ALBERT

Description: Mantle cell lymphoma (MCL) is a mostly incurable malignancy arising from naive B cells (NBCs) in the mantle zone of lymph nodes. We analyzed genomewide methylation in MCL patients with the HELP (HpaII tiny fragment Enrichment by Ligation–mediated PCR) assay and found significant aberrancy in promoter methylation patterns compared with normal NBCs. Using biologic and statistical criteria, we further identified 4 hypermethylated genes CDKN2B, MLF-1, PCDH8, and HOXD8 and 4 hypomethylated genes CD37, HDAC1, NOTCH1, and CDK5 when aberrant methylation was associated with inverse changes in mRNA levels. Immunohistochemical analysis of an independent cohort of MCL patient samples confirmed CD37 surface expression in 93% of patients, validating its selection as a target for MCL therapy. Treatment of MCL cell lines with a small modular immunopharmaceutical (CD37-SMIP) resulted in significant loss of viability in cell lines with intense surface CD37 expression. Treatment of MCL cell lines with the DNA methyltransferase inhibitor decitabine resulted in reversal of aberrant hypermethylation and synergized with the histone deacetylase inhibitor suberoylanilide hydroxamic acid in induction of the hypermethylated genes and anti-MCL cytotoxicity. Our data show prominent and aberrant promoter methylation in MCL and suggest that differentially methylated genes can be targeted for therapeutic benefit in MCL.

Description: Abstract 436 mTOR inhibitors have been used with clinical success in solid tumors and non-Hodgkin lymphoma (NHL), and are attractive therapeutic options for DLBCL (diffuse large B-cell lymphoma, which has been shown to have constitutively active mTOR signaling). However, resistance to this class of agents remains problematic, and mechanisms of resistance are poorly understood. We performed candidate drug discovery using connectivity mapping and global gene expression profiling (GEP) to understand the pathways and genes responsible for resistance to the mTOR inhibitor Rapamycin (Sirolimus), which is the active metabolite of several clinically available mTOR inhibitors (eg, Temsirolimus, Everolimus). Treatment of DLBCL cell lines by Rapamycin at varying doses permitted stratification of cell lines into 2 groups of 3 cell lines each: sensitive (SU-DHL6, WSU-NHL, and Karpas-422) and resistant (SU-DHL4, OCI-Ly19, and Farage). Using the Affymetrix Human Gene 1.0 ST Array, we generated a profile of 1164 differentially-expressed genes (P

Description: Abstract 435 Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoid malignancy, representing approximately 30–40% of all lymphomas. While significant progress has been made in treating this disease over the past decade, it is still regarded as a heterogeneous disease which, after being classified as relapsed or refractory, is fatal in about one-third of patients. Histone deacetylase inhibitors (HDACI) are presently approved for the treatment of relapsed or refractory cutaneous T- cell lymphomas (CTCL), and have marked activity in peripheral T-cell lymphomas (PTCL), though their effectiveness in DLBCL is less established. DNA methyltransferases (DNMTs) are known to recruit and cooperate with histone deacetylases to induce gene silencing. Combinations of drugs affecting these pathways have emerged as active and important, mostly in myeloid leukemias. We hypothesized that the combination of HDACI and DNMT inhibitors (DNMTI) in DLBCL may be active only in combination and not as single agents. We examined the interaction between a broad range of HDACI including vorinostat, depsipeptide, panobinostat and DNMTI using in vivo and in vitro models of DLBCLs, clearly confirming that these agents are in fact synergistic with decitabine. Synergy was measured by relative risk ratio (RRR) and the values obtained were as low as 0.01, representing very strong synergy. This combination of drugs, specifically panobinostat and decitabine, was also shown to be strongly synergistic in a murine xenograft model of DLBCL. In addition, we analyzed the molecular basis for this synergistic effect by evaluating the global gene expression and methylation using microarrays on the cells treated with the single agents and combination in DLBCL. Three DLBCL lines (OCI-Ly1, OCI-Ly10 and Su-DHL6) were treated with decitabine alone (2.5 μ M), panobinostat alone (2.5 nM) or their combination for 48h hours. DNA and RNA from untreated and treated cells were used for genome wide methylation analysis through Illumina Humanmethyation27 platform and gene expression profiling analysis with Illumina HumanHT-12 v3 Expression arrays. 3D principal component analysis clearly clustered the samples treated with panobinostat and combination therapy together and at greater distances from untreated samples and samples treated by decitabine alone. Therefore, the contribution to the gene expression phenotype of the combination was greater from the HDACI than with DNMTI. Consistent with this observation, the top network of genes differentially expressed (p

