Transcriptomic Correlates of Response to Selinexor in Multiple Myeloma Reveal a Predictive Signature

Abstract Selinexor acts by inhibiting the nuclear export protein XPO1; however, its mRNA expression does not correlate with response, and the biological mechanisms underlying treatment response for different patients remain unclear. There is a critically unmet need for validated genomic biomarkers to help guide treatment recommendations to selinexor based therapy. Here, we characterized the transcriptomic correlates of response to selinexor in data from 189 patients from multiple studies of selinexor-based therapy and identified and validated a 3-gene expression signature predictive of treatment response. We performed RNA sequencing on CD138+ cells from 103 patients who participated in the BOSTON study, a phase III, open-label clinical trial of patients with multiple myeloma (MM) who were treated with selinexor, bortezomib and dexamethasone (XVd) after 1-3 lines of standard therapy versus a bortezomib and dexamethasone (Vd) regimen (Grosicki et al 2020 Lancet; Fig 1A-B). Then, we performed differential expression, followed by pathway analysis, to compare patients with long and short progression-free survival (PFS) in the XVd arm of the BOSTON dataset across various PFS and overall depth-of-response (OR) cutoffs. Here, we identified a total of 24 unique downregulated genes and 33 unique upregulated genes that were associated with longer PFS or better depth of response in the XVd arm (FDR < 0.05). Pathway analyses revealed downregulation of apoptosis and MYC targets in patients with selinexor-associated longer PFS or better depth of response (FDR < 0.05), consistent with the known relationship between depth and duration of response in MM. Using the differentially expressed genes, we employed time-to-event univariate Cox proportional hazard models (CPH) with repeated 4-fold cross validation, log-rank testing, and spearman correlations to identify a novel signature that predicts PFS in the BOSTON dataset. This analysis revealed a GSVA score composed of three genes, WNT10A, DUSP1, and ETV7, that were upregulated in XVd patients with PFS > 120 days. Further, this signature accurately distinguished patients with long term PFS in the XVd arm of the BOSTON study (Fig 1C; log rank P = 0.017; spearman Rho = 0.46, P = 0.0007; CPH, FDR=0.047, HR=0.36 [95% CI = 0.14-0.84]). We also found that the signature significantly tracks with a depth of response of VGPR or better (Fig 1D, Wilcoxon P = 0.025). Finally, we validated the accuracy of our signature using transcriptomic data from two external cohorts: the STORM trial of penta-refractory MM (N = 64; Chari et al., NEJM), and a cohort of patients treated with selinexor-based regimens at Mount Sinai who were not part of a clinical trial (N = 21). This signature validated successfully in the STORM study (Fig 1E, log-rank P = 0.02; spearman Rho = 0.18, P = 0.14; CPH P = 0.08, HR=0.63 [95% CI = 0.47-1.03 ]) and in the non-trial Mount Sinai cohort (Fig 1G, log-rank P = 0.0033; spearman Rho = 0.6, P = 0.0043; CPH P = 0.004, HR = 0.215 [95% = 0.15-0.72]). Additionally, the association of the signature expression with depth-of-response validated in the STORM cohort (Fig 1F; Wilcoxon P = 0.021), further supporting the robustness of our signature. We used the MMRF-COMMPASS dataset (N=700) as a negative control and found that the signature is not predictive of PFS in patients who were treated with non-selinexor based, standard of care therapies. Together, these results support the conclusion that our signature is specific to selinexor treatment response and is not reflective of overall prognosis. We are currently performing experimental validation of the three genes in cell line experiments to better understand the mechanisms underlying their predictive power. We are also evaluating the utility of augmenting gene-expression based biomarkers with an ex-vivo mass-based biomarker assay to more accurately predict response to selinexor. In summary, we report a novel gene expression signature for response to selinexor-based therapy in patients with MM. We have validated our findings in several external transcriptomic datasets of MM patients treated with selinexor-based regimens. This signature has important clinical significance as it could identify patients most likely to benefit from treatment with selinexor-based therapy, especially in earlier lines of therapy. Figure 1 Figure 1. Disclosures Stevens: Travera: Current Employment. Richter: Adaptive Biotechnologies: Speakers Bureau; Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Karyopharm: Consultancy; Antengene: Consultancy; Sanofi: Consultancy; X4 Pharmaceuticals: Consultancy; Oncopeptides: Consultancy; Adaptive Biotechnologies: Consultancy; Celgene: Speakers Bureau; Janssen: Speakers Bureau; Secura Bio: Consultancy; Astra Zeneca: Consultancy. Richard: Karyopharm, Janssen: Honoraria. Chari: Takeda: Consultancy, Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Research Funding; Secura Bio: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Millenium/Takeda: Consultancy, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Shattuck Labs: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Jagannath: Bristol Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Janssen Pharmaceuticals: Consultancy; Sanofi: Consultancy; Legend Biotech: Consultancy; Takeda: Consultancy. Walker: Karyopharm Therapeutics Inc.: Current Employment. Landesman: Karyopharm Therapeutics: Current Employment, Current equity holder in publicly-traded company. Parekh: Foundation Medicine Inc: Consultancy; Amgen: Research Funding; PFIZER: Research Funding; CELGENE: Research Funding; Karyopharm Inv: Research Funding.

