Stewart, A. Keith

Introduction Despite continuous improvement of clinical outcome in multiple myeloma (MM), disease relapse remains a major challenge, leading to progressively shorter remissions and fewer treatment options. Strategies attempting to counteract this challenge include recent efforts resulting in an increase in the availability of novel promising anti-MM agents and targeting specific genetic profiles of the disease. In this context, we aim to develop predictive models of sensitivity and resistance to novel compounds by connecting an ex vivo high-throughput drug screen with genetic, transcriptomics, FISH, and clinical features. Methods Twenty compounds (afatinib, afuresertib, belinostat, buparlisib, cobimetinib, CPI-0610, crenolanib, dinaciclib, dovitinib, JQ1, LGH447, osimertinib, OTX015, panobinostat, romidepsin, selinexor, sunitinib, trametinib, venetoclax, and vorinostat) were selected based on overall promising anti-MM activity from an ex vivo high throughput drug screen with a panel of 79 single agents incubated for 24 hours. The area under the curve (AUC) was used to rank order the ex vivo responses for each compound and the lowest and highest quartile samples were identified for further analysis. Clinical data and FISH data, including t(11;14), t(4;14), t(14;16), del(17p), +1q, monosomy 13, and MYC rearrangement, were collected. Targeted DNA sequencing was performed using a 2.3 Mb custom capture panel covering 139 MM-relevant genes. mRNA-sequencing was performed and differential gene expression analysis in the highest and lowest quartile identified subsets of markers positively and negatively associated with the AUC response for a given compound. An additional unbiased selection of markers using lasso techniques was performed, resulting in predictive generalized linear models (GLM) for each agent. Responses from the remaining intermediate samples were estimated with the predictive models, with overall predictive ability assessed by correlating predicted AUCs with their actual counterparts. Results Our integrative analysis was performed on 50 primary patient samples (36% untreated and 64% relapsed MM). Venetoclax, dinaciclib, romidepsin, panobinostat, osimertinib, belinostat and selinexor were the most active compounds in the cohort. Interestingly, LGH447, dovitinib, selinexor, JQ1, OTX-015, cobimetinib, and trametinib showed increased activity in relapsed MM when compared to untreated samples (Wilcoxon Test; p<0.05). We generated GLMs using an average of 92 markers (range 64-107) per compound, combining mRNA-sequencing expression with FISH and mutation data. The analysis proposed in the present study was validated through the unbiased selection of BCL2 among the subset of markers included in the GLM predicting sensitivity to venetoclax, a first-in-class orally bioavailable selective BCL2 inhibitor. Expression level of critical NF-kB and cell cycle genes, such as BIRC3, CKS1B, PAX5, NFKB2, and CCND2, were included in 60% of our predictive models. Mutations of DNA repair genes (ATM,TP53) were included in the GLMs of three epigenetic therapies, one histone deacetylase inhibitor and two BET inhibitors, associated to ex vivo resistance to the drugs. The presence of monosomy 13 was also a marker for ex vivo resistance for five epigenetic therapies, four HDAC inhibitors and one BET inhibitor. The three BET inhibitors, JQ1, CPI-0610, and OTX015, were among the compounds most accurately predicted by our integrative approach, with Spearman correlation values between 0.773-0.858. Overall, our models accurately predicted the ex vivo response for 16 (80%) of the compounds (r>0.7). Five (25%) of these compounds displayed a remarkably accurate prediction model in both training (highest and lowest quartiles) and validation (intermediate quartiles) samples (r>0.8). Conclusions The GLM data integration approach enabled the establishment of effective predictive models, identifying FISH, transcriptomics, and mutations of putative driver genes important in anti-MM agent responsiveness. In addition, the resulting dataset is promising for future research focusing on the discovery of novel mechanisms of action and establishing markers of sensitivity and resistance to novel compounds. We are currently increasing our dataset and seek to create an omnibus approach that predicts responses to multiple anti-MM agents simultaneously. Disclosures Bergsagel: Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Stewart:Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Ono: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy.