Description: MCL (Mantle cell lymphoma) is an aggressive and incurable B cell malignancy with a median survival of 5-6 years. Cyclin D1 (CCND1) overexpression is a key diagnostic feature of this disease, observed in more than 90% of MCL tumors. However, murine models over-expressing CCND1 in B cells do not recapitulate the phenotype of MCL. The SOX11 transcription factor is aberrantly expressed in 80-90% of primary MCL. Our published data demonstrated that SOX11 binds and functionally regulates key components in multiple oncogenic pathways in MCL such as WNT and TGFβ pathways. Recent studies have also showed that SOX11 regulates PAX5 and PDGFA to block differentiation and facilitate lymphoma growth. We thus hypothesize that SOX11 expression may contribute directly and functionally cooperate with CCND1 in MCL pathogenesis. To study the role of SOX11 in MCL tumorigenesis in vivo, we have generated a novel SOX11 transgenic mouse model with B cell-specific tissue expression under the E-mu enhancer and an IRES-eGFP tag to monitor the expression of SOX11. The presence of SOX11 can be readily detected in pre-pro-B stage in the bone marrow coincided with the activation of E-mu enhancer and was persistent through all stages of B cells. SOX11 over-expression in our mouse model led to an aberrant oligo-clonal expansion of CD19+/CD5+ B cells. This phenotype was evident in all SOX11 transgenic mice studied (100% penetrance, n= 42 mice) with an average of 7-12 fold increase (p

Description: Interactions between histone deacetylase inhibitors (HDACIs) and decitabine were investigated in models of diffuse large B-cell lymphoma (DLBCL). A number of cell lines representing both germinal center B-like and activated B-cell like DLBCL, patient-derived tumor cells and a murine xenograft model were used to study the effects of HDACIs and decitabine in this system. All explored HDACIs in combination with decitabine produced a synergistic effect in growth inhibition and induction of apoptosis in DLBCL cells. This effect was time dependent, mediated via caspase-3 activation, and resulted in increased levels of acetylated histones. Synergy in inducing apoptosis was confirmed in patient-derived primary tumor cells treated with panobinostat and decitabine. Xenografting experiments confirmed the in vitro activity and tolerability of the combination. We analyzed the molecular basis for this synergistic effect by evaluating gene-expression and methylation patterns using microarrays, with validation by bisulfite sequencing. These analyses revealed differentially expressed genes and networks identified by each of the single treatment conditions and by the combination therapy to be unique with few overlapping genes. Among the genes uniquely altered by the combination of panobinostat and decitabine were VHL, TCEB1, WT1, and DIRAS3.

Description: RNA editing is an epitranscriptomic modification of emerging relevance to disease development and manifestations. Here we identify a novel role of the RNA editing enzyme ADAR1 in multiple myeloma (MM) progression as inducer of cognate DNA mutations. We have previously demonstrated (Lagana et al, ASH 2017) that ADAR1, which resides on human chromosome 1q21, is an RNA editor whose over-expression, either by IFN induction or through gene amplification, is associated with poor outcomes in MM. We now demonstrate robust and reproducible ADAR-mediated RNA editing in MM that increases with disease progression. At the same time, since disease progression is also correlated with the acquisition of new mutations, we asked whether ADAR1 could play the dual role of RNA editor and DNA mutator in MM, especially in the context of relapse. In fact, previous work has revealed that ADAR can exert its functions by acting on DNA/RNA hybrids in vitro (Zheng et al, Nucleic Acids Research 2017), and that DNA mutations at edited sites occur more often than at unedited sites in human and D melanogaster (Popitsch et al, BioRxiv 2017). We performed a careful bioinformatic dissection of matched pre-and post-relapse samples from 21 patients in the MMRF CoMMpass Study. Samples were profiled both with whole-exome sequencing (WES) to identify DNA mutations, and with RNAseq to identify editing instances. WES raw data was processed according to GATK Best Practices to generate alignment files, which were then processed with Samtools to identify mutations. RNAseq data was mapped using the tool GSNAP and processed using REDItools to identify editing events. Downstream analysis revealed a correlation between locations of RNA editing at diagnosis and of DNA mutation at relapse, with regions mutated matching known MM mutational hotspots in genes participating in several pathways that are relevant in MM, such as IFNa, IFNg response, IL2-STAT5 and TNF-NFkB. Finally, we demonstrated that editing at those locations is reproducible in a number of tumor cell lines, and that targeted editing of those locations could also result in the generation of mutations, similar to those we observed from patient data. Overall, we have shown that the RNA editor ADAR1, can also mutate the DNA cognate to the targeted transcript, generating specific mutational signatures at predetermined locations. We further hypothesize that this dual role of RNA editor and DNA mutator might be shared by other deaminases, and we suggest that in some contexts, DNA mutation might be the result of collateral damage on the genome by an editing enzyme whose primary job is to re-code the cognate transcript toward specific functional outcomes. Disclosures Madduri: undation Medicine: Consultancy; Celgene: Consultancy; Abbvie: Consultancy; Takeda: Consultancy. Richter:Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Speakers Bureau; Bristol-Meyers Squibb: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Chari:Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Research Funding; Oncoceutics: Research Funding; Novartis Pharmaceuticals: Research Funding; GlaxoSmithKline: Research Funding; Array Biopharma: Research Funding; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cho:Agenus: Research Funding; Genentech: Honoraria, Research Funding; BMS: Consultancy; GSK: Consultancy; Takeda: Research Funding; Celgene: Honoraria, Research Funding; The Multiple Myeloma Research Foundation: Employment. Jagannath:Celgene: Consultancy; Novartis: Consultancy; Merck: Consultancy; Medicom: Speakers Bureau; Multiple Myeloma Research Foundation: Speakers Bureau; BMS: Consultancy. Parekh:Foundation Medicine Inc.: Consultancy; Karyopharm Inc.: Research Funding; Celgene Corporation: Research Funding.

