# In vivo treatment with epigenetic modulating agents induces transcriptional alterations associated with prognosis and immunomodulation in multiple myeloma

K Maes, E de Smedt, A Kassambara, D Hose, A Seckinger, E van Valskenborgh, E Menu, B Klein, K Vanderkerken, J. Moreaux, et al.

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## ► To cite this version:

K Maes, E de Smedt, A Kassambara, D Hose, A Seckinger, et al.. In vivo treatment with epigenetic modulating agents induces transcriptional alterations associated with prognosis and immunomodulation in multiple myeloma. Oncotarget, 2015, 6 (5), pp.3319-3334. 10.18632/oncotarget.3207. hal-01153601

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# *In vivo* treatment with epigenetic modulating agents induces transcriptional alterations associated with prognosis and immunomodulation in multiple myeloma

Ken Maes<fnref n="1" />, Eva De Smedt<fnref n="1" />, Alboukadel Kassambara<fnref n="2" />,<fnref n="3" />, Dirk Hose<fnref n="4" />, Anja Seckinger<fnref n="4" />, Els Van Valckenborgh<fnref n="1" />, Eline Menu<fnref n="1" />, Bernard Klein<fnref n="2" />,<fnref n="3" />,<fnref n="5" />, Karin Vanderkerken<fnref n="1" />, Jérôme Moreaux<fnref n="2" />,<fnref n="3" />,<fnref n="5" />,* and Elke De Bruyne<fnref n="1" />,*

**Correspondence to:** Elke De Bruyne, email: eldebruy@vub.ac.be

**Keywords:** Epigenetics, Multiple Myeloma, HDACi, DNMTi, murine model

**Received:** October 13, 2014

**Accepted:** December 17, 2014

**Published:** December 26, 2014

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

## ABSTRACT

Histone deacetylase inhibitors (HDACi) and DNA methyltransferase inhibitors (DNMTi) are in early clinical development for multiple myeloma (MM) therapy. Despite all encouraging pre-clinical data, clinical activity of HDACi and DNMTi is mostly lacking. To optimize the trials, characterization of the *in vivo* response towards HDACi and DNMTi will be crucial. Therefore, we investigated the transcriptional response after *in vivo* treatment with the HDACi quisinostat or DNMTi decitabine using the murine 5T33MM model.

We identified 504 and 154 genes deregulated by quisinostat and decitabine, respectively. Of interest, MM patients' gene expression levels of 62 quisinostat- and 25 decitabine-deregulated genes were predictive for overall survival of patients. This prognostic information was implemented in a DNA methylation and histone acetylation score. A high score was related to a high proliferative and immature phenotype of MM cells. Furthermore, highly scored MM patients had an adverse overall survival. Interestingly, bio-informatic prediction tools revealed an association of quisinostat-deregulated genes with lymphocyte activation, proliferation, immune-effector mechanisms and T-helper-1 development.

Overall, treatment of 5T33MM mice with epigenetic modulating agents led to the translation of gene signatures to predict overall survival of MM patients. HDACi mainly deregulated tumoral immunomodulatory pathways, supporting the rationale to combine HDACi with immunomodulatory therapies.

## INTRODUCTION

Multiple myeloma (MM) is a hematological plasma cell malignancy. Malignant plasma cells mainly reside in the bone marrow (BM) where numerous interactions between MM cells and the BM compartments confer survival and growth of MM cells and induce angiogenesis,

bone destruction, drug resistance and immune escape [1]. MM is characterized by a genetic heterogeneity that is translated to a large range of patients' survival times [2]. Prognosis of MM patients can be assessed by the International Staging System reflecting disease activity and inflammatory status. The molecular characteristics of MM cells are determined by chromosomal alterations and alterations in gene expression [3-11]. Treatments

<footnote n="1">¹ Department of Hematology and Immunology, Myeloma Center Brussels, Vrije Universiteit Brussel, Brussels</footnote>

<footnote n="2">² Department of Biological Haematology, CHU Montpellier, Montpellier, France</footnote>

<footnote n="3">³ Institute of Human Genetics, CNRS-UPR1142, Montpellier F-34396, France</footnote>

<footnote n="4">⁴ Medizinische Klinik, Universitätsklinikum Heidelberg, Heidelberg, Germany</footnote>

<footnote n="5">⁵ University of Montpellier 1, UFR de Médecine, Montpellier, France</footnote>

<footnote>* These authors contributed equally to this work</footnote>
consist of combinations of various drug classes such as glucocorticoids, proteasome inhibitors, alkylators and immunomodulatory drugs (IMiDs) with or without high dose melphalan (HDM) and autologous stem cell transplantation (ASCT) [12]. Although the combination of these drugs have significantly improved patient survival [13-15], a majority of patients still relapses, emphasizing the need to find alternative treatment options.

Epigenetic aberrations have been reported to contribute to MM pathogenesis, together with genetic abnormalities [16-18]. DNA methylation and posttranslational histone modification are two major epigenetic modifications. DNA methylation is considered a prognostic marker in human MM [19-23] and differences can be detected between MGUS and MM in terms of gene-specific methylation and global hypomethylation of non-CpG islands (repetitive elements or intergenic regions) [22, 24]. Moreover, cytogenetic MM subgroups are defined by differences in DNA methylation patterns [22, 25]. Among posttranslational histone modifications, methylation and acetylation are extensively studied in relation to cancer [26]. The t(4;14) translocation in MM cells, present in approximately 15% of the patients, leads to overexpression of *WHSC1* encoding for the histone methyltransferase MMSET [27]. In addition, mutations in the histone methyltransferases *WHSC1L1*, *MLL1-3* and in the histone demethylase *UTX* are identified in MM patients [28, 29].

