COMMON_UP, which contained genes up-regulated in at least one of three distinct types of stem cells (27), were ranked as 7^th, 8^th, 54^th, and 208^th most enriched gene sets, respectively. Of note, the other stem cell signature, STEMCELL_COMMON_DN, which contained genes down-regulated in all of three types of stem cells, was not significantly enriched. Taken together, these observations strongly indicate that high total miRNA expression level may be associated with the high-grade undifferentiated stage of cancer, which tends to behave more aggressively than the low-grade counterparts (25).

Supporting the observed association of high expression of total miRNA with proliferation index, the 14^th most enriched gene set was ZHAN_MM_CD138_PR_VS_REST (Table 3), which is up-regulated in the proliferation molecular subgroup of MM (28). Furthermore, in the category of computational gene sets, the majority of the 10 most enriched sets (Table 3) were composed of cell-proliferation genes. Of note, hundreds of cancer-related gene sets in the category of computational gene sets were significantly associated with high miRNA expression (Table 4). Analysis revealed that high expression of total miRNAs was associated with a variety of high-risk gene sets, where were signatures of undifferentiated cancer cells and cell cycle.

Expression of individual miRNAs was associated with GEP-defined risk score and proliferation index

The associations of each of the 95 expressed human miRNAs with risk score and proliferation index were investigated. Consistent with the observations above, no individual miRNA was significantly negatively associated with either GEP-defined risk score or proliferation index; however, 28 miRNAs significantly (FDR < .1) were positively associated with risk score and two with proliferation index. Table 5 shows the association of expression level of individual miRNAs, risk score (RS), and proliferation index (PI) The two miRNAs associated with proliferation index were also associated with risk score. Remarkably, among the 10 expressed miRNAs that mapped to chromosome 13, eight were significantly positively associated with risk score and one with proliferation index.

Table 5

Pearson's correlation

coefficient P value FDR*

Association with RS

hsa-miR-106a 0.54 3.13E-05 7.42E-04

hsa-miR-17-5p 0.55 2.68E-05 7.42E-04

hsa-miR-18a 0.57 8.68E-06 7.42E-04

hsa-miR-20a 0.55 2.83E-05 7.42E-04

hsa-miR-142-3p 0.54 4.31 E-05 8.19E-04

hsa-miR-20b 0.53 5.20E-05 8.24E-04

hsa-miR-25 0.52 8.12E-05 1.10E-03

hsa-miR-106b 0.51 1.30E-04 1.55E-03

hsa-miR-142-5p 0.49 2.37E-04 2.50E-03

hsa-miR-103 0.48 3.53E-04 3.36E-03

hsa-miR-19a 0.46 6.24E-04 5.39E-03

hsa-miR-107 0.44 1.07E-03 7.83E-03 hsa-miR-125a 0.44 1.02E-03 7.83E-03 hsa-miR-92 0.42 1.70E-03 1.15E-02