Description: Selinexor (KPT-330) is a selective inhibitor of nuclear export (SINE) which specifically targets XPO1 (Exportin 1)-mediated nuclear export, leading to increased nuclear retention of major tumor suppressor proteins and inducing selective apoptosis in cancer cells. Several phase I and II clinical trials demonstrate evidence of anti-cancer activity of Selinexor in solid tumors (i.e metastatic prostate cancer (PMID: 29487219), advanced refractory bone or soft tissue sarcoma (PMID: 27458288) and non-small cell lung cancer (PMID: 28647672); as well as, hematological malignancies, including non-Hodgkin lymphoma (PMID: 28468797), acute myeloid leukemia (PMID: 29304833) and multiple myeloma (MM) (PMID: 29381435). In the STORM (Selinexor Treatment of Refractory Myeloma) trial, the combination of Selinexor with dexamethasone in MM patients refractory to bortezomib, carfilzomib, lenalidomide and pomalidomide (quad-refractory), or in addition, to daratumumab (penta-refractory), has shown an overall response rate (ORR) of 21% (Vogl et al, JCO 2018). Our objective is to identify biomarkers for selection of patients at higher likelihood of clinical benefit from Selinexor salvage and understand mechanisms of Selinexor resistance. We therefore analyzed transcriptional differences using RNA sequencing in CD138+ cells from bone marrow aspirates obtained prior to treatment from 32 MM patients enrolled in STORM. The raw data (fastq) was mapped by using the tool STAR and gene-level annotated by featureCounts. Patients were split in two groups based on their progression-free survival (PFS). Differential expression analysis was performed using the tool DESeq2, which enables a more quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The results revealed significant up-regulation of 13 genes in patients with PFS 〈 120 days (n = 21, p 〈 0.05) versus patients with PFS 〉 120 days (n=11), including the transcription factor E2F1 and its targets MYBL2, FANCA, GINS3 and SLX4 (Fig. 1). Next, we evaluated the expression of E2F1 in another set of 26 patients from the STORM trial by Affymetrix U133 gene expression microarrays. Data was analyzed using the Signal Space Transformation (SST)-Robust Multi-Chip Analysis (RMA) algorithm. Patients with PFS 〈 120 days (n = 19) exhibited significant up-regulation of E2F1 (p 〈 0.05) (Fig. 2). E2F1 is a transcription factor that regulates cell cycle G1/S progression. At rest, E2F1 is complexed with its negative regulator retinoblastoma(RB) protein. Upon phosphorylation of RB by the Cyclin D1-CDK4/6 complex, pRB is inactivated allowing E2F1 to commence transcription of target genes allowing G1/S progression. E2F transcription factors are exported by XPO1 from the nucleus to the cytoplasm. We treated RPMI8226 (IC50=150nM) and MM1S (IC50=25nM) human myeloma cell lines with Selinexor at IC50 and examined nuclear vs cytoplasmic expression of E2F1 after 24 and 48 hours by western blotting. Our results demonstrated nuclear retention of E2F1 following treatment of HMCLs with Selinexor and suggest a model where overexpression of E2F1 overwhelms the nuclear export mechanism and may result in downstream gene programming that confers a proliferative advantage in cells, manifested by rapid progression (