Epigenetic modulating agents such as histone deacetylase inhibitors (HDACi) and DNA methyltransferase inhibitors (DNMTi) interfere with epigenetic aberrations in cancer [30]. HDACi used alone or in combination with conventional anti-MM agents have potent pre-clinical anti-MM effects [31-33]. The same holds true for the DNMTi azacytidine or decitabine [34, 35]. Furthermore, using HDACi and DNMTi, we recently identified gene expression-based risk scores, which are predictive for the sensitivity of MM cells towards DNMTi and HDACi as well as for the overall survival of MM patients. This pre-clinical work provides the rationale for clinical trials evaluating the anti-MM activity of HDACi. While single-agents HDACi appeared to mediate little to no clinical activity [36-38], combinatory treatment of the pan-HDACi vorinostat or panobinostat in combination with respectively the proteasome inhibitor bortezomib or bortezomib plus dexamethasone did prolong progression free survival with respectively 0.8 months and 3.9 months [39, 40]. However, the clinical relevance in terms of overall survival is not yet clear and a high occurrence of side effects was observed [39, 40]. In addition, a phase II trial of the combination of panobinostat with melphalan, thalidomide and prednisone was also associated with a high occurrence of side effects [41]. As for the DNMTi, the therapeutic potential in MM is yet to be evaluated in clinical trials.

These above mentioned clinical trials raise questions

about the efficacy of epigenetic modulating agents in MM patients. Although the pre-clinical studies on epigenetic modulating agents demonstrated pleiotropic mechanisms of action explaining their anti-MM activity [34, 42-44], it is widely known that the drug response of MM cells is influenced by interactions with the BM microenvironment and immune system [45-47]. Thus, a better understanding of the *in vivo* mechanisms of epigenetic modulating agents will be crucial as it can provide new possibilities for combinatory therapies, identify more specific targets, reduce side effects and identify the patients whom might benefit from treatment with epigenetic modulating agents [48]. We have previously demonstrated potent *in vivo* anti-MM activity for decitabine and the HDACi quisinostat using the immune competent, syngeneic 5TMM models [31, 32, 34]. These models are suitable for studies on MM biology and pre-clinical drug testing because they take into account the BM microenvironment and immune system [32, 49-52]. Here, we investigated the *in vivo* transcriptional response of MM cells towards decitabine and quisinostat in the 5T33MM model to validate our previous work on the prognostic relevance and to identify new *in vivo* relevant targets.

## RESULTS

### *In vivo* treatment with epigenetic modulating agents induced transcriptional changes linked with survival of MM patients

The syngeneic immunocompetent 5T33MM model was used to study the *in vivo* transcriptional response towards the DNMTi decitabine and the HDACi quisinostat. Quisinostat is a hydroxamate-based pan-HDAC inhibitor with similar HDAC selectivity as panobinostat [53]. In a preliminary series of experiments, the sub-lethal concentrations showing minimal effects on BM plasmacytosis ensuring the yield of good quality RNA were determined (data not shown). Next, mice with established disease were treated with these sub-lethal concentrations of decitabine or quisinostat for 5 days (Supplementary Figure S1A). Total BM plasmacytosis was above 80% in the vehicle and decitabine-treated mice, while quisinostat decreased tumor load to approximately 60% (Supplementary Figure S1B). Overall, the short-term treatment had only minimal anti-MM effects thus warranting good sample quality.

