Patent Application
20150218654
The present invention relates to methods for predicting multiple myeloma treatment response.
Malignant transformation requires oncogenic activation and inactivation of tumor suppressor genes, which help cancer cells overriding the normal mechanisms controlling cellular survival and proliferation (1,2). These molecular events are caused by genetic alterations (translocations, amplification, mutations) and also by epigenetic modifications (3). Epigenetic modifications include methylation of DNA cytosine residues and histone modifications and have been shown to be critical in the initiation and progression of cancers (4). DNA methyltransferase inhibitors or HDAC inhibitors are now being used in the treatment of several hematologic malignancies including MM (5-8).
Multiple myeloma (MM) is a plasma cell neoplasm characterized by the accumulation of malignant plasma cells, termed Multiple Myeloma Cells (MMCs) within the bone marrow (BM). Despite the recent introduction of therapies such as Lenalidomide and Bortezomib, MM remains an almost incurable disease. MM arises through the accumulation of multiple genetic changes that include an aberrant or overexpression of a D-type cyclin gene, cyclin D1 (CCND1) in the case of t(11;14) translocation or gain in 11q13, cyclin D3 (CCND3) in the case of the rare t(6;14) translocation, or cyclin D2 (CCND2) on the background of a translocation involving c-maf (414;16)) or MMSET/FGFR3 (t(4;14)) (9,10).
Recent studies have shown that epigenetic changes such as DNA methylation play a role by silencing various cancer-related genes in MM. Most of these studies have been performed on limited number of genes using methylation specific PCR (11-18). Among the genes identified with promoter hypermethylation in MM, cyclin-dependent kinase inhibitor 2A (CDKN2A) and transforming growth factor beta receptor 2 (TGFBR2) have been shown to be associated with a poor prognosis in MM patients with discrepant results for CDKN2A (12). Heller at al. have identified several epigenetic inactivated cancer related genes in 3 human myeloma cell lines (HMCLs) and validated the relevance of 10 of these genes in 6 additional HMCLs, premalignant PCs from 24 MGUS patients and MMCs from 111 patients with MM (19). Methylation of secreted protein acidic and rich in cysteine (SPARC) and Bcl2/adenovirus E1B 19 kDa interacting protein 3 (BNIP3) promoters was associated with poor overall survival of MM patients (19). SOCS3 methylation was found to be associated with extramedullary manifestations, plasma cell leukemia and significant shortened survival in MM patients (20). More recently, Morgan et al have shown that the transition of normal plasma cell (PC) and MGUS stage to MM stage is associated with DNA hypomethylation, but the transition of intramedullary MM stage to plasma cell leukemia or HMCL stage is associated with DNA hypermethylation (21). They described 2 specific subgroups of hyperdiploid MM on the basis of their methylation profile, which had a significantly different overall survival (21).
DNMT inhibitors can be sub-divided into nucleoside analogue and non-nucleoside analogue families. 5-Azacytidine (azacytidine)/5-Aza-2′-deoxycytidine (decitabine) are both nucleoside analogues with Food and Drug Administration approval for use in myelodysplastic syndrome. Clinical trials in myeloma combining these demethylating agents with chemotherapy or other agents are underway (8).
An important objective for optimizing these clinical trials will be the identification of biomarkers predictive for sensitivity of MMCs to DNMTi. In the present invention, the inventors used gene expression profiling of Multiple Myeloma Cells (MMCs) to build a novel “DNA methylation gene expression score” that makes it possible identification of patients whose MMCs will be targeted by DNMT inhibition.
The present invention relates to a method of testing whether a patient suffering of multiple myeloma will respond or not to a DNA methyltransferase inhibitor (DNMTi).
The multiple myeloma treatment response was investigated by the inventors using DNA methyltransferase inhibitors (DNMTi), human multiple myeloma cell lines and primary myeloma cells. The inventors analyzed gene expression profiles of 5 MM cell lines treated with decitabine using microarrays. The inventors identified 127 genes deregulated by decitabine. 47 out of 127 decitabine deregulated genes have prognostic value in inventor's cohort of 206 newly-diagnosed MM patients. The inventors summarized the prognostic information in a DNA methylation score (DM Score or DMS) built using the probe set signal value weighted by the beta coefficient of prognostic genes. The DM Score is predictive for DNMT inhibitor sensitivity of HMCLs and primary myeloma cells in vitro. The DM Score allows identification of myeloma patients that could benefit DNMT inhibitor treatment.
The inventors also demonstrated that the DM Score is also predictive of myeloma cells sensitivity for another DNMTi, 5-Azacytidine. HMCLs with a high DM Score exhibited significant 3-fold higher 5-Azacytidine sensitivity than HMCLs with a low DM Score and these results were validated in vitro with primary myeloma cells of patients.
The term “patient” denotes a mammal. In a preferred embodiment of the invention, a patient refers to any patient (preferably human) afflicted with multiple myeloma. The term “multiple myeloma” refers to multiple myeloma such as revised in the World Health Organisation Classification C90.