Download Full-text

Analysis of Serial Patient Samples Reveals a Variety of Clonal Dynamics In Multiple Myeloma

Blood ◽

10.1182/blood.v116.21.1923.1923 ◽

2010 ◽

Vol 116 (21) ◽

pp. 1923-1923

Author(s):

Jonathan J Keats ◽

Esteban Braggio ◽

Scott Van Wier ◽

Patrick Blackburn ◽

Angela Baker ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Board Of Directors ◽

Research Funding ◽

Cell Leukemia ◽

Advisory Committees ◽

Tumor Mass ◽

Time Points ◽

Tumor Genome ◽

Time Point

Abstract Abstract 1923 Our understanding of the genetic abnormalities associated with the development of multiple myeloma has increased significantly in the last decade. However, very little is known about how, or if, myeloma tumor genomes change with time and if therapeutic interventions influence these events. To address these issues we studied a cohort of 29 patients for whom at least two serial samples (1-65 months, median 19 months) were available for analysis. Each serial pair was analyzed by both array-based comparative genomic hybridization (aCGH) and microarray gene expression profiling (GEP) to identify DNA copy number abnormalities (CNA) at a 25kb resolution and gene expression differences present in the bulk of the tumor mass. Though this does not address the intra-clonal heterogeneity that may exist at a given time point, it does answer if the bulk of the tumor mass is changing with time. This study has unearthed several surprising and clinically relevant findings. First, myeloma tumor genomes are not as unstable as previous cytogenetic analyses suggest. In 40% of patients we observed no detectable CNA changes (1-37 months, median 12 months). In 24% of patients we observed the exclusive acquisition of new CNA (1-12, median 3.5) (3-22 months, median 18 months). In 36% of patients we observed both the loss (1-20, median 3) and gain (1-33, median 21) of CNA (5-43 months, median 20 months). Because time was not a significant influence on the detection of stable or unstable genomes we compared CNA changes with TC class and found patients with the high-risk 4p16 and maf IgH translocations were over-represented in the latter subset of patients. These observations raise the question of what happens between multiple rounds of therapy and if different regimens influence these phenotypes differently. For two patients with no CNA changes between the first two time points there was an additional sample that extended the follow-up by 52 and 12 months. Again no CNA changes were seen between diagnosis and these final samples taken 63 and 50 months later. For one patient with CNA changes (5 shared, 29 lost, and 32 gained) we have a detailed time course of 5 samples from diagnosis through to end-stage plasma cell leukemia. This patient received continuous lenalidomide-dexamethasone (Rd) for 20 months and progessed with a clone containing a BIRC2/3 deletion, which activates the NFKB pathway. The patient received single agent PR-171 and a bortezomib containing regimen and unexpectedly, the tumor genome observed in the third sample was almost identical (32 shared, 2 lost, and 4 gained CNA) to the first time point, including two copies of BIRC2/3. Subsequently, the patient received melphalan-prednisone-bortezomib (MPV) and the tumor genome observed in the fourth and fifth samples, which were identical, were similar to that seen in the second sample (24 shared, 13 lost, and 39 gained CNA). To understand these observations better we performed FISH to ascertain if the observed clones were detectable earlier, albeit at a low frequency. These experiments proved that the two dominant subclones observed at time points 1 and 3 versus 2, 4, 5 were mutually exclusive at the single cell level. Moreover, both of these clones were detectable at diagnosis with 12% of the tumor mass being the second subclone that eventually evolved into plasma cell leukemia. Interestingly, we assayed 5 of the 39 unique CNA observed in the final two samples and only one, the 17p13 deletion, was detectable earlier. This suggests the MPV regimen effectively eliminated a clone that was previously sensitive to Rd and selected for a dramatically evolved subclone that was previously sensitive to two different proteasome inhibitors. Although it is clear that the high-risk patients are enriched in the subset with the most changes, it is not clear if the specific drugs used (Melphalan vs IMID vs proteasome inhibitor) or intervention strategy (Cycled vs continuous/maintenance) and perhaps the response achieved (PR vs CR) influences these events. These observations do highlight two important clinical concepts that need to be considered in the future. First, the meaning of a partial response needs further investigation as this may reflect effective elimination of one subclone but not another. Second, because some patients are not changing or can revert back to a previous subclone we need to consider re-chanllenging patients with previously effective regimens when patients progress. Disclosures: Fonseca: Genzyme: Consultancy; Medtronic: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Otsuka: Consultancy; Celgene: Consultancy, Research Funding; Intellikine: Consultancy; Cylene: Research Funding; Onyx: Research Funding; FISH probes prognostication in myeloma: Patents & Royalties. Stewart:Millennium: Consultancy; Celgene: Honoraria. Bergsagel:Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Millennium: Speakers Bureau; Novartis: Speakers Bureau.