Using Significance Analysis of Microarray (SAM), decitabine treatment resulted in a significant upregulation of 172 probe sets and downregulation of 8 ones (false discovery rate (FDR) ≤ 5%; ratio ≥ 2), corresponding to 154 unique genes. Quisinostat treatment induced a significant upregulation of 569 probe sets and downregulation of 5 probe sets (FDR ≤ 5%; ratio ≥ 2),
<table><caption>**Table 1: Prognostic value of the deregulated genes as determined by Maxstat analysis.** The human orthologs of the *in vivo* deregulated murine genes after treatment with quisinostat or decitabine were used to determine the prognostic value in the HM training cohort.</caption><thead><tr><th colspan="4">Decitabine</th></tr><tr><th>Probeset</th><th>Name</th><th>Benjamini-Hochberg adjusted p-value (FDR)</th><th>Hazard ratio</th></tr></thead><tbody><tr><td colspan="4">Good prognostic genes (5)</td></tr><tr><td>203708_at</td><td>PDE4B</td><td>0.016</td><td>0.184</td></tr><tr><td>225629_s_at</td><td>ZBTB4</td><td>0.051</td><td>0.346</td></tr><tr><td>217492_s_at</td><td>PTENP1</td><td>0.049</td><td>0.358</td></tr><tr><td>202917_s_at</td><td>S100A8</td><td>0.034</td><td>0.363</td></tr><tr><td>215210_s_at</td><td>DLST</td><td>0.049</td><td>0.405</td></tr><tr><td colspan="4">Bad prognostic genes (20)</td></tr><tr><td>219684_at</td><td>RTP4</td><td>0.046</td><td>5.158</td></tr><tr><td>224701_at</td><td>PARP14</td><td>0.002</td><td>4.106</td></tr><tr><td>217503_at</td><td>STK17B</td><td>0.010</td><td>3.796</td></tr><tr><td>222848_at</td><td>CENPK</td><td>0.007</td><td>3.743</td></tr><tr><td>223271_s_at</td><td>CTDSPL2</td><td>0.007</td><td>3.681</td></tr><tr><td>228351_at</td><td>HEATR1</td><td>0.046</td><td>3.633</td></tr><tr><td>204709_s_at</td><td>KIF23</td><td>0.012</td><td>3.550</td></tr><tr><td>213647_at</td><td>DNA2</td><td>0.009</td><td>3.537</td></tr><tr><td>219211_at</td><td>USP18</td><td>0.020</td><td>3.140</td></tr><tr><td>212416_at</td><td>SCAMP1</td><td>0.048</td><td>2.981</td></tr><tr><td>212577_at</td><td>SMCHD1</td><td>0.024</td><td>2.782</td></tr><tr><td>208901_s_at</td><td>TOP1</td><td>0.026</td><td>2.753</td></tr><tr><td>224227_s_at</td><td>BDP1</td><td>0.041</td><td>2.711</td></tr><tr><td>242625_at</td><td>RSAD2</td><td>0.049</td><td>2.701</td></tr><tr><td>213742_at</td><td>SRSF11</td><td>0.036</td><td>2.611</td></tr><tr><td>218585_s_at</td><td>DTL</td><td>0.036</td><td>2.582</td></tr><tr><td>228006_at</td><td>PTEN</td><td>0.045</td><td>2.580</td></tr><tr><td>225647_s_at</td><td>CTSC</td><td>0.045</td><td>2.554</td></tr><tr><td>243213_at</td><td>STAT3</td><td>0.046</td><td>2.538</td></tr><tr><td>226942_at</td><td>PHF20L1</td><td>0.047</td><td>2.382</td></tr><tr><th colspan="4">Quisinostat</th></tr><tr><th>Probeset</th><th>Name</th><th>Benjamini-Hochberg adjusted p-value (FDR)</th><th>Hazard ratio</th></tr><tr><td colspan="4">Good prognostic genes (31)</td></tr><tr><td>223806_s_at</td><td>NAPSA</td><td>0.048</td><td>0.153</td></tr><tr><td>222717_at</td><td>SDPR</td><td>0.045</td><td>0.184</td></tr><tr><td>203708_at</td><td>PDE4B</td><td>0.029</td><td>0.184</td></tr><tr><td>210889_s_at</td><td>FCGR2B</td><td>0.008</td><td>0.237</td></tr><tr><td>224826_at</td><td>GPCPD1</td><td>0.009</td><td>0.240</td></tr><tr><td>202878_s_at</td><td>CD93</td><td>0.012</td><td>0.261</td></tr><tr><td>201445_at</td><td>CNN3</td><td>0.007</td><td>0.270</td></tr><tr><td>200934_at</td><td>DEK</td><td>0.020</td><td>0.281</td></tr><tr><td>212298_at</td><td>NRP1</td><td>0.013</td><td>0.302</td></tr></tbody></table>
<table><tr><td>201243_s_at</td><td>ATP1B1</td><td>0.044</td><td>0.314</td></tr><tr><td>224586_x_at</td><td>SUB1</td><td>0.046</td><td>0.322</td></tr><tr><td>210664_s_at</td><td>TFPI</td><td>0.018</td><td>0.326</td></tr><tr><td>235593_at</td><td>ZEB2</td><td>0.050</td><td>0.342</td></tr><tr><td>207794_at</td><td>CCR2</td><td>0.046</td><td>0.347</td></tr><tr><td>225175_s_at</td><td>SLC44A2</td><td>0.039</td><td>0.349</td></tr><tr><td>224964_s_at</td><td>GNG2</td><td>0.045</td><td>0.353</td></tr><tr><td>203799_at</td><td>CD302 /// LY75-CD302</td><td>0.045</td><td>0.355</td></tr><tr><td>224983_at</td><td>SCARB2</td><td>0.043</td><td>0.358</td></tr><tr><td>212268_at</td><td>SERPINB1</td><td>0.044</td><td>0.359</td></tr><tr><td>222391_at</td><td>TMEM30A</td><td>0.036</td><td>0.360</td></tr><tr><td>209916_at</td><td>DHTKD1</td><td>0.039</td><td>0.363</td></tr><tr><td>202917_s_at</td><td>S100A8</td><td>0.048</td><td>0.363</td></tr><tr><td>209829_at</td><td>FAM65B</td><td>0.039</td><td>0.367</td></tr><tr><td>200965_s_at</td><td>ABLIM1</td><td>0.044</td><td>0.374</td></tr><tr><td>226841_at</td><td>MPEG1</td><td>0.050</td><td>0.375</td></tr><tr><td>219062_s_at</td><td>ZCCHC2</td><td>0.045</td><td>0.382</td></tr><tr><td>224442_at</td><td>PHF6</td><td>0.044</td><td>0.388</td></tr><tr><td>202990_at</td><td>PYGL</td><td>0.049</td><td>0.390</td></tr><tr><td>205790_at</td><td>SKAP1</td><td>0.045</td><td>0.402</td></tr><tr><td>215210_s_at</td><td>DLST</td><td>0.049</td><td>0.405</td></tr><tr><td>226925_at</td><td>ACPL2</td><td>0.049</td><td>0.409</td></tr></table>

Bad prognostic genes (30)