The term “DNA methyltransferase inhibitors” or “DNMTi” has its general meaning in the art and refers to a multiple myeloma treatment. The term “DNA methyltransferase inhibitors” or “DNMTi” refers to DNA methyltransferase inhibitor that can be sub-divided into nucleoside analogue (5-Azacytidine (azacytidine), 5-Aza-2′-deoxycytidine (decitabine, 5-Aza-CdR), zebularine, 5-Fluoro-2′-deoxycytidine (5-F-CdR), 5,6-Dihydro-5-azacytidine (DHAC)) and non-nucleoside analogue families (Hydralazine, Procainamide, Procaine, EGCG ((−)-epigallocatechin-3-gallate), Psammaplin A, MG98, RG108) (8).
The term “biological sample” refers to multiple myeloma cells, bone marrow or medullary cell.
All the genes pertaining to the invention are known per se, and are listed in the below Table A.
The present invention relates to a method of testing whether a patient suffering of multiple myeloma will respond or not to a DNA methyltransferase inhibitor (DNMTi) comprising:
wherein βi represent the regression β coefficient reference value for the gene Gi and Ci=1 if the expression of the gene Gi (ELi) is higher than the predetermined reference level (ELRi) or Ci=−1 if the expression of the gene (ELi) is lower than or equal to the predetermined reference level (ELRi)
In some embodiments, the levels of at least 25 genes from Table A are determined wherein said genes are:
202061_s_at SEL1L
202304_at FNDC3A
202558_s_at STCH
203397_s_at GALNT3
204079_at TPST2
205483_s_at G1P2
205552_s_at OAS1
205718_at ITGB7
208725_at EIF2S2
209083_at CORO1A
209118_s_at TUBA3
209122_at ADFP
209417_s_at IF135
211990_at HLA-DPA1
214152_at CCPG1
214179_s_at NFE2L1
219118_at FKBP11
221658_s_at IL21R
222690_s at TMEM39A
223129_x_at MYLIP
224451_x_at ARHGAP9
224763_at RPL37
227755_at EST, GenBank AA042983
228831_s_at GNG7
232488_at MGC15875
In some embodiment, the level of 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, or 47 genes from Table A are determined wherein every combinations of genes comprises a minimal set of 25 genes consisting of:
202061_s_at SEL1L
202304_at FNDC3A
202558_s_at STCH
203397_s_at GALNT3
204079_at TPST2
205483_s_at G1P2
205552_s_at OAS1
205718_at ITGB7
208725_at EIF2S2
209083_at CORO1A
209118_s_at TUBA3
209122_at ADFP
209417_s_at IF135
211990_at HLA-DPA1
214152_at CCPG1
214179_s_at NFE2L1
219118_at FKBP11
221658_s_at IL21R
222690_s_at TMEM39A
223129_x at MYLIP
22445 l_x_at ARHGAP9
224763_at RPL37
227755_at EST, GenBank AA042983
228831_s at GNG7
232488_at MGC15875
In some embodiments, the level of the 47 genes of Table A are determined
Determination of the expression level of the genes can be performed by a variety of techniques. Generally, the expression level as determined is a relative expression level. More preferably, the determination comprises contacting the biological sample with selective reagents such as probes, primers or ligands, and thereby detecting the presence, or measuring the amount, of polypeptide or nucleic acids of interest originally in the biological sample. Contacting may be performed in any suitable device, such as a plate, microtiter dish, test tube, well, glass, column, and so forth. In specific embodiments, the contacting is performed on a substrate coated with the reagent, such as a nucleic acid array or a specific ligand array. The substrate may be a solid or semi-solid substrate such as any suitable support comprising glass, plastic, nylon, paper, metal, polymers and the like. The substrate may be of various forms and sizes, such as a slide, a membrane, a bead, a column, a gel, etc. The contacting may be made under any condition suitable for a detectable complex, such as a nucleic acid hybrid or an antibody-antigen complex, to be formed between the reagent and the nucleic acids or polypeptides of the biological sample.
In a preferred embodiment, the expression level may be determined by determining the quantity of mRNA.
Methods for determining the quantity of mRNA are well known in the art. For example the nucleic acid contained in the biological sample is first extracted according to standard methods, for example using lytic enzymes or chemical solutions or extracted by nucleic-acid-binding resins following the manufacturer's instructions. The extracted mRNA is then detected by hybridization (e.g., Northern blot analysis) and/or amplification (e.g., RT-PCR). Preferably quantitative or semi-quantitative RT-PCR is preferred. Real-time quantitative or semi-quantitative RT-PCR is particularly advantageous.
Other methods of amplification include ligase chain reaction (LCR), transcription-mediated amplification (TMA), strand displacement amplification (SDA) and nucleic acid sequence based amplification (NASBA).
Nucleic acids having at least 10 nucleotides and exhibiting sequence complementarity or homology to the mRNA of interest herein find utility as hybridization probes or amplification primers. It is understood that such nucleic acids need not be identical, but are typically at least about 80% identical to the homologous region of comparable size, more preferably 85% identical and even more preferably 90-95% identical. In certain embodiments, it will be advantageous to use nucleic acids in combination with appropriate means, such as a detectable label, for detecting hybridization. A wide variety of appropriate indicators are known in the art including, fluorescent, radioactive, enzymatic or other ligands (e.g. avidin/biotin).