hsa-miR-15a 0.40 3.14E-03 1.99E-02

hsa-miR-181 b 0.40 3.43E-03 2.04E-02

hsa-miR-19b 0.39 4.25E-03 2.37E-02

hsa-miR-16 0.38 5.10E-03 2.68E-02

hsa-miR-29b 0.38 5.37E-03 2.68E-02

hsa-let-7i 0.38 5.84E-03 2.77E-02

hsa-miR-148a 0.35 1.1 1 E-02 4.84E-02

hsa-miR-331 0.35 1.12E-02 4.84E-02

hsa-let-7c 0.33 1.77E-02 7.00E-02

hsa-miR-193b 0.33 1.77E-02 7.00E-02

hsa-miR-21 0.31 2.49E-02 9.11 E-02

hsa-miR-99a 0.31 2.45E-02 9.11 E-02

hsa-miR-26b 0.31 2.71 E-02 9.55E-02

hsa-miR-125b 0.30 2.86E-02 9.71 E-02

Association with PI

hsa-miR-107 0.45 7.81 E-04 5.36E-02

hsa-miR-18a 0.44 1.13E-03 5.36E-02

In addition to the statistically significant positive associations with risk score and proliferation index, many more positive associations (risk score, N = 77; proliferation index, N = 77) were identified than negative associations (risk score, N = 18; proliferation index, N = 18). Not all associations were statistically significant; the difference in positive and negative associations (77 vs. 18) was significant (P = 1.3E-9; one-sided proportional test). Furthermore, the means of Pearson's correlation coefficients were 0.19 for risk score and 0.13 for proliferation index, both of which were significantly greater than expected by permutation test (P < 1 E-10; one-sided t test). These observations suggested that although some individual miRNAs alone could not significantly contribute to disease progression, their collective synergy might significantly contribute to MM disease progression.

Unsupervised clustering stratified patients according to high risk and low risk

Unsupervised hierarchical clustering analysis was applied to miRNA expression profiles. The 52 patients clearly separated into two clusters on the basis of expression levels of 95 expressed miRNAs (Figs. 4A-4B; see Table 4 for a list of miRNAs up- or down-regulated in the two clusters). The patients in cluster 1 had significantly higher GEP-defined risk scores than those in cluster 2 (P = .02; one-sided t test), suggesting that overall miRNA expression profiles were associated with risk scores. Of note, proliferation indexes were not significantly different between the clusters (P = .4; two-sided t test), indicating that the unsupervised clustering could not stratify the patient samples according to high or low proliferation index.

CDKN1A/o21Waf1/CiDl was a target of multiple mi^'RNAs

The targets of the expressed miRNAs were investigated in Tarbase, a comprehensive database for experimentally supported animal miRNA targets (29). It was found that CDKN1A/p21Waf1/Cip1 was an experimentally supported target of four distinct miRNAs (miR-106a, miR-106b, miR-17-5p, and miR-20b) among the 95 miRNAs expressed in myeloma cells; interestingly, all four miRNAs were associated with risk score (Table 5). No other single gene was targeted by more than three of the expressed miRNAs, according to Tarbase. Western blotting and luciferase reporter assays confirmed that p21Waf1/Cip1 was repressed in the JJN3 myeloma cell line after transfection of mimics of miR-106a, miR-106b, miR-17-5p, and miR-20b (Figs. 5A-5B). P21Waf1/Cip1 is a cyclin-dependent kinase inhibitor and functions as a regulator of cell cycle progression at Gi, and numerous studies have reported it as a tumor suppressor gene in MM (30-32). In 272 patients newly diagnosed with MM, p21Waf1/Cip1 expression level was highly significantly associated with overall survival (P = 1 E-1 1 ; hazard ratio = 0.12) (Fig. 5C).

Silencing of EIF2C2/AGQ2 and DICER 1 arrested growth and promoted apoptosis in myeloma cell lines

Consistent with the multiple lines of evidence that global increased expression of miRNAs in MM is associated with disease outcome, we previously reported that AG02, a master regulator of miRNA genesis and functionality (16-18) and of B-cell differentiation (18), was an important marker for MM disease prognosis in the model for GEP-defined risk score (23). Furthermore, AG02 is sensitive to DNA copy number, and the copy number of its locus is significantly associated with disease outcome (33) (see Figs. 6A-6D).

To validate the functional roles of AG02 in myeloma cells, two AG02 shRNAs were used to knockdown AG02 in two myeloma cell lines (H929 and OCI-My5); Western blots confirmed AG02 knockdown in both cell lines (Fig. 7D). We used qPCR to measure changes in expression of eight randomly selected miRNAs on chromosomes 8, 9, 11 , 13, 17, 22, and X before and after AG02 silencing. All the miRNAs were down- regulated after AG02 silencing in both cell lines (Fig. 7A). Expression of AG02 did not, however, significantly correlate with total expression of miRNAs in the primary tumor samples (P > .05). This suggested that AG02 might not be the only factor regulating global expression of miRNAs in myeloma cells.