<table><tr><td>204040_at</td><td>RNF144A</td><td>0.043</td><td>5.358</td></tr><tr><td>217503_at</td><td>STK17B</td><td>0.017</td><td>3.796</td></tr><tr><td>223271_s_at</td><td>CTDSPL2</td><td>0.010</td><td>3.681</td></tr><tr><td>203091_at</td><td>FUBP1</td><td>0.049</td><td>3.336</td></tr><tr><td>241879_at</td><td>LPP</td><td>0.044</td><td>3.195</td></tr><tr><td>203449_s_at</td><td>TERF1</td><td>0.047</td><td>3.195</td></tr><tr><td>215111_s_at</td><td>TSC22D1</td><td>0.040</td><td>3.133</td></tr><tr><td>212151_at</td><td>PBX1</td><td>0.043</td><td>3.078</td></tr><tr><td>203636_at</td><td>MID1</td><td>0.020</td><td>3.046</td></tr><tr><td>201912_s_at</td><td>GSPT1</td><td>0.038</td><td>2.985</td></tr><tr><td>212416_at</td><td>SCAMP1</td><td>0.049</td><td>2.981</td></tr><tr><td>209798_at</td><td>NPAT</td><td>0.044</td><td>2.944</td></tr><tr><td>233011_at</td><td>ANXA1</td><td>0.043</td><td>2.944</td></tr><tr><td>222133_s_at</td><td>PHF20L1</td><td>0.038</td><td>2.860</td></tr><tr><td>204809_at</td><td>CLPX</td><td>0.035</td><td>2.860</td></tr><tr><td>223319_at</td><td>GPHN</td><td>0.037</td><td>2.789</td></tr><tr><td>212577_at</td><td>SMCHD1</td><td>0.036</td><td>2.782</td></tr><tr><td>208901_s_at</td><td>TOP1</td><td>0.038</td><td>2.753</td></tr><tr><td>224227_s_at</td><td>BDP1</td><td>0.045</td><td>2.711</td></tr><tr><td>1557737_s_at</td><td>NKTR</td><td>0.050</td><td>2.651</td></tr></table>
<table><tr><td>208831_x_at</td><td>SUPT6H</td><td>0.050</td><td>2.640</td></tr><tr><td>224848_at</td><td>CDK6</td><td>0.047</td><td>2.625</td></tr><tr><td>213742_at</td><td>SRSF11</td><td>0.048</td><td>2.611</td></tr><tr><td>218585_s_at</td><td>DTL</td><td>0.043</td><td>2.582</td></tr><tr><td>214953_s_at</td><td>APP</td><td>0.046</td><td>2.581</td></tr><tr><td>228006_at</td><td>PTEN</td><td>0.046</td><td>2.580</td></tr><tr><td>225647_s_at</td><td>CTSC</td><td>0.049</td><td>2.554</td></tr><tr><td>220994_s_at</td><td>STXBP6</td><td>0.045</td><td>2.546</td></tr><tr><td>225097_at</td><td>HIPK2</td><td>0.043</td><td>2.539</td></tr><tr><td>202446_s_at</td><td>PLSCR1</td><td>0.049</td><td>2.447</td></tr></table>

corresponding to 504 unique genes. Ninety-eight genes were commonly deregulated by decitabine and quisinostat (Figure 1, Supplementary Tables S1-S2). The prognostic value of the human orthologs for the overall survival of patients with MM of murine deregulated genes was investigated using patients data from the University

Clinics of Heidelberg and Montpellier (HM)-cohort as a training cohort and the University of Arkansas for Medical Sciences-Total Therapy 2 (TT2-cohort) as validation cohort [54-57]. Using the HM-cohort, five decitabine-deregulated probe sets had a prognostic value for a better overall survival and 20 for an adverse one

<figure><img src="image_3.png" /><figcaption>**Figure 1: Overview of the *in vivo* transcriptional response towards decitabine or quisinostat and the gene expression-based risk scores.** A: Venn-diagram of deregulated genes after *in vivo* treatment of the 5T33MM model with decitabine or quisinostat. Microarray data were normalized using MAS5 and analyzed using SAM for the identification of differentially expressed probe sets (ratio ≥ 2, Benjamini-Hochberg p value < 0.05). B: The Mu-DM and Mu-HA score during MM progression. The prognostic value of decitabine- or quisinostat-deregulated genes was calculated using Maxstat. Decitabine and quisinostat led to deregulation of respectively 25 and 61 prognostic genes which were used to develop a gene expression-based risk score as explained in Material and Methods. The boxes represent median and 10-90 percentiles of the score values during MM progression. \* = p<0.05, \*\* = p<0.001. BMPC = healthy donor bone marrow plasma cell, MM = multiple myeloma, HMCL = human myeloma cell lines.</figcaption></figure>
(Table 1). Thirty-one quisinostat-deregulated genes had a favourable prognostic value and 30 an adverse one (Table 1). The prognostic information of the 25 decitabine- or 61 quisinostat-deregulated genes was implemented into respectively a murine DNA methylation (Mu-DM) score or a murine histone acetylation (Mu-HA) score, as described in Material and Methods. Subsequently, we analyzed the scores during MM progression [58]. MM cells had a higher Mu-HA score (p<0.05) compared to purified healthy plasma cells (BMPCs). HMCLs had significant higher scores (p<0.0001) compared to MM cells and BMPCs (Figure 1). Both scores could predict patients’ overall survival. In the training cohort, a maximum difference in overall survival (OS) was identified with a Mu-DM score of -6.06 (Figure 2A). Patients with Mu-DM score ≤ -6.06 had a significant lower risk (80% of the patients who did not reach median OS) compared to patients with a Mu-DM score > -6.06 (20% of the patients with a median OS of 25 months). For the Mu-HA score, a maximal difference in OS was observed with -20.13 as cut-off, splitting patients in a low-risk group (Mu-HA score ≤ -20.13; 77.7% of the patients who did not reach median OS) and a high-risk group (Mu-HA score > -20.13; 22.3% of the patients with a median survival of 24 months) (Figure 2A). This prognostic value of the score was validated in an independent validation cohort (TT2-cohort) as shown in Figure 2B. We then

evaluated whether the scores could have a prognostic value in relapsed patients. Using the Mulligan-cohort, including patients treated with bortezomib after relapse, only the Mu-HA score kept prognostic value (Figure 2C). Next, we evaluated whether the scores could be used to predict response to therapy. Using the HM-cohort, we could not find a significant difference in the distributions of low scored and high scored patients between different response groups following high-dose therapy and autologous stem cell transplantation (data not shown). For the Mulligan-cohort we only tested the Mu-HA score. The distributions of low scored and high scored patients were significantly different between non-responders (no change + progressive disease) compared to responders (complete response + partial response + minimal response) (Supplementary Table S3). The prediction had a high specificity (97.3%), but a low sensitivity (16.1%).