Probes typically comprise single-stranded nucleic acids of between 10 to 1000 nucleotides in length, for instance of between 10 and 800, more preferably of between 15 and 700, typically of between 20 and 500. Primers typically are shorter single-stranded nucleic acids, of between 10 to 25 nucleotides in length, designed to perfectly or almost perfectly match a nucleic acid of interest, to be amplified. The probes and primers are “specific” to the nucleic acids they hybridize to, i.e. they preferably hybridize under high stringency hybridization conditions (corresponding to the highest melting temperature Tm, e.g., 50% formamide, 5× or 6×SCC. SCC is a 0.15 M NaCl, 0.015 M Na-citrate).
The nucleic acid primers or probes used in the above amplification and detection method may be assembled as a kit. Such a kit includes consensus primers and molecular probes. A preferred kit also includes the components necessary to determine if amplification has occurred. The kit may also include, for example, PCR buffers and enzymes; positive control sequences, reaction control primers; and instructions for amplifying and detecting the specific sequences.
In a particular embodiment, the methods of the invention comprise the steps of providing total RNAs extracted from a biological samples and subjecting the RNAs to amplification and hybridization to specific probes, more particularly by means of a quantitative or semi-quantitative RT-PCR.
In another preferred embodiment, the expression level is determined by DNA chip analysis. Such DNA chip or nucleic acid microarray consists of different nucleic acid probes that are chemically attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead. A microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose. Probes comprise nucleic acids such as cDNAs or oligonucleotides that may be about 10 to about 60 base pairs. To determine the expression level, a biological sample from a test patient, optionally first subjected to a reverse transcription, is labelled and contacted with the microarray in hybridization conditions, leading to the formation of complexes between target nucleic acids that are complementary to probe sequences attached to the microarray surface. The labelled hybridized complexes are then detected and can be quantified or semi-quantified. Labelling may be achieved by various methods, e.g. by using radioactive or fluorescent labelling. Many variants of the microarray hybridization technology are available to the man skilled in the art (see e.g. the review by Hoheisel, Nature Reviews, Genetics, 2006, 7:200-210)
In this context, the invention further provides a DNA chip comprising a solid support which carries nucleic acids that are specific to the genes listed in table A.
Predetermined reference values ELRi or DMSR used for comparison may consist of “cut-off” values.
For example; each reference (“cut-off”) value ELRi for each gene may be determined by carrying out a method comprising the steps of:
a) providing a collection of samples from patients suffering of multiple myeloma;
b) determining the expression level of the relevant gene for each sample contained in the collection provided at step a);
c) ranking the samples according to said expression level
d) classifying said samples in pairs of subsets of increasing, respectively decreasing, number of members ranked according to their expression level,
e) providing, for each sample provided at step a), information relating to the actual clinical outcome for the corresponding cancer patient (i.e. the duration of the disease-free survival (DFS) or the overall survival (OS) or both);
f) for each pair of subsets of tumour tissue samples, obtaining a Kaplan Meier percentage of survival curve;
g) for each pair of subsets of tumour tissue samples calculating the statistical significance (p value) between both subsets
h) selecting as reference value ELR for the expression level, the value of expression level for which the p value is the smallest.
For example the expression level of a gene Gi has been assessed for 100 samples of 100 patients. The 100 samples are ranked according to the expression level of gene Gi. Sample 1 has the highest expression level and sample 100 has the lowest expression level. A first grouping provides two subsets: on one side sample Nr 1 and on the other side the 99 other samples. The next grouping provides on one side samples 1 and 2 and on the other side the 98 remaining samples etc., until the last grouping: on one side samples 1 to 99 and on the other side sample Nr 100. According to the information relating to the actual clinical outcome for the corresponding cancer patient, Kaplan Meier curves are prepared for each of the 99 groups of two subsets. Also for each of the 99 groups, the p value between both subsets was calculated. The reference value ELRi is then selected such as the discrimination based on the criterion of the minimum p value is the strongest. In other terms, the expression level corresponding to the boundary between both subsets for which the p value is minimum is considered as the reference value. It should be noted that according to the experiments made by the inventors, the reference value ELRi is not necessarily the median value of expression levels.
The man skilled in the art also understands that the same technique of assessment of the DMSR could be used for obtaining the reference value and thereafter for assessment of the response to DNMTi. However in one embodiment, the reference value DMSR is the median value of DMS.
In one embodiment, the reference value ELRi for the genes are described in table A (right column).
In one embodiment, the reference value DMSR is −19.7 for determining whether a patient suffering of multiple myeloma will respond to a DNMTi or −15.3 for predicting the survival time of patient suffering of multiple myeloma.