Cell survival experiments showed that AG02 silencing did not inhibit cell proliferation until day 3 in H929 cells and day 4 in OCI-My5 cells; nevertheless, a dramatic decrease in cell viability was observed in cells expressing AG02 shRNA (on day 4 in H929 cells and day 5 in OCI-My5 cells), as compared to control cells (58%-60% vs. 93%-96%; P < .05). On day 6, most cells expressing AG02 shRNA had died, but control cells continued to proliferate (Fig. 7B).

The effects of silencing AG02 on cell-cycle progression were investigated by using flow cytometry analysis on day 4 in H929 cells and on day 5 in OCI-My5 cells after doxycycline induction. Silencing of AG02 significantly enhanced G₀ to phase accumulation and led to cell-cycle arrest in both myeloma cell lines (Fig. 7C). Western blotting was used to examine effects of AG02 silencing on cell-cycle proteins p21Waf1/Cip1 , p27Kip1 , CDK2, and CCND1 (Fig. 7D). Silencing of AG02 enhanced protein expression of cyclin-dependent kinase inhibitors p21Waf1/Cip1 and p27Kip1. These proteins might then inhibit CDK2 and CCND1 expression, which are important for the Gi- to S-phase transition. Our results suggested that AG02 silencing resulted in enhanced cell-cycle arrest that was mediated by dysregulated p21Waf1/Cip1 , p27 Kip1 , CDK2, and CCND1. Induction of p21Waf1/Cip1 expression might result from decreased activity of miR-106a, miR-106b, miR-17-5p, and miR-20b following AG02 silencing, as discussed above.

Flow cytometric cell-cycle analysis showed that silencing of AG02 increased sub-G₀ DNA content and induced strong apoptosis compared to that in controls (47.9% vs. 1.33% in H929; 47.39% vs. 1.17% in OCI-My5; Fig. 7C). Western blot analyses of apoptotic mechanisms showed strong activation of caspase-3, -8 and, -9 in cells expressing AG02 shRNA (Fig. 7D), which indicated that AG02 knockdown induced apoptosis via activation of caspase signaling. The results suggested that silencing of AG02 induced apoptosis mediated by activation of caspase-3, -8, and -9.

To further validate that total miRNA expression elevation drives MM disease progression, we knocked down DICER1, another master regulator of miRNA genesis that cleaves double-stranded RNA precursors, generating short RNAs that are then transferred to Argonaute proteins (34). Two DICER1 shRNAs were used to knockdown DICER1 in OCI-My5. Western blots confirmed DICER1 knockdown in OCI-My5 (Fig. 8A). Similar to the results of AG02 knockdown, DICER1 knockdown decreased the viability of the cells, significantly enhanced G₀- to G phase accumulation, led to cell-cycle arrest, and greatly increased apoptosis (Figs. 8B-8C).

The following references were cited herein:

1. Bartel, D. P. (2004) Cell 116: 281-297.

2. Fabbri, M., Croce, C. M., & Calin, G (2009) Leuk Lymphoma 50: 160-170.

3. Visone, R. & Croce, C. M. (2009) Am J Pathol 174: 1131 -1138.

4. Bartels, C. L. & Tsongalis, G. J. (2009) Clin Chem 55: 623-631.

5. Calin, G. A. & Croce, C. M. (2006) Nat Rev Cancer 6: 857-866.

6. lorio, et al. (2008) Eur J Cancer 44: 2753-2759.

7. Krutzfeldt, et al. (2005) Nature 438: 685-689.

8. Czech, M. P. (2006) N Engl J Med 354: 1194-1195.

9. Medina, P. P. & Slack, F. J. (2009) Nat Methods 6: 37-38.

10. Barlogie, et al. (2005) in Williams Hematology, ed. Marshall Al Lichtman et al. (McGraw-Hill Professional, New York), pp. 1501-1533.