Next, the prognostic value for overall survival of Mu-DM and Mu-HA scores was compared with conventional prognostic factors including beta-2-microglobulin (B2m), International Staging (ISS), t(4;14) and del17p. We also included other gene expression-based risk scores such as High Risk Score (HRS corresponding to UAMS-70-gene model) [8], Intergroupe Francophone du Myélome (IFM) score [4], Risk Score (RS) [7], DNA methylation (DM) score [35], histone acetylation (HA) score [59] and gene expression-based proliferation

<figure><img src="image_4.png" /><figcaption>**Figure 2: The prognostic value of the Mu-DM and Mu-HA score in terms of overall survival.** A: Using Maxstat analysis, the prognostic value of Mu-DM (top) and Mu-HA (bottom) score was calculated by the optimal separation of patients of the HM-cohort (n=206) in a low and high risk group based on a cut-off value (-6.06 for Mu-DM score; -20.13 for Mu-HA score). B: The prognostic value of the Mu-DM (top) and Mu-HA (bottom) score were tested in the independent TT2-cohort (n=345) using the same cut-off value as obtained in the HM-cohort. C: The prognostic value of the Mu-DM (top) and Mu-HA (bottom) score were tested in the independent Mulligan-cohort using the same cut-off value as obtained in the HM-cohort.</figcaption></figure>
index (GPI) [5]. Univariate Cox analysis showed that all these factors have prognostic value (Supplementary table S4) [5, 35, 59]. When analyzed 2 by 2, Mu-DM remained significant with del17p and Mu-HA score in the HM training cohort. The Mu-HA score remained significant with t(4;14) and Mu-DM in bivariate analyses. Multivariate Cox analysis with all parameters tested together showed that Mu-DM, Mu-HA and t(4;14) remained independent prognostic factors in the HM training cohort. In the TT2 validation cohort, Cox bivariate analysis showed significance of Mu-DM and Mu-HA score together with all factors including the previously reported DM and HA scores but not with HRS and IFM scores

(Supplementary table S4). HRS and t(4;14) remained independent prognostic factors when all parameters were tested together in the TT2 validation cohort. When tested with the previously reported DM and HA score, the Mu-DM and Mu-HA score remained independent in the HM validation cohort but not in the TT2 training cohort in the multivariate analysis (Supplementary Table S4).

To investigate the characteristics of the stratified patients, the distribution of the Mu-DM and the Mu-HA score among the 8 molecular subgroups of MM using the TT2-cohort was analyzed [60]. Both scores were significantly higher (p<0.05) in the proliferation subgroup (PR) compared to all other subgroups (Figure 3A). The PR

<figure><img src="image_5.png" /><figcaption>**Figure 3: The score values in the different MM molecular subgroups, gene expression-based proliferation index (GPI) subgroups and during B-cell development.** A: The Mu-DM (left) and Mu-HA (right) score values were calculated based on the gene expression data of the 8 MM molecular subgroups of the TT2-cohort. PR = proliferation, LB = low bone disease, MS = MMSET, HY = hyperdiploid, CD1 = cyclin D1, CD2 = cyclin D2, MF = MAF; MY = myeloid. \* indicates higher score value compared to all other groups with p<0.05. B: The Mu-DM (left) and Mu-HA (right) score values were calculated in the different gene expression-based proliferation index groups (GPI). \* = p<0.05. C: The score values in the different stages of *in vitro* generated plasma cell differentiation. MBC = memory B-cell, PPB = pre-plasmablast, PB = plasmablast, PC = early plasma cell, LL-PC = long-lived plasma cell, BMPC = healthy donor bone marrow plasma cell. \* indicates p<0.05 compared to all other groups. The boxes represent median and 10-90 percentiles of the score values. Dots are outliers and the dotted lines represent the cut-off value for each score.</figcaption></figure>
subgroup was the only group with a median score above the cut-point and has been associated with a poor prognosis [60]. In addition, a significant correlation between GPI groups from HM-cohort and Mu-DM or Mu-HA score was identified (r = 0.36; p<.01; n=206 and r = 0.32; p< .01; n=206 respectively) in MM cells of patients. This is further confirmed by the significant gradual increase in the Mu-DM and Mu-HA scores from GPI<sup>low</sup> to GPI<sup>medium</sup> and GPI<sup>high</sup> groups (p<0.05) (Figure 3B) [5]. Recently, the presence of a hierarchical plasma cell progenitor organization was described in the BM of MM patients and it was furthermore suggested that an epigenetic-mediated plasticity exists between these plasma cell progenitors [61]. Therefore, we analyzed the scores in the *in vitro* generated transitional stages from memory B-cell to plasma cells [58, 62]. We found that both scores were significantly higher (p<0.05) in pre-plasmablast (Pre-PB) stages and significantly lower (p<0.05) in healthy donor BMPCs compared to all other conditions. The other stages were considered as having an intermediate Mu-DM and Mu-HA score with plasmablasts (PBs), plasma cells (PCs) and long-lived plasma cells (LL-PCs) having a higher Mu-

DM score compared to memory B-cells (MBCs) (Figure 3C). In summary, quisinostat and decitabine altered *in vivo* the expression of a gene signature that is associated with prognosis allowing risk stratification of patients. High scores are furthermore linked with a proliferative and immature plasma cell phenotype.