Description: Selinexor is the first FDA-approved drug for penta-refractory multiple myeloma (MM) patients, i.e. patients refractory to at least two proteasome inhibitors, two immunomodulatory drugs, and an anti-CD38 monoclonal antibody. Selinexor is an oral selective inhibitor of nuclear export (SINE), which specifically targets XPO1 (Exportin 1)-mediated nuclear export, leading to increased nuclear accumulation and activation of major tumor suppressor proteins, inhibition of NF-kB, and inducing selective apoptosis in cancer cells. Several phase I and II clinical trials demonstrated evidence of anti-cancer activity of Selinexor in solid tumors, non-Hodgkin lymphoma, acute myeloid leukemia and MM (PMIDs: 29487219, 27458288, 28647672, 28468797, 29304833, 29381435). In the pivotal phase 2b STORM (Selinexor Treatment of Refractory Myeloma) trial, the combination of Selinexor with dexamethasone in patients with MM treated with prior bortezomib, carfilzomib, lenalidomide, pomalidomide and daratumumab (penta-exposed) and triple class refractory MM, has shown an overall response rate (ORR) of 26.2% and a clinical benefit rate of 39.3%, with a median progression-free survival (PFS) of 3.7 months and median overall survival (OR) of 8.6 months (Chari et al, NEJM in press 2019). In order to identify biomarkers for selection of patients at higher likelihood of clinical benefit from Selinexor therapy and understand mechanisms of Selinexor resistance, we analyzed the transcriptome of CD138+ cells from bone marrow aspirates obtained prior to treatment from 54 patients enrolled in STORM. The raw data (fastq) was mapped using the tool STAR and gene-level annotated using the tool featureCounts. Gene expression counts were normalized using the variance stabilization transformation (VST) method implemented in the tool DESeq2, including potential confounding variables such as sequencing batch as covariates. Patients were split in two groups based on PFS = 120 days. We trained a linear Support Vector Machine (SVM) with L1 regularization to select features discriminating between responders (PFS 〉 120, n = 17) and non-responders (PFS 〈 120, n = 37). The L1 regularization reduces data dimensionality in large dataset and enables selection of the most relevant features. We trained the SVM using 80% (43) of the samples for feature selection and training with leave-one-out cross validation, and the remaining 20% (11) for model testing. The SVM achieved an AUC (Area Under ROC Curve) value of 0.71 on the test set. The model consisted of 30 genes which identified 3 patient clusters with significantly different PFS (Fig 1a, 1b). The cluster with poorer PFS was characterized by up-regulation of MAGE-A1. MAGE-A is a cancer testis antigen that is aberrantly expressed in MM (PMID: 21565982) and has recently been described to have a critical role in chemotherapy resistance via regulation of BIM and p53 (Chari et al, Blood Advances 2017; Cho et al, ASH 2018). To test whether MAGE-A may contribute mechanistically to Selinexor resistance, we treated MM cells (H929 and RPMI8226) with Selinexor after depletion of MAGE-A by RNA interference. Our results show decreased viability in MAGE-A depleted cells as compared to non-target siRNA, suggesting that MAGE-A depletion increases sensitivity to Selinexor. Taken together, our results provide a signature for selecting patients that may benefit from Selinexor therapy and provide insights into mechanisms of Selinexor resistance. We are currently performing whole-exome sequencing profiling of the patients in this study and will present the results at ASH 2019. Disclosures Landesman: Karyopharm Therapeutics Inc: Employment. Madduri:undation Medicine: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Abbvie: Consultancy. Richter:Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Speakers Bureau; Bristol-Meyers Squibb: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Chari:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Array Biopharma: Research Funding; GlaxoSmithKline: Research Funding; Novartis Pharmaceuticals: Research Funding; Oncoceutics: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees. Cho:BMS: Consultancy; The Multiple Myeloma Research Foundation: Employment; Takeda: Research Funding; GSK: Consultancy; Celgene: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Agenus: Research Funding. Jagannath:BMS: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Merck: Consultancy; Medicom: Speakers Bureau; Multiple Myeloma Research Foundation: Speakers Bureau. Parekh:Karyopharm Inc.: Research Funding; Foundation Medicine Inc.: Consultancy; Celgene Corporation: Research Funding.