11. Loffler, et al. (2007) Blood 110: 1330-1333.

12. Pichiorri, et al. (2008) Proc Natl Acad Sci U S A 105: 12885-12890.

13. Roccaro, et al. (2009) Blood 113: 6669-6680.

14. Lionetti, et al. (2009) Genes Chromosomes Cancer 48: 521-531.

15. Nicoloso, et al. (2007). Br J Haematol 139: 709-716.

16. Diederichs, S. & Haber, D. A. (2007) Cell 131 : 1097-1108.

17. Liu, et al. (2004) Science 305: 1437-1441.

18. O'Carroll, (2007) Genes Dev 21 : 1999-2004.

19. Griffiths-Jones, et al. (2008) Nucleic Acids Res 36: D154-158.

20. Tusher, et al. (2001) Proc Natl Acad Sci U S A 98: 5116-5121.

21. Shaughnessy, et al. (2000) Blood 96: 1505-1511.

22. Tricot, et al. (1995) Blood 86: 4250-4256.

23. Shaughnessy, et al. (2007) Blood 109: 2276-2284.

24. Subramanian, et al. (2005) Proc Natl Acad Sci U S A 102: 15545-15550.

25. Rhodes, et al. (2004) Proc Natl Acad Sci U S A 101 : 9309-9314.

26. Tarte, et al. (2003) Blood 102: 592-600.

27. Ramalho-Santos, et al. (2002) Science 298: 597-600.

28. Zhan, er al. (2006) Blood 108: 2020-2028.

29. Sethupathy, et al. (2006) Rna 12: 192-197.

30. Chen, et al. (1999) Blood 94: 251-259.

31. Lavelle, et al. (2001) Am J Hematol 68: 170-178. 32. Stewart, et al. (2004) Br J Haematol 126: 72-76.

33. Zhou, et al. (2008) Blood (ASH Annual Meeting Abstracts) 12: 250.

34. Jinek, M. & Doudna, J. A. (2009) Nature 457: 405-412.

35. Carrasco, et al. (2006) Cancer Cell 9: 313-325.

36. O'Donnell, et al. (2005) Nature 435: 839-843.

37. Chang, et al. (2008) Nat Genet 40: 43-50.

38. Kumar, et al. (2007) Nat Genet 39: 673-677.

39. Merritt, et al. (2008) N Engl J Med 359: 2641-2650.

40. Tarte, et al. (2002) Blood 100: 1113-1122.

41. Sarkar, et al. (2009) Nucleic Acids Res 37: e17.

42. Hua, et al. (2008) Genomics 92: 122-128.

43. Peltier, H. J. & Latham, G. J. (2008) Rna 14: 844-852.

44. Pradervand, et al. (2009) Rna 15: 493-501.

45. Rao, et al. (2008) Stat Appl Genet Mol Biol 7: Article22.

46. Subramanian, et al. (2005) Proc Natl Acad Sci U S A 102: 15545-15550.

47. Shaughnessy, et al. (2007) Blood 109: 2276-2284.

48. Zhan, et al. (2006) The molecular classification of multiple myeloma. Blood 108: 2020-2028.

Any patents or publications mentioned in this specification are indicative of the levels of those skilled in the art to which the invention pertains. Further, these patents and publications are incorporated by reference herein to the same extent as if each individual publication was specifically and individually incorporated by reference.

One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The present examples along with the methods, procedures, treatments, molecules, and specific compounds described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses will occur to those skilled in the art which are encompassed within the spirit of the invention as defined by the scope of the claims.

Claims

WHAT IS CLAIMED IS:

1. A diagnostic or prognostic indicator of multiple myeloma in a subject, comprising:

a global expression profile of total miRNA in the subject, wherein a pattern of 39 up-regulated and 1 down-regulated miRNAs indicates a diagnosis of multiple myeloma in the patient or is determinative of the subject's prognosis.