**In vivo** quisinostat or decitabine treatment altered the expression of genes involved in immune pathways

Using DAVID gene ontology (GO) software, the enrichment of the *in vivo* deregulated genes with a biological process was investigated [63, 64]. GO analysis showed a significant enrichment of genes involved in immune regulation, metabolism/homeostasis, development and differentiation, cytoskeleton/migration, regulation of gene expression and cell death (Supplementary Table S5). The hierarchical clustering of the co-enriched gene signatures is shown in Figure 4 and 5. Looking to the genes included in the scores in more detail showed that

<figure><img src="image_6.png" /><figcaption>**Figure 4:** Heatmap of quisinostat-deregulated genes with overlapping functions identified by GO analysis and Reactome. Hierarchical clusters were made using Cluster and Treeview. Sample 1-4: quisinostat. Sample 5-8: vehicle.</figcaption></figure>
these genes were dispersed across all the different enriched GO sets. STRING protein network analysis confirmed the GO analysis and showed network interactions linked with immune cell regulation, cell death pathways and metabolism (Supplementary Figure S2, S3) [65]. Reactome pathway analysis revealed that after quisinostat treatment, there were several enriched gene sets and these were associated with growth factor signaling including tumor necrosis factor (TNF), TNF-related apoptosis-inducing ligand (TRAIL), interferon-gamma (IFN-γ), interleukin-1 (IL-1), IL-2, IL-12, bone morphogenetic protein (BMP) and transforming growth factor beta (TGF-β). In addition, several signaling pathways showed enrichment of deregulated genes and include p38/MAPK, PI3K/Akt, SMAD, eIF4e, proteoglycan and integrin signaling (Supplementary Table S6).

Of particular interest, GO analysis, Reactome and STRING commonly identified a significant enrichment of genes linked with immune pathways. This immune pathway gene signature was characterized by genes encoding for proteins involved in lymphocyte activation and proliferation, immune-effector processes and T-helper-1 development as depicted by Reactome. Using Pathway-Guide, we found that the gene signature is built up by genes involved in chemokine signaling, cytokine interactions, phagocytosis, T-cell receptor and natural-killer (NK) cell signaling (Supplementary Table S7). The signature includes genes coding for chemokines (CXCL12, PBPB, CCL9, CCL3), chemokine receptors (CCR5, CCR2), cytokines (IFNG, OSM) and

cytokine receptors (IL7R, IL18R1, KIT, FLT3, CSF1R, CSF3R, MET, TNFRSF12). Genes encoding for proteins involved in lymphocyte activation include co-inhibitory proteins (CTLA4, CTLA2A, CTLA2B), co-stimulatory proteins (PTPRC), antigen-presenting protein (H2-DMA), lymphocyte activation marker (CD69), monocyte marker (CD14), transcription factors (RUNX1, PBX1, SMAD1, HOXA9, NFKBIZ, JUN, KLF6) and NK-cell inhibitory receptors (KLRD1, KLRA7). Genes encoding for proteins associated with immune-effector responses include phagocytose receptors (FCGR3, FCGR2B), phagocytosis mediators (PRKCA, PRKCD), extracellular matrix (ECM) component FN1 and its receptor ITGA5 and more downstream regulators (PAK1, RHOA, VAV3, IQGAP2, ENAH, APCRB, TMSB4X, DIAP1) involved in actin polymerization, adherens junctions and stress fibers polymerization (Figure 4, Supplementary Table S5, S6, S7). Thus, in vivo treatment with quisinostat induced a broad transcriptional response within the tumor cells with a significant deregulation of genes encoding for proteins involved in immune regulation pathways.

DISCUSSION

To gain insight into the *in vivo* transcriptional response towards epigenetic modulating agents, we treated the syngeneic immunocompetent murine 5T33MM model with sub-lethal doses of the DNMTi decitabine and the HDACi quisinostat. First, *in vivo* treatment identified several deregulated genes with a prognostic value in the

<figure><img src="image_7.png" /><figcaption>**Figure 5:** Heatmap of decitabine-deregulated genes with overlapping functions identified by GO analysis and Reactome. Hierarchical clusters were made using Cluster and Treeview. Sample 1-4: decitabine. Sample 5-8: vehicle.</figcaption></figure>
HM-cohort allowing the development of gene expression-
based risk scores. In accordance with our previous work in
HMCL, both the Mu-DM and Mu-HA score were found to
have a prognostic value for OS in two tested independent
MM cohorts [35, 59]. In line, both the Mu-DM and Mu-
HA score were an independent variable for prognosis of
OS together with t(4;14) in the HM-cohort but not in the
TT2-cohort after multivariate COX analysis. In addition,
the Mu-HA and Mu-DM score were independent of the
previous published HA- and DM score in the HM cohort
[35, 59]. In addition, we showed that the Mu-HA score
is predictive for overall survival of relapsed patients
treated with bortezomib and further confirmed that
gene expression profiling is useful for the prediction of
outcome of newly diagnosed and relapsed patients [66].
The predictive power of the scores for treatment response
was limited as the Mu-HA score had a low specificity
of prediction for response to bortezomib and we did not
identify a significant association between the Mu-DM
or Mu-HA score and response to highdose conditioning
chemotherapy and autologous stem cell transplantation.
This confirms recently published data that gene expression
profiling alone is not sufficient for predicting treatment
response [67, 68]. Overall, these data show the translation
of gene signatures obtained after in vivo treatment of
mice with epigenetic modulating agents that can be used
to predict overall survival of previously untreated and
relapsed MM patients. This furthermore validates the
use of the 5T33MM model for evaluating the response
towards epigenetic modulating agents and provides the
framework for testing other epigenetic modulating agents
with other or more specific targets.