Abstract MYC expression is frequently dysregulated in multiple myeloma (MM). In one comprehensive study, MYC structural variations (SV) were found in nearly half of MM cases (Affer et al. Leukemia 2014). The prevalence was higher in hyperdiploid (HRD) tumors (65%) compared to non-hyperdiploid (NHRD) tumors (36%). The large amount of tumor DNA required for all of the genomic studies performed may have biased the samples analyzed (e.g., to those with higher tumor burdens). To validate the findings of recurrent MYC SV in another dataset, we analyzed the CoMMpass data. We analyzed long-insert whole genome sequencing (WGS) data from diagnostic samples in 420 patients from the IA7 release (dbGAP phs000748) for SV. The results of clinical data, processed WES (SNV), RNASeq (gene expression) and WGS (copy number) data from IA8 (http://research.themmrf.org) were used to calculate survival, RAS and NFKB pathway mutation (WES), TC class (RNA), NFKB index (RNA) and hyperdiploid index (WGS). MYC SVs were identified in 38% of tumors. They were present in 53% HRD and 28% NHRD, and by TC in 55% D1, 43% D2, 36% MMSET, 26% MAF, 13% CCND. Juxtaposition of an Ig enhancer (IgH, IgK, IgL) close to MYC was the most common MYC SV, representing ~40% of the MYC SV. Other enhancers identified have mostly been reported previously, with the most frequent being NSMCE2 (12% of SV) and TXNDC5 (5% of SV). Intrachromosomal SV (deletions, inversions, duplications) not associated with any known enhancer were also frequent (18% of SV). As expected, MYC expression was higher in tumors with MYC SV compared to those without (~2.4 fold, p-value<0.0001). We used Gene Set Enrichment Analysis (GSEA) to identify activated pathways that might substitute for MYC in HRD patients without MYC SV and observed significant activation of the NFkB pathway. Interestingly, examining patients with RAS/MAPK pathway (NRAS, KRAS, BRAF, FGFR3 - abbreviated RAS) mutations (identified in 50% of all patients) the same pattern was observed: in the absence of RAS mutation, there was a significantly higher NFkB index. In general, the HRD tumors seem to have either activation of RAS and/or MYC, or activation of NFkB (Figure). There was no difference in overall survival in patients with versus those without MYC SV. We developed a clinically applicable sequencing platform to identify MYC SV, which cannot be reliably identified by FISH. We sought to validate this targeted capture approach, where in addition to the coding exons of 81 interesting genes described previously (M3P), we also pulled down the region surrounding MYC (2 Mb), IgH (0.5 Mb), IgK (50 kb) and IgL (100 kb) allowing us to additionally identify SV in MYC and Ig loci. Using this approach we identified IgH translocations in 29/30 samples with translocations previously identified by FISH (97%). Moreover, we identified MYC SV in 19/22 patients with SV previously identified by mate-pair WGS (86%). Importantly, sequencing identified the precise translocation breakpoint, and identity of the enhancer dysregulating MYC, which may be important variables. In one informative patient two different MYC SV were present at diagnosis, only one of which was still present following a partial response to four cycles of lenalidomide and dexamethasone. This suggests that the two MYC SV are in different subclones, one of which was much more sensitive to the treatment. Interestingly, the enhancer dysregulating MYC in the sensitive subclone harbors 5 strong Ikaros binding sites identified by ChIPseq, suggesting one intriguing mechanism for sensitivity to lenalidomide. To summarize, we verified in a large dataset that MYC expression is frequently dysregulated by SV in MM (38%), and the RAS/MAPK (50%) and NFKB (23%) pathways are frequently activated by mutations. Surprisingly, given their generally good prognosis, nearly half of HRD tumors seem to be MYC driven, while this is true for only a quarter of NHRD tumors. HRD tumors not driven by MYC or RAS appear to be driven by NFKB. Remarkably, the pathways most commonly activated by mutation in MM: CCND, MYC, RAS, NFKB are common to many cancers and have been studied extensively individually. To understand their clinical impact in MM we have developed a comprehensive custom capture sequencing panel that identified 97% of IgH translocations, 86% of MYC SV, and as well as SNV and CNV of 81 recurrently mutated genes. It will be important to include such a comprehensive genetic analysis to complement clinical trials in the future. Figure. Figure. Disclosures Stewart: Bristol Myers Squibb: Consultancy; Celgene: Consultancy; Takeda Oncology: Consultancy; Janssen Pharmaceuticals: Consultancy.