2. The diagnostic or prognostic indicator of claim 1 , wherein the 39 up- regulated miRNAs are hsa-miR-30b, hsa-miR-19, hsa-miR-22, hsa-miR-766, hsa-miR-513, hsa-miR-574, hsa-miR-197, hsa-miR-125b, hsa-miR-365, hsa-miR-487b, hsa-miR-67,1 hsa-miR-181a, hsa-miR-765, hsa-miR-138, hsa-miR-30a-5p, hsa-miR-212, hsa-miR-99a, hsa-let-7c, hsa-miR-30d, hsa-miR-150, hsa-miR-181 b, hsa-miR-181d, hsa-miR-195, hsa- miR-198, hsa-miR-324-3p, hsa-miR-23a, hsa-miR-188, hsa-miR-222, hsa-miR-509, hsa- miR-623, hsa-let-7e, hsa-miR-202, hsa-miR-57,5 hsa-let-7b, hsa-miR-25, hsa-miR-92, hsa- miR-15,b hsa-miR-328, and hsa-let-7d.

3. The diagnostic or prognostic indicator of claim 1 , wherein the down- regulated miRNA is hsa-miR-370.

4. The diagnostic or prognostic indicator of claim 1 , wherein the global expression profile comprises a microarray.

5. The diagnostic or prognostic indicator of claim 1 , wherein a high mean expression level of the miRNAs compared to a control from a healthy subject is indicative of a poor prognosis.

6. A method for diagnosing multiple myeloma in a subject, comprising: obtaining a biological sample from the subject; and

determining a global expression profile of all miRNAs in the sample; wherein a pattern of 39 up-regulated and 1 down-regulated specific miRNAs of claim 1 is a diagnostic indicator of multiple myeloma.

7. The method of claim 6, further comprising comparing one or both of a risk score or proliferation index to the miRNAs expression profile as prognosis for survival of the subject, wherein a positive correlation of a high risk score and a high proliferation index with the expression profile is indicative of a poor prognosis for the patient.

8. The method of claim 7, wherein determining risk comprises calculating the average log2-scale expression of 51 up-regulated genes minus the average log2-scale expression of 19 down-regulated genes.

9. The method of claim 8, wherein the group of up-regulated genes are 202345_s_at, 1555864_s_at, 204033_at, 206513_at, 1555274_a_at, 211576_s_at, 204016_at, 1565951_s_at, 219918_s_at, 201947_s_at, 213535_s_at, 204092_s_at, 213607_x_at, 2081 17_s_at, 210334_x_at, 204023_at, 201897_s_at, 216194_s_at, 225834_at, 238952_x_at, 200634_at, 208931_s_at, 206332_s_at, 220789_s_at, 218947_s_at, 213310_at, 224523_s_at, 201231_s_at, 217901_at, 226936_at, 58696_at, 200916_at, 201614_s_at, 200966_x_at, 225082_at, 242488_at, 243011_at, 201 105_at, 224200_s_at, 222417_s_at, 210460_s_at, 200750_s_at, 206364_at, 201091_s_at, 203432_at, 221970_s_at, 212533_at, 213194_at, 244686_at, 200638_s_at, and 205235_s_at.

10. The method of claim 8, wherein the group of down-regulated genes are 201921_at, 227278_at, 209740_s_at, 227547_at, 225582_at, 200850_s_at,

213628_at, 209717_at, 222495_at, 1557277_a_at, 1554736_at, 218924_s_at, 226954_at, 202838_at, 230192_at, 48106_at, 237964_at, 202729_s_at, and 212435_at.

11. The method of claim 7, wherein the proliferation index is based on the up-regulation of the 11 genes TOP2A, BIRC5, CCNB2, NEK2, ANAPC7, STK6, BUB1,

CDC2, C10orf3, ASPM, and CDCA1.

12. The method of claim 6, wherein the biological sample is bone marrow.

13. A method for determining prognosis of a subject with multiple myeloma, comprising:

obtaining a biological sample from the subject;

determining an expression profile of 40 specific miRNAs within an expression profile of total mRNAs in the sample; and calculating a risk score and proliferation index based on the up- and down- regulation of a group of 70 genes comprising the sample: wherein up-regulation of 39 mRNAs and down-regulation of 1 mRNA and a high risk score and proliferation index in the subject compared to a healthy control is indicative of poor prognosis.