The regression β coefficient reference values may be easily determined by the skilled man in the art for each gene using a Cox model. The Cox model is based on a modeling approach to the analysis of survival data. The purpose of the model is to simultaneously explore the effects of several variables on survival. The Cox model is a well-recognised statistical technique for analysing survival data. When it is used to analyse the survival of patients in a clinical trial, the model allows us to isolate the effects of treatment from the effects of other variables. The logrank test cannot be used to explore (and adjust for) the effects of several variables, such as age and disease duration, known to affect survival. Adjustment for variables that are known to affect survival may improve the precision with which we can estimate the treatment effect. The regression method introduced by Cox is used to investigate several variables at a time. It is also known as proportional hazards regression analysis. Briefly, the procedure models or regresses the survival times (or more specifically, the so-called hazard function) on the explanatory variables. The hazard function is the probability that an individual will experience an event (for example, death) within a small time interval, given that the individual has survived up to the beginning of the interval. It can therefore be interpreted as the risk of dying at time t. The quantity h0 (t) is the baseline or underlying hazard function and corresponds to the probability of dying (or reaching an event) when all the explanatory variables are zero. The baseline hazard function is analogous to the intercept in ordinary regression (since exp0=1). The regression coefficient β gives the proportional change that can be expected in the hazard, related to changes in the explanatory variables. The coefficient β is estimated by a statistical method called maximum likelihood. In survival analysis, the hazard ratio (HR) (Hazard Ratio=exp(β)) is the ratio of the hazard rates corresponding to the conditions described by two sets of explanatory variables. For example, in a drug study, the treated population may die at twice the rate per unit time as the control population. The hazard ratio would be 2, indicating higher hazard of death from the treatment.
In one embodiment, the regression β coefficient reference values are described in table A.
The invention also relates to a kit for performing the methods as above described, wherein said kit comprises means for measuring the expression level of the genes listed in Table A. Typically the kit may include a primer, a set of primers, a probe, a set of probes as above described. In a particular embodiment, the probe or set of probes are labelled as above described. The kit may also contain other suitably packaged reagents and materials needed for the particular detection protocol, including solid-phase matrices, if applicable, and standards.
In a particular embodiment, the score may be generated by a computer program.
The method of the invention allows to define a subgroup of patients who will be responsive (“responder”) or not (“non responder”) to the treatment with a DNA methyltransferase inhibitor.
A further object of the invention relates to a method for the treatment of multiple myeloma in a patient in need thereof
In the context of the invention, the term “treating” or “treatment”, as used herein, means reversing, alleviating, inhibiting the progress of, or preventing the disorder or condition to which such term applies, or one or more symptoms of such disorder or condition.
In a particular embodiment, the method comprises the following steps
a) testing whether the patient will respond or not to a DNA methyltransferase inhibitor (DNMTi) by performing the method according to the invention
b) administering the DNA methyltransferase inhibitor, if said patient has as score higher than the reference value DMSR (i.e. the patient will respond to the DNA methyltransferase inhibitor).
A further object of the invention relates to a DNA methyltransferase inhibitor for use in the treatment of multiple myeloma in a patient in need thereof, wherein the patient was being classified as responder by the method as above described.
The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.
(A) DNA methylation Score in normal bone marrow plasma cells (N=7), in premalignant plasma cells of patients with monoclonal gammopathy of undetermined significance (MGUS, N=5), in multiple myeloma cells of patients with intramedullary MM (N=206) and in human myeloma cell lines (N=40). ** Indicate that the score value is significantly different with a P value <0.01. (B) DNA methylation Score in the 8 groups of the molecular classification of multiple myeloma. The DM Score was investigated in the 8 groups of the molecular classification of multiple myeloma in UAMS-TT2 cohort of patients. PR: proliferation, LB: low bone disease, MS: MMSET, HY: hyperdiploid, CD1: Cyclin D1, CD2: Cyclin D2, MF: MAF, MY: myeloid. * Indicate that the score value is significantly higher in the group compared to all the patients of the cohort (P<0.05). ** Indicate that the score value is significantly lower in the group compared to all the patients of the cohort (P<0.05).
Patients of HM cohort were ranked according to increased DM Score and a maximum difference in OS was obtained with DM Score=−15.3 splitting patients in a high risk (34.5%) and low risk (65.5%) groups. The prognostic value of DM Score was tested on an independent cohort of 345 patients from UAMS treated with TT2 therapy (UAMS- TT2 cohort). The parameters to compute the DM Score of patients of UAMS-TT2 cohort and the DM Score cut-off delineating the 2 prognostic groups were those defined with HM cohort.
(A) HMCLs with high DM Score (N=5) exhibit significant higher DNMTi sensitivity compared to HMCLs with low DM Score (N=5). HMCLs were cultured for 4 days in 96-well flat-bottom microtiter plates in RPMI 1640 medium, 10% FCS, 2 ng/ml IL-6 culture medium (control), with graded decitabine concentrations. Data are mean values plus or minus standard deviation (SD) of 5 experiments determined on sextuplet culture wells.
Mononuclear cells from tumor samples of 12 patients with MM were cultured for 4 days in the presence of IL-6 (2 ng/ml) with or without graded decitabine concentrations. At day 4 of culture, the cell count and the viability were determined and the percentage of CD138+ viable plasma cells was determined by flow cytometry. Black color represents patients with high DM Score (N=6) and white represents patients with low DM Score values (N=6).