Abstract Abstract 1217 Poster Board I-239 Background Single autologous stem cell transplant (ASCT) is considered the standard of care after induction therapy for younger multiple myeloma (MM) patients (pts). However, it is not curative and virtually all patients will ultimately relapse. As more options are available to treat relapsed disease, the role of a second ASCT as salvage therapy is unclear. Method Retrospective review of all MM pts who received a 2nd ASCT as salvage therapy at Princess Margaret Hospital. Results Between February 1997 and July 2009, 79 MM pts received a second ASCT for relapsed MM at our institution. Median age was 60 yrs (range 39-72) at second transplant. 48 pts (61%) were male. Immunoglobulin subtype included IgG (42), IgA (21), light chain (11), nonsecretory (3), IgM(1) and IgD (1). Transplant conditioning regimen for first transplant was high dose melphalan (MEL) 140-200 mg/m2 in 67, busulphan and cyclophosphamide in 1, and combinations of MEL with etoposide (E) or TBI in the rest. 2nd ASCT conditioning consisted of MEL alone in 76, the remaining 3 had MEL with either TBI or E. The median time to relapse after the first transplant was 2.72 years (0.81-8.26), with a median interval between transplants of 3.61 years (1.63-9.59). Response to first transplant in 78 evaluable patients was 13 CR/nCR (17%), 56 PR/VGPR (72%), 9 MR/SD (12%). Nineteen pts received maintenance therapy between transplants. Two transplant-related deaths occurred following 2nd ASCT. In 73 evaluable patients, response to second transplant was 11 CR/nCR (15%), 57 PR (78%), 6 MR/SD (8%). After 2nd ASCT, with median follow up 5.92 years (71 months), median progression-free survival (PFS) was 18.5 months and median overall survival (OS) was 4.4 years. Long term progression-free status based on the progression-free interval after 1st ASCT is summarized Table 1. PFS based on progression free interval after 1st ASCT is outlined in Figure 1. Conclusions 2nd ASCT is a feasible and safe salvage therapy in relapsed MM patients. It is effective in providing a median progression free survival of 18.5 months and median overall survival of 4.4 years (52.8 months) after 2nd ASCT. This is comparable if not better than modern salvage chemotherapies. The longer the disease free interval after 1st ASCT the more effective 2nd ASCT is at extending both progression free survival and overall survival. It is reasonable, therefore, to consider a 2nd ASCT if the time to progression is greater than 2 years after first ASCT. Disclosures Chen: Celgene: Honoraria, Research Funding; Ortho Biotech: Honoraria. Trudel:Celgene: Honoraria, Speakers Bureau; Ortho Biotech: Honoraria. Kukreti:Celgene: Honoraria.

Abstract The oncogene c-maf is frequently over-expressed in multiple myeloma cell lines and patient samples and contributes to increased cellular proliferation in part by inducing cyclin D2 expression. Therefore, small molecules that inhibit c-maf and its targets could be useful chemical probes to better understand the role and regulation of this protein. We developed a high throughput chemical screen in NIH 3T3 cells stably over-expressing the promoter of the c-maf target cyclin D2 driving firefly luciferase. From a screen of 2400 off-patent drugs and chemicals, we identified 32 compounds that preferentially reduced cyclin D2 transactivation. Of these, 24 of the 32 hits belonged to the corticosteroid family of drugs. Indeed, the screen identified 24 of the 26 corticosteroids in the library. The most potent inhibitors were glucocorticoids such as dexamethasone. Mineralocorticoids such as fludrocortisone were weak hits, reflecting their weak glucocorticoid activity. The 24 glucocorticoids identified in this screen preferentially reduced cyclin D2 transactivation in the presence of c-maf. For example, the IC50 of dexamethasone was 11 ± 0.7 nM and >50 uM in NIH 3T3 cell with and without c-maf, respectively. Given the effects of glucocorticoids on c-maf in NIH 3T3 cells, we extended our investigation to multiple myeloma cell lines and demonstrated that nanomolar concentrations of the glucocorticoid dexamethasone reduced levels of c-maf protein and its target cyclin D2 within 6 hours of treatment. C-Maf was down regulated in isogenic MM1.S but not MM1.R cells respectively sensitive and resistant to dexamethasone. We also observed reductions in another c-maf target, beta integrin. Compared to cell lines RPMI 8226 and OCIMY5 that harbor the t(14;16) c-maf translocation, the concentration of dexamethasone required to reduce c-maf was approximately 50-fold lower in cell lines such as LP1 and OPM1 that lack the translocation but over-express c-maf. While dexamethasone reduced c-maf protein, no changes in levels of c-maf mRNA were detected. In both multiple myeloma and NIH3T3 cells, dexamethasone increased the ubiquitin-dependent destruction of c-maf. Finally, we linked glucocorticoids to c-maf ubiquitination by demonstrating that dexamethasone upregulated ubiquitin C mRNA at concentrations associated with the ubiquitination of c-maf. Moreover, ectopic expression of ubiquitin cDNA recapitulated the effects of dexamethasone and reduced levels of c-maf, suggesting that increased expression of ubiquitin C by dexamethasone is functionally important for dexamethasone’s effects on c-maf levels. Conclusion: a chemical biology screen identified glucocorticoids as c-maf dependent inhibitors of cyclin D2 transactivation. Glucocorticoids reduce c-maf by promoting its ubiquitination via the upregulation of ubiquitin C mRNA. This work provides new insights into the regulation of c-maf and has identified a novel mechanism by which glucocorticoids exert an anti-myeloma effect.