Subsequently, we studied which factor(s) are
responsible for the separation of MM patients. The
observation that patients from the “PR” MM subgroup
linked with a bad prognosis have higher Mu-DM and/or
Mu-HA scores compared to all other subgroups indicates
an association of high risk with proliferation and is in
line with the results of the *in vitro* obtained DM- and
HA-score [35, 59, 69]. The gradual higher score values
in the *GPI*^high^ and *GPI*^medium^ group compared to the *GPI*^low^
group furthermore confirm the link between a high score
and high proliferation [5]. We also compared transitional
populations from memory B-cells to plasma cells for
their score values. Pre-plasmablasts and plasmablasts
are characterized by a higher proliferation rate compared
to memory B-cells, early plasma cells and BMPCs [62,
70]. In addition, HMLCs were developed from primary
MM samples which escaped the BM dependency and are
highly proliferative [58, 71]. Our observation that pre-
plasmablasts and HMCL displayed the highest Mu-DM
and Mu-HA scores again indicates a link between a high
score and proliferation. Overall, this validates our earlier
work with the DM-score and HA-score [35, 59, 69] and
suggests an association between the expression levels of
epigenetically regulated genes and a high risk linked with

a high proliferative and immature plasma cell phenotype.

Recently, the existence of plasma cell progenitors that recapitulate the different maturation stages of plasma cell differentiation was described within the BM of MM patients [62, 70] and was associated with proteasome inhibitor resistance [61, 72]. MM progenitors including B cells and pre-plasmablasts were found to survive treatment with proteasome inhibitors and were significantly enriched in MM patients refractory to bortezomib treatment. These Xbp1s negative pre-plasmablastic cells are characterized by a diminished endoplasmic reticulum stress and thus resistance to proteasome inhibitors since they are not committed to high Ig production [72, 73]. Furthermore, plasmablastic progenitors have been described to overexpress epigenetic regulators, compared to mature plasma cells, suggesting that transitions in plasma cell differentiation stages could be linked to epigenetic plasticity [61]. Thus, HDACi or DNMTi combined treatment could influence the plasticity of plasma cell progenitors and potentially target tumor progenitors that contribute to treatment failure in MM. Moreover, the scores presented here may be useful to predict the presence of such immature plasma cell populations.

In line with previous *in vitro* gene-expression
profiling studies, we found associations of *in vivo*
HDACi- and DNMTi-deregulated genes with biogenesis,
cytoskeletal organization and immunological pathways
[20, 35, 59]. In particular, we provide evidence that
quisinostat (and to a lesser extend decitabine) induces
tumoral transcriptional changes of genes involved
in immune response pathways, namely lymphocyte
activation and proliferation, immune-effector processes
and T-helper-1 development. The immune system is
suppressed in MM and studies focusing on reversing
the suppressive state to boost immune-mediated anti-
tumor responses are ongoing [74]. Emerging evidence
indicates that epigenetics plays an important role
in various aspects of the immune system including
cytokine production, dendritic cell activation, plasticity
of CD4+ T cells, regulatory T cell function and plasma
cell differentiation [75, 76]. In line, recent pre-clinical
work has demonstrated that epigenetic modulating
agents have immunomodulatory effects. West et al.
suggested that HDACi require an intact immune system
for long term effects in murine cancer models [43].
The authors demonstrated that the HDACi vorinostat
mediated immunogenic anti-tumor effects through IFN-γ
production by B-cells [77]. These effects were moreover
enhanced by α-galactosylceramide and may imply a
role for NKT cells [77, 78]. Vorinostat was also shown
to possess immunomodulatory properties in isolated
peripheral blood mononuclear cells and in mice after
lipopolysaccharide exposure [79]. Interestingly, in AML
patients, panoinostat in combination with azacytidine
decreased TNFR2+ regulatory T-cell populations
associated with IFN-γ and IL-2 induction [80]. Taken
together, our data and work by others supports the use of HDACi in combination with immunomodulatory therapies such as IMiDs, transplantation, humoral or cellular/peptide vaccines as recently suggested [77, 81].

So far, a few pre-clinical studies demonstrated combinatorial effects of HDACi and IMiDs in MM but lack mechanistic evidence. Vorinostat in combination with the thalidomide derivate IMiD1 showed combinatorial effects in vitro [82]. In addition, panobinostat enhanced anti-MM effects of lenalidomide and dexamethasone in vitro and in vivo [83]. Vorinostat or panobinostat have been shown to present synergistic effects in combination with the immune cell stimulating antibodies anti-CD40 and anti-CD137 in immunocompetent models of mammary, renal and colon carcinoma [84]. Very recently, in a murine melanoma model, panobinostat was demonstrated to synergize with an adoptive T-cell transfer leading to systemic immune responses to reduce melanoma tumor burden [81]. Panobinostat influenced T-cell populations and systemic cytokine production independent from the presence of tumor [81]. The above work suggests the potential of combining HDACi with immunotherapy strategies. Nevertheless, future pre-clinical in vivo work is mandatory to further address this possibility. Regarding safety issues, it was only recently shown that the combination of vorinostat and lenalidomide plus dexamethasone is well tolerated in MM patients in a phase I clinical trial [85].

In conclusion, in vivo treatment of MM cells with epigenetic modulating agents results in a transcriptional response that can be linked to prognosis. This prognostic signature was used to construct a gene expression based risk score allowing risk stratification of newly diagnosed patients. In addition, HDACi treatment (and to a lesser extent DNMTi) resulted in a wide transcriptional response involving overlapping gene signatures mainly mediating immune response. This indicates that transcriptional immune regulation is an important in vivo biological response of tumor cells towards HDACi and supports the rationale for the combination of HDACi and immunomodulatory therapies.

# MATERIALS AND METHODS

## Drugs

Decitabine (Dacogen) and quisinostat (JNJ-26481585) were kindly provided by Johnson & Johnson (Beerse, Belgium) and used as a filter sterilized 10% hydroxypropyl-cyclodextran suspension.