HMCLs with a high DM Score (n=6) exhibit significant higher 5-azacitidine sensitivity compared to HMCLs with a low DM Score (n=6). HMCLs were cultured for 4 days in 96-well flat-bottom microtiter plates in RPMI 1640 medium, 10% FCS, 2 ng/ml IL-6 culture medium (control), and graded 5-azacitidine concentrations. Data are mean values plus or minus standard deviation (SD) of 5 experiments determined on sextuplet culture wells.
Mononuclear cells from tumor samples of 14 patients with MM were cultured for 4 days in the presence of IL-6 (2 ng/ml) with or without graded 5-azacitidine concentrations. At day 4 of culture, the count of viable CD138′ MMCs was determined using flow cytometry. The black columns represent the mean±SD of primary myeloma cell counts (expressed as the percentage of the count without adding 5-azcytidine) of the 7 patients with a low DM Score and the white columns that of the 7 patients with a high DM Score.
Material & Methods
Human Myeloma Cell Lines (HMCLs)
XG-1, XG-2, XG-3, XG-4, XG-5, XG-6, XG-7, XG-10, XG-11, XG-12, XG-13, XG-14, XG-16, XG-19, XG-20 and XG-21 human myeloma cell lines were obtained as previously described (22-26). JJN3 was kindly provided by Dr Van Riet (Bruxelles, Belgium), JIM3 by Dr MacLennan (Birmingham, UK) and MM1S by Dr S. Rosen (Chicago, USA). AMO-1, LP1, L363, U266, OPM2, and SKMM2 were from DSMZ (Germany) and RPMI8226 from ATTC (USA). All HMCLs derived in inventor's laboratory were cultured in the presence of recombinant IL-6. HMCLs microarray data have been deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088.
Primary Multiple Myeloma Cells
MMCs were purified from 206 patients with newly-diagnosed MM after written informed consent was given at the University hospitals of Heidelberg (Germany) or Montpellier (France) and agreement of the Center for Biological Resources of Montpellier University Hospital (N° DC-2008-417). The study was approved by the ethics boards of Heidelberg and Montpellier Universities. These 206 patients were treated with high dose Melphalan (HDM) and autologous stem cell transplantation (ASCT) (27) and were termed in the following Heidelberg-Montpellier (HM) series (Supplementary Table S1). The CEL files and MASS files have been deposited in the ArrayExpress public database (E-MTAB-372). The structural chromosomal aberrations including t(4;14)(p16.3;q32.3) and t(11;14)(q13;q32.3), as well as numerical aberrations including 17p13 and 1q21 gain, were assayed by fluorescence in situ hybridization (iFISH) (28). The inventors also used Affymetrix data of a cohort of 345 purified MMC from previously untreated patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, Ark.). The patients were treated with total therapy 2 including HDM and ASCT (29) and termed in the following UAMS-TT2 series. These data are publicly available via the online Gene Expression Omnibus (Gene Expression Profile of Multiple Myeloma, accession number GSE2658. http://www.ncbi.nlm nih.gov/geo/). As iFISH data were not available for UAMS-TT2 patients, t(4;14) translocation was evaluated using MMSET spike expression (30) and dell7p13 surrogated by TP53 probe set signal (31). After Ficoll-density gradient centrifugation, plasma cells were purified using anti-CD138 MACS microbeads (Miltenyi Biotech, Bergisch Gladbach, Germany).
Cell Culture and Treatment for Gene Expression Profiling
The human MM cell lines XG-5, XG-6, XG-7, XG-20 and LP1 were grown in RPMI 1640 supplemented with 10% fetal bovine serum and 2 ng/ml recombinant IL-6. Cells (2×105/mL) were treated either with 0.5 μmol/L 5-Aza-2′-deoxycytidine (decitabine) (Sigma, St. Louis, Mo.) for 7 days. Control cells were cultured in the same conditions without adding drug.
Growth Assay for Myeloma Cells
HMCLs were cultured for 4 days in 96-well flat-bottom microtiter plates in RPMI 1640 medium, 10% FCS, 2 ng/ml IL-6 culture medium (control), with graded decitabine concentrations. Cell growth was evaluated by quantifying intracellular ATP amount with a Cell Titer Glo Luminescent Assay (Promega, Madison, Wis.) with a Centro LB 960 luminometer (Berthold Technologies, Bad Wildbad, Germany).
Mononuclear Cell Culture
Mononuclear cells from tumor samples of 12 patients with MM (agreement of the Center for Biological Resources of Montpellier University Hospital (N° DC-2008-417)) were cultured for 4 days at 2×105 cells/ml in RPMI 1640 medium, 10% FCS, 2 ng/ml IL-6, with or without graded concentrations of decitabine. In each culture group, viability and cell counts were assayed and MMCs were stained with an anti-CD138-PE mAb (Immunotech, Marseille, France) as previously described (32).
Preparation of Complementary RNA (cRNA) and Microarray Hybridization
RNA was extracted using the RNeasy Kit (Qiagen, Hilden, Germany) as previously described (33,34). Biotinylated cRNA was amplified with a double in vitro transcription and hybridized to the human U133 2.0 plus GeneChips, according to the manufacturer's instructions (Affymetrix, Santa Clara, Calif.). Fluorescence intensities were quantified and analyzed using the GECOS software (Affymetrix).