14. The method of claim 13, wherein the 39 up-regulated miRNAs are hsa-miR-30b, hsa-miR-19, hsa-miR-22, hsa-miR-766, hsa-miR-513, hsa-miR-574, hsa- miR-197, hsa-miR-125b, hsa-miR-365, hsa-miR-487b, hsa-miR-67,1 hsa-miR-181a, hsa- miR-765, hsa-miR-138, hsa-miR-30a-5p, hsa-miR-212, hsa-miR-99a, hsa-let-7c, hsa-miR- 30d, hsa-miR-150, hsa-miR-181 b, hsa-miR-181d, hsa-miR-195, hsa-miR-198, hsa-miR- 324-3p, hsa-miR-23a, hsa-miR-188, hsa-miR-222, hsa-miR-509, hsa-miR-623, hsa-let-7e, hsa-miR-202, hsa-miR-57,5 hsa-let-7b, hsa-miR-25, hsa-miR-92, hsa-miR-15,b hsa-miR- 328, and hsa-let-7d.

15. The method of claim 13, wherein the down-regulated miRNA is has- miR-370.

16. The method of claim 13, wherein determining risk comprises calculating the average log2-scale expression of 51 up-regulated genes minus the average log2-scale expression of 19 down-regulated genes.

17. The method of claim 16, wherein the group of up-regulated genes are 202345_s_at, 1555864_s_at, 204033_at, 206513_at, 1555274_a_at, 21 1576_s_at, 204016_at, 1565951_s_at, 219918_s_at, 201947_s_at, 213535_s_at, 204092_s_at, 213607_x_at, 208117_s_at, 210334_x_at, 204023_at, 201897_s_at, 216194_s_at, 225834_at, 238952_x_at, 200634_at, 208931 _s_at, 206332_s_at, 220789_s_at, 218947_s_at, 213310_at, 224523_s_at, 201231_s_at, 217901_at, 226936_at, 58696_at, 200916_at, 201614_s_at, 200966_x_at, 225082_at, 242488_at, 24301 1_at, 201105_at, 224200_s_at, 222417_s_at, 210460_s_at, 200750_s_at, 206364_at, 201091_s_at, 203432_at, 221970_s_at, 212533_at, 213194_at, 244686_at, 200638_s_at, 205235_s_at.

18. The method of claim 16, wherein the group of down-regulated genes are 201921_at, 227278_at, 209740_s_at, 227547_at, 225582_at, 200850_s_at, 213628_at, 209717_at, 222495_at, 1557277_a_at, 1554736_at, 218924_s_at, 226954_at, 202838_at, 230192_at, 48106_at, 237964_at, 202729_s_at, 212435_at.

19. The method of claim 16, wherein the proliferation index is based on the up-regulation of the 11 genes TOP2A, BIRC5, CCNB2, NEK2, ANAPC7, STK6, BUB1, CDC2, C10orf3, ASPM, and CDCA1.

20. The method of claim 13, wherein the biological sample is bone marrow.

21. A method for treating a subject having multiple myeloma, comprising:

administering to the subject one or more therapeutic compounds that inhibits a miRNA maturation pathway in the subject.

22. The method of claim 21 , wherein inhibition of miRNA maturation comprises inhibiting expression of one or both of AG02 or DICER1 genes at the nucleic acid or protein level.

23. The method of claim 21 , wherein the therapeutic inhibitor is a shRNA, an antibody or other small molecule inhibitor.

24. A method for screening for therapeutic compounds useful in treating high- risk myeloma, comprising:

contacting a biological sample comprising one or both of AG02 or DICER1 with a potential inhibitory compound; and

determining the inhibitory effect of the compound on one or both of an expression or activity of AG02 or DICER1 or on miRNA maturation, wherein a decrease in expression or maturation levels is indicative of an inhibitory activity of the therapeutic against high-risk multiple myeloma.

25. The method of claim 24, wherein the biological sample is bone marrow.

26. The method of claim 24, wherein the therapeutic inhibitor is a shRNA, antibody or other small molecule inhibitor.