Abstract Abstract 1845 Poster Board I-871 Background: Proteasome inhibition is an effective strategy for the treatment of multiple myeloma. In patients, proteasome inhibition has primarily been measured in peripheral blood samples (whole blood or mononuclear cells). However, it is unknown whether myeloma cells in the bone marrow (BM) are equally sensitive to proteasome inhibitors such as bortezomib (BTZ) and carfilzomib (CFZ). Aim: To measure proteasome inhibition in purified tumor cells from BM samples taken from patients enrolled in two ongoing Phase 2 trials of single agent CFZ in relapsed or refractory myeloma: PX-171-003 (003) and PX-171-004 (004). Methods: CFZ was administered as an IV bolus of 20 mg/m2 on Days 1, 2, 8, 9, 15 and 16 of a 28-day cycle on both trials. Bone marrow samples, from an optional sub-study of both trials, were taken during screening and Day 2 (post-treatment) and sorted into CD138+ and CD138− cells. Proteasome activity was measured by an enzymatic assay using a fluorogenic substrate (LLVY-AMC) for the chymotrypsin-like (CT-L) activity and an active site ELISA (ProCISE) to quantitate levels of the CT-L subunits of the constitutive proteasome (Beta5) and immunoproteasome (LMP7) and the immunoproteasome subunit MECL1. Results: Whole blood samples from patients treated with CFZ showed inhibition of CT-L activity of ∼80+, similar to values obtained in Phase 1 studies. A total of 10 CD138+ screening samples, 6 from 004 and 4 from 003, and 9 post-dose samples, 5 from 004 and 4 from 003, were analyzed for proteasome levels and activity. In addition, 15 CD138−screening samples, 7 from 004 and 8 from 003, and 9 post-dose samples, 5 from 004 and 4 from 003, were analyzed. When compared to the average base-line activity, CFZ treatment resulted in 88% CT-L inhibition in CD-138+tumor cells from 004 patients (P = 0.0212 by unpaired t-test) and 59% CT-L inhibition in CD-138+ tumor cells from 003 patients (P = 0.25). Baseline CT-L activity in CD138+ tumor cells was 3-fold higher in 004 than 003, which includes a more heavily pre-treated patient population with greater prior exposure to BTZ. Higher specific enzymatic activity was due to increased levels of both constitutive and immunoproteasomes in tumor cells, where immunoproteasomes account for >75% of total cellular proteasomes. No differences between trials were seen in baseline CT-L activity from non-tumor (CD138−) cells. Inhibition in CD138− cells was 84% (P = 0.0380 and 42% (P = 0.38) in 004 and 003, respectively. Using ProCISE, we measured inhibition of LMP7 (66%), beta5 (48%) and MECL1 (64%) in CD138+ tumor cells from 004 patients. Three patients from 004 and one from 003 had both a screening and post-dose tumor cell samples available for analysis. Inhibition of CT-L activity was >80% in two of the 3 patients on 004; the third patient showed no proteasome inhibition by ProCISE and was unavailable for analysis by CT-L. CT-L activity in the CD138+ tumor cells in the 003 patient was not inhibited, however, inhibition was seen in non-tumor cells. Conclusions: CFZ inhibits the proteasome activity of myeloma cells in the bone marrow of relapsed and refractory myeloma patients. The levels of inhibition were similar to those measured in whole blood samples, supporting the use of the blood-based assay as a surrogate marker for proteasome inhibition in tumor cells. CFZ treatment resulted in inhibition of both CT-L subunits as well as additional subunits of the immunoproteasome in tumor cells. Reduced baseline activity in the more heavily pretreated 003 patients may reflect reduced tumor-dependency on the proteasome and may be related to prior treatment with BTZ in these patients. More samples are needed in order to make correlations between levels of proteasome inhibition in bone marrow tumor cells and prior therapies or response. These observations support further evaluation of proteasome activity and the effects of this promising new agent in primary tumors cells from myeloma patients. Disclosures: Trudel: Celgene: Honoraria, Speakers Bureau; Ortho Biotech: Honoraria. Lee:Proteolix, Inc.: Employment. Kirk:Proteolix, Inc.: Employment. Lonial:Celgene: Consultancy; Millennium: Consultancy, Research Funding; BMS: Consultancy; Novartis: Consultancy; Gloucester: Research Funding. Wang:Proteolix, Inc.: Research Funding. Kukreti:Celgene: Honoraria. Stewart:Genzyme, Celgene, Millenium, Proteolix: Honoraria; Takeda, Millenium: Research Funding; Takeda-Millenium, Celgene, Novartis, Amgen: Consultancy. Jagannath:Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. McDonagh:Proteolix: Research Funding. Zonder:Celgene: Speakers Bureau; Pfizer: Consultancy; Seattle Genetics, Inc.: Research Funding; Amgen: Consultancy; Millennium: Research Funding. Bennett:Proteolix: Employment.