Gene Expression Profiling and Statistical Analyses
Gene expression data were normalized with the MASS algorithm and analyzed with inventor's bioinformatics platforms—RAGE (http://rage.montp.inserm.fr/) (35) and Amazonia (http://amazonia.montp.inserm.fr/) (36)—or SAM (Significance Analysis of Microarrays) software (37). The statistical significance of differences in overall survival between groups of patients was calculated by the log-rank test. Multivariate analysis was performed using the Cox proportional hazards model. Survival curves were plotted using the Kaplan-Meier method. All these analyses have been done with R.2.10.1 (http://www.r-project.org/) and bioconductor version 2.5. Gene annotation and networks were generated through the use of Ingenuity Pathways Analysis (Ingenuity® Systems, Redwood City, Calif.) (38).
Results
Modulation of Gene Expression by Decitabine in HMCLs: Identification of Prognostic Genes
Five HMCLs were treated with 0.5 μM of decitabine for 7 days, a concentration which did not affect myeloma cell viability (Supplementary Table S2) (19). Using SAM supervised paired analysis, the expression of 48 genes was found to be significantly upregulated and that of 79 genes downregulated by decitabine treatment of 5 HMCLs (FDR <5%; Supplementary Table S3 and S4). Decitabine-regulated genes are significantly enriched in genes related to “Cancer” and “Cell death” pathways (FDR <5%; Ingenuity pathway analysis. Investigating the expression of these 127 decitabine-regulated genes in primary MMCs of a cohort of 206 newly-diagnosed patients (HM cohort), 22 genes had bad prognostic value and 25 a good one after Benjamini-Hochberg multiple testing correction (Supplementary Table S5). The prognostic information of decitabine regulated genes was gathered within an DNA methylation score (DM Score), which was the sum of the beta coefficients of the Cox model weighted by ±1 according to the patient MMC signal above or below the probe set maxstat value as previously described (38). The value of DM Score in normal, premalignant or malignant plasma cells is displayed in
Prognostic value of DM score compared to usual prognostic factors
DM Score had prognostic value when used as a continuous variable (P≦10−4), or by splitting patients into two groups using Maxstat R function (38). A maximum difference in overall survival (OS) was obtained with DM Score=−15.3 splitting patients in a high-risk group of 34.5% patients (DM Score>−15.3) with a 42.1 months median OS and a low risk group of 65.5% patients (DM Score<−15.3) with not reached median survival (
Using univariate Cox analysis, DM Score, UAMS-HRS, IFM-score and GPI had prognostic value as well as t(4;14), del17p, β2m, albumin and ISS using the HM patient cohort (Supplementary Table S6). When compared two by two, DM Score tested with ↑2m remained significant. When these parameters were tested together, DM Score, β2m and t(4;14) kept prognostic value. The DM Score is also prognostic in an independent cohort of 345 patients from UAMS treated with TT2 therapy (UAMS-TT2 cohort). For each patient of UAMS-TT2 cohort, DM Score was computed using parameters defined with HM patients' cohort. The median OS of patients within high score group (DM Score>−15.3) was 53.7 months and not reached for patients with low DM Score (P=0.0001) (
DM Score is Predictive for Sensitivity of Human Myeloma Cell Lines or Patients' Primary MMCs to Decitabine In Vitro.
The inventors sought to determine whether DM Score could predict for the sensitivity of 10 HMCLs to DNMT inhibitor. Starting from a large cohort of 40 HMCLs (22), the 10 HMCLs with the highest or lowest DM Score were selected to assay decitabine sensitivity.
The 5 HMCLs with the highest DM Score exhibited a significant 11-fold higher decitabine sensitivity (median IC50=0.68 μM; range: 0.15 to 2.22 μM) than the 5 HMCLs with low DM Score (P=0.01; median IC50=7.94 μM; range: 2.92 to 60.81 μM) (
Primary MMCs were cultured with their BM environment and recombinant IL-6 and graded concentrations of decitabine for 4 days. Primary MMCs of patients with a DM Score above median value (−19.7,
Discussion
In this study, the inventors have identified a gene expression-based DNA methylation score (DM Score) which is predictive for patients' survival and for the in vitro sensitivity of human myeloma cell lines or patients' primary myeloma cells to a DNMT inhibitor, decitabine. Clinical trials in multiple myeloma combining these demethylating agents with chemotherapy or other agents are underway (8). The current identification of DM Score should be very useful to investigate whether the higher response to DNMTi is found in patients with highest DM Score and to speed up the investigation of the clinical efficacy of the novel agents.