## Treatment and isolation of murine 5T33MM cells

C57BL/KaLwRij mice were purchased from Harlan CPB (Horst, The Netherlands). Mice were

housed according to the conditions approved by the Ethical Committee for Animal Experiments of the Vrije Universiteit Brussel (license no. LA1230281). The 5T33MM model was maintained as previously described [86]. At day 0, naive C57BL/KaLwRij mice were injected with 5x10⁵ 5T33MM cells. At established disease (day 16), mice were treated with decitabine (0.2mg/kg) (intraperitoneal injection, daily) or quisinostat (1.5mg/kg) (subcutaneous injection, once every other day). After 5 days, mice were sacrificed. Bone marrow was isolated from hind legs and subjected to red blood cell lysis. For mRNA analysis, tumor cells were purified by depletion of CD11b+ contaminating cells. An overall mean purity of plasma cells of above 90% was obtained (Supplementary Figure S1C). Cytospins were made before and after depletion to count the percentage of plasma cells as described previously [32].

## Gene expression profiling

Samples with more than 95% plasma cells (N=4 in each group) were used for RNA isolation using the RNeasy Kit (Qiagen, Venlo, The Netherlands). RNA was further processed and hybridized to the Mouse Genome 430 2.0 Array (Affymetrix, Santa Clara, CA, USA) as described in Moreaux et. al [35]. Microarray data are available at ArrayExpress database (Accession number: E-MTAB-3178).

## Microarray data of primary multiple myeloma cells and human myeloma cell lines

Affymetrix data of two independent cohorts of previously untreated MM patients was used. The training cohort consists of 206 MM patients and is termed the Heidelberg-Montpellier (HM)-cohort. This cohort also includes 7 bone marrow plasma cell (BMPC) samples from healthy donors. The samples were obtained after written informed consent in accordance with the Declaration of Helsinki and after approval of the ethics committee of Montpellier and Heidelberg. These data are publically available through ArrayExpress database (E-MTAB-372). The validation cohort contains 345 MM patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA) and is termed the TT2-cohort. These data can be accessed at the online Gene Expression Omnibus (GSE2658). In the HM study, patients underwent front line high-dose conditioning chemotherapy with 200mg/m² melphalan and autologous stem cell transplantation [5, 56]. In the UAMS study, patients received thalidomide or placebo in combination with 4 consecutive induction cycles containing (i) vincristine + doxorubicin + dexamethasone, (ii) cyclophosphamide + etoposide + cisplatin + dexamethasone, (iii) cyclophosphamide + doxorubicin,
and (iv) cyclophosphamide + etoposide + cisplatin +
dexamethasone (corresponding to total therapy 2 (TT2)).
This was followed by high-dose melphalan and autologous
stem cell transplantation [54, 87, 88]. The presence of
cytogenetic abnormalities was evaluated by iFISH in
the patients of the HM cohort. For TT2, the presence of
t(4;14) was predicted by *MMSET* spike expression [89].
We also used Affymetrix data of 152 relapsed MM patients
subsequently treated with bortezomib (GSE9782) from the
study by Mulligan *et al*. [57]. The clinical characteristics
of the cohorts have been described previously and are
summarized in Supplementary Table S8 [54-57]. In
addition, we used the Affymetrix data of 40 human
myeloma cell lines (HMCLs) stored in the ArrayExpress
database (E-TABM-937 and E-TABM-1088) [58]. The
derivation of the HMCLs as well as their phenotypic and
molecular characteristics were previously published [58].

Statistical analysis and bio-informatics

Normalization of gene expression data was done
by the MAS5 algorithm (scale 100) and analyzed by
Significance of Microarray Analysis (SAM) [90] and bio-
informatics platforms RAGE and Amazonia [91, 92]. The
Benjamini-Hochberg multiple testing corrections were
done for estimation of false discovery rate (FDR). The
prognostic value of the genes in terms of overall survival
(OS) was determined using the Maxstat R package.
Significance was determined by log-rank test followed
by Benjamin-Hochberg multiple testing correction. To
combine the prognostic value of all those genes, a score
was constructed and termed the murine DNA methylation
(Mu-DM) and histone acetylation (Mu-HA) score. The
scores are the sum of the Cox β-coefficients of the human
orthologs of the decitabine- or quisinostat-deregulated
genes with prognostic value in the training cohort,
weighted by + or - 1 if the patients' MM cell MAS5 signal
for any given gene is above or below the probe set Maxstat
cutoff of this gene [35, 59, 89]. Results were plotted using
the Kaplan-Meier method. Multivariate analysis of the
scores was done using Cox proportional hazard model.
The above analyses were performed using R 2.15.1
and Bioconductor 2.0 software. Gene ontology (GO)
analysis was done by the online Database for Annotation,
Visualization and Integrated Discovery (DAVID) [63, 64].
Pathway analysis was conducted by Reactome software,
Pathway-Guide and STRING analysis.

ACKNOWLEDGMENTS

We thank Carine Seynaeve for her expert technical
assistance. Decitabine and quisinostat were kindly
provided by Johnson & Johnson (Beerse, Belgium).

This work is funded by a Brian D. Novis research
grant of the International Myeloma Foundation, Vlaamse

Liga tegen Kanker, Stichting tegen kanker, Fonds voor
Wetenschappelijk Onderzoek, ARC (SF120121205949,
Paris, France), ANR emergence (ETTMM), CRLR grant
(R14026FF), 7th EU-framework program “OverMyR”, the
German Research Foundation (DFG; SFB/TRR79) and
the Federal Ministry of Education and Research (BMBF;
CAMPSIMM and CLIOMMICS), Bonn, Germany.
Elke De Bruyne and Els Van Valckenborgh are fellows
of FWO-Vlaanderen. This work was published with the
support of the University Foundation Belgium.

Conflict-of-interest disclosure

Dirk Hose and Anja Seckinger receive research
support from Novartis. The other authors declare no
competing financial interests.

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