Abstract Abstract 1802 Poster Board I-828 Background Heterogeneity in multiple myeloma (MM) leads to disparate outcomes with a wide variation in overall survival (OS). Based upon the evidence predominantly gathered from newly diagnosed patients treated with standard chemotherapy and/or transplantation, the current Mayo (mSMART) prognostic model uses a combination of metaphase cytogenetics, fluorescence in-situ hybridization (FISH) and plasma cell labeling index (PCLI) for risk-stratification at diagnosis of MM. The objective of our study was to evaluate the significance of this stratification model in the era of novel therapies, and to assess the independent prognostic value of each of the components in the model. Methods Data were abstracted from a cohort of 1556 consecutive patients with MM who presented to our institution between January 1999 and March 2009 within ninety days of diagnosis of MM A total of three hundred and eighty six patients had been evaluated with conventional cytogenetics near diagnosis in addition to interphase FISH. Out of this cohort, three hundred and sixty six patients had availability of all the three tests, including the slide-based PCLI. High-risk MM was defined as presence of any one or more of the following: cytogenetics: hypodiploidy, monosomy of chromosome 13 or deletion 13q ; FISH: deletion of p53 (locus 17p13) or immunoglobulin heavy chain (IgH) translocations, t(4;14) (p16.3;q32) or t(14;16)(q32;q23); PCLI ≥3%. Survival estimates were created using the Kaplan-Meier method and compared by log-rank tests. The impact of each of the three tests was assessed by a multivariate (MV) analysis using Cox proportional hazards model. Results The median OS of the cohort of 366 patients was 65 months from presentation. One hundred and twelve (31%) patients were deemed high risk by any one or more of the three tests: Metaphase cytogenetics, FISH or PCLI (current mSMART definition of high-risk MM). The median OS of high risk patients was 52 months versus 71 months for standard-risk (P=0.007). We then studied the prognostic value of the three tests separately. Patients classified as high risk by FISH had a median OS of 35 months versus 65 months for the standard risk, HR= 2.3, P<0.001. Similarly, cytogenetics alone was also able to classify patients into high and standard risk with a comparable HR of 2.1 (P=0.008, median survival 37 vs. 64 months). In contrast, PCLI did not have a significant prognostic value (HR =1.41, P=0.32). This was confirmed on multivariate analysis in a model that included all 3 tests. FISH (HR=2.36, P=0.001) and cytogenetics (HR=1.99; P=0.017) emerged as independent determinants of prognosis in the multivariable analysis, while PCLI (HR=1.03, P=0.93) did not provide additional prognostic value. We then examined whether the prognostic value of FISH contributed additionally in patients classified into high and standard risk by conventional cytogenetics, and vice versa. Those classified as high risk by cytogenetics (n=44, 11 %), were further categorized into 2 groups by additional prognostic information obtained by FISH (median survival 18 vs. 52 months; P=0.005). Similarly, those classified as standard risk by cytogenetics (n=342, 89 %), were further categorized into 2 groups (median survival 52 vs. 71 months; P=0.017) by additional FISH-based stratification. Similarly, high-risk patients defined by FISH (n=65, 17 %) were further classified into 2 groups with significant difference in median OS (18 vs. 52 months, P=0.004), based on the presence or absence of high-risk features on cytogenetics. In contrast, no additional prognostic information was gained by cytogenetics in patients who were stratified as standard risk by FISH alone (n=321, 83%). Conclusion Our study validates high risk features defined by FISH and conventional cytogenetics in the mSMART risk stratification model among patients treated after the introduction of novel therapies. Either test identifies a high risk cohort with a hazard ratio of approximately 2 (median survival ∼37 versus ∼65 months). In patients known to have high risk disease by one of the two tests, knowledge of the other appears to add additional prognostic value. Disclosures Fonseca: Amgen, BMS, Celgene, Otsuka, Genzyme and Medtronic: Consultancy. Gertz:celgene, millennium: Honoraria. Dispenzieri:celgene: Research Funding. Kumar:celgene, genzyme, millennium, novartis, bayer: Research Funding; genzyme: Membership on an entity's Board of Directors or advisory committees.