Besides the potential utility of the current DM Score in selecting patients who could benefit from DNMTi therapies, the current study highlights pathways which could be involved in the emergence of multiple myeloma cells. Among the genes downregulated by decitabine treatment and associated with a poor prognosis, the inventors identified RECQ1 (ATP-dependent DNA helicase Q1) and KIF21B (kinesin family member 21B). RECQ helicase are a ubiquitous family of DNA unwinding enzymes involved in the maintenance of chromosome stability (40-42). Mutations in the genes of RECQ family members are linked with genetic disorders associated with genomic instability, cancer predisposition and features of premature ageing. Consistent with their ability to unwind DNA, several functions have been attributed to RECQ proteins, including roles in stabilization and repair of damaged DNA replication forks, telomere maintenance, homologous recombination, and DNA damage checkpoint signaling (40-42). Recent reports supported a cancer specific role for RECQ1 (43-45). RECQ1 silencing in cancer cells resulted in mitotic catastrophe and administration of siRNA targeting RECQ1 prevented tumor growth in murine models (43-45). More recently, it was demonstrated that RECQ1 is highly expressed in various types of solid tumors including colon carcinoma, thyroid cancer, lung cancer and brain glioblastoma tissues (46) In glioblastoma cell lines, depletion of RECQ1 by RNAi results in a significant reduction of cellular proliferation, perturbation of S-phase progression, spontaneous γ-H2AX foci formation and hypersensitivity to hydroxyurea and temozolomide treatments (46). KIF21B is a kinesin family member. Kinesins are a conserved class of microtubule-dependent molecular motor proteins that have adenosine triphosphatase activity and motion characteristics (47). Kinesins support several cellular functions, such as mitosis, meiosis and the transport of macromolecules. In mitosis of eukaryotic cells, kinesins participate in spindle formation, chromosome congression and alignment, and cytokinesis (48). Abnormal expression and function of kinesins are involved in the development or progression of several kinds of human cancers (49,50). Interestingly, KIF21B maps to chromosome 1q arm (1q32.1) whose amplification characterizes a significant fraction of high-risk MM tumors (51). More recently, KIF21B gene was found in a critical neighbor-gene model associated with a poor prognosis across independent data sets of respectively, 559, 247 and 264 myeloma patients (52). These data suggest that decitabine treatment could synergize with DNA-damaging agents, in MM, targeting genes involved in DNA-repair and maintenance of chromosome stability. These results demonstrated that the study of MMCs response to epigenetic-targeted treatments will extend our knowledge of MM development and progression and will lead to potential therapeutic advances. Epigenetic therapies could be combined with conventional therapies to develop personalized treatments in MM, render resistant tumors responsive to treatment.
1++ if growth is strictly dependent on adding exogenous IL-6, + if dependent on adding exogenous IL-6, − if not;
2Origin of the HMCL, MN Montpellier or Nantes, CO collected;
3Disease at diagnosis: MM multiple myeloma, PCL plasma cell leukemia, PCT plasmacytoma;
4Origin of the sample: AF ascitic fluid, BM bone marrow, PE pleural effusion, PB peripheral blood.
Drosophila)
The prognostic factors were tested as single variable or multi variables using Cox-model. P-values and the hazard ratios (HR) are shown. NS, Not significant at a 5% threshold; GPI, gene expression based proliferation index; ISS, International Staging System; HRS, high-risk score; IFM, Intergroupe Francophone du Myelome; NA, Not available.
In order to identify the minimal number of genes among the 47 genes used to calculate the DM score, the inventors used PAM (Prediction Analysis of Microarray) statistical technique used for class prediction from gene expression data using nearest shrunken centroids. 25 genes were identified and are depicted in Table B.
Epigenetics is characterized by a wide range of changes that are reversible and orchestrate gene expression. Recent studies of the epigenome have shown that epigenetic modifications play a role in cancer physiopathology including hematologic malignancies (Issa 2007, Issa, et at 2004, Oki, et al 2008, Smith, et al 2009). In MM, DNA hypomethylation was reported as the predominant early change during myelomagenesis that is gradually transformed to DNA hypermethylation in relapsed cases and during the progression of the disease (Heuck, et al 2013, Walker, et al 2010). Hypermethylation of GPX3, RBP1, SPARC and TGFBI genes was demonstrated to be associated with significantly shorter overall survival, independent of age, ISS score and adverse cytogenetics (Kaiser, et at 2013). DNA methylation is regulated by DNA methyltransferases (DNMT) (Hollenbach, et at 2010). Decitabine (5-aza-2′-deoxycytidine) or 5-azacytidine are both FDA (U.S. Food and Drug Administration)-approved DNMT inhibitors for the treatment of myelodysplastic syndrome (MDS) (Rodriguez-Paredes and Esteller 2011). 5-azacytidine is a ribonucleoside and Decitabine is a deoxyribonucleoside. Decitabine is incorporated only in DNA whereas 5-azacytidine incorporates both in DNA and RNA including rRNAs, tRNAs, mRNAs and miRNAs (Hollenbach, et al 2010). Once incorporated into DNA, 5-azacytidine and Decitabine will lead to DNMTs depletion, DNA hypomethylation and DNA damage induction (Flotho, et al 2009, Ghoshal, et al 2005, Hollenbach, et al 2010, Kiziltepe, et al 2007, Palii, et al 2008, Stresemann, et at 2006). Via incorporation into newly synthetized RNA, 5-azacytidine will alterate the processing of RNAs inhibiting protein synthesis (Hollenbach, et al 2010). The inventors have recently reported the building of a DNA methylation score (DM Score) predicting for the efficacy of decitabine to kill MM cells (MMCs). Given that 5-azacitidine can incorporate in both DNA and RNA and block DNA methylation and RNA traduction. The inventors have currently investigated whether DM Score could also predict for MMCs sensitivity to 5-azacitidine.
Material & Methods
Human Myeloma Cell Lines (HMCLs) and Primary Multiple Myeloma Cells of Patients.
Human myeloma cell lines XG-1, XG-2, XG-5, XG-6, XG-12, XG-13, XG-16, XG-19, XG-20, RPMI8226, LP1 and SKMM2 (HMCLs, N=12) were obtained as previously described (Gu, et al 2000, Moreaux, et al 2011, Rebouissou, et al 1998, Tarte, et al 1999, Zhang, et al 1994) or purchased from DSMZ and American Type Culture Collection. These HMCLs were extensively phenotypically and molecularly characterized as described (Moreaux, et al 2011). Microarray data are deposited in the ArrayExpress public database (accession numbers E-TABM-937 and E-TABM-1088). Bone marrow of patients presenting with previously untreated multiple myeloma (n=14) at the university hospital of Montpellier were obtained after patients' written informed consent in accordance with the Declaration of Helsinki and agreement of the Montpellier University Hospital Center for Biological Resources. MMCs were purified as previously published (Mahtouk, et al 2004) and whole genome gene expression profiling assayed with Affymetrix U133 2.0 plus microarrays (Affymetrix, Santa Clara, Calif., USA) (De Vos, et al 2002). Gene expression data were analyzed using the inventor's bioinformatics platforms (http://rage.montp.inserm.fr/ and http.//amazonia.montp.inserm.fr/) (Reme, et al 2008, Tanguy Le Carrour 2010) and computations performed using R 2.15.1 (http://www.r-project.org/) and bioconductor 2.0 (http://www.bioconductor.org).
Sensitivity of Myeloma Cell Lines and Primary Myeloma Cells to 5-Azacitidine.
HMCLs were cultured with graded 5-azacitidine (Sigma, St. Louis, Mo.) concentrations. HMCLs cell growth was quantified with a Cell Titer Glo Luminescent Assay (Promega, Madison, Wis.) and half inhibitory concentration (IC50) was determined using GraphPad Prism (http://www.graphpad.com/scientific-software/prism/).
Primary myeloma cells of 14 patients were cultured with or without graded concentrations of 5-azacitidine and MMC cytotoxicity evaluated using anti-CD138-PE mAb (Immunotech, Marseille, France) as described (Jourdan, et al 1998; Mahtouk, et al 2004; Moreaux, et al 2012).
Gene Set Enrichment Analysis (GSEA)
The inventors compared the gene expression levels from high DM Score versus low DM Score patients and picked up the genes which had significant different expression for Gene set enrichment analysis (GSEA). Gene set enrichment analysis was carried out by computing overlaps with canonical pathways and gene ontology gene sets obtained from the Broad Institute.
Results and Discussion
The efficacy of DM Score to predict MMCs sensitivity to 5-azacitidine was investigated on 12 HMCLs with high or low DM Score. The 6 HMCLs with high DM Score exhibited a 3 fold higher 5-azacitidine sensitivity (P=0.01; median IC50=2.43 μM; range: 1.12 to 8.25 μM) compared to the 6 ones with a low DM Score (median IC50=7.45 μM; range: 5.73 to 27.9) (
Thus, the DM Score, which was built using 47 genes whose expression is deregulated by decitabine in HMCLs and which have prognostic value for patients overall survival, can predict for the sensitivity of MM cell lines and primary MMCs to the two-clinical grade inhibitors of DNMT. These 47 genes include 22 genes associated with a bad prognosis and 25 with good one. Using GSEA analysis, MMCs of patients with a high DM Score show a significant enrichment in genes associated with proliferation (gene sets: REACTOME CELL CYCLE, CELL CYCLE G2 M and PLASMA CELLS VS PLASMABLAST DN, P<0.001, and supplementary Tables S7, S8 and S9). On the other hand, MMCs of patients with a low DM Score show a significant enrichment in genes coding for solute carrier group of membrane transport proteins (gene sets: REACTOME TRANSPORT OF INORGANIC CATIONS ANIONS AND AMINO ACIDS OLIGOPEPTIDES, P<0.001, and supplementary Tables S10).
Thus, the higher sensitivity of MMCs of patients with a high DM Score to DNMT inhibitors could be explained by the fact that these inhibitors are mainly active in cell cycling cells since incorporation into DNA is restricted to the S-phase (Hollenbach, et al 2010), and also by a reduced drug export in MMCs of these patients, resulting in higher intracellular drug accumulation.
Clinical trials evaluate the safety of DNMTi as monotherapy or in combination with lenalidomide or dexamethasone in MM (Maes, et al 2013; Toor, et al 2012) and investigate the link between HM Score and response of patients to DNMTi could be promising.
In conclusion, the DM Score allows identification of MM patients who could benefit from treatment with two clinical grade DNMT inhibitors and the development of personalized treatment.
Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.
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| Number | Date | Country | Kind |
|---|---|---|---|
| 12306141.8 | Sep 2012 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2013/069736 | 9/23/2013 | WO | 00 |