Download Full-text

Dysregulation of Splicing in Multiple Myeloma: The Splicing Factor SRSF1 Supports MM Cell Proliferation Via Splicing Control

Blood ◽

10.1182/blood-2018-99-118845 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4500-4500

Author(s):

Mariateresa Fulciniti ◽

Michael A Lopez ◽

Anil Aktas Samur ◽

Eugenio Morelli ◽

Hervé Avet-Loiseau ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Alternative Splicing ◽

Board Of Directors ◽

Research Funding ◽

Splicing Factor ◽

Splicing Factors ◽

Rna Seq ◽

Advisory Committees ◽

Disease Biology

Abstract Gene expression profile has provided interesting insights into the disease biology, helped develop new risk stratification, and identify novel druggable targets in multiple myeloma (MM). However, there is significant impact of alternative pre-mRNA splicing (AS) as one of the key transcriptome modifier. These spliced variants increases the transcriptomic complexity and its misregulation affect disease behavior impacting therapeutic consideration in various disease processes including cancer. Our large well annotated deep RNA sequencing data from purified MM cells data from 420 newly-diagnosed patients treated homogeneously have identified 1534 genes with one or more splicing events observed in at least 10% or more patients. Median alternative splicing event per patient was 595 (range 223 - 2735). These observed global alternative splicing events in MM involves aberrant splicing of critical growth and survival genes affects the disease biology as well as overall survival. Moreover, the decrease of cell viability observed in a large panel of MM cell lines after inhibition of splicing at the pre-mRNA complex and stalling at the A complex confirmed that MM cells are exquisitely sensitive to pharmacological inhibition of splicing. Based on these data, we further focused on understanding the molecular mechanisms driving aberrant alternative splicing in MM. An increasing body of evidence indicates that altered expression of regulatory splicing factors (SF) can have oncogenic properties by impacting AS of cancer-associated genes. We used our large RNA-seq dataset to create a genome wide global alterations map of SF and identified several splicing factors significantly dysregulated in MM compared to normal plasma cells with impact on clinical outcome. The splicing factor Serine and Arginine Rich Splicing Factor 1 (SRSF1), regulating initiation of spliceosome assembly, was selected for further evaluation, as its impact on clinical outcome was confirmed in two additional independent myeloma datasets. In gain-of (GOF) studies enforced expression of SRSF1 in MM cells significantly increased proliferation, especially in the presence of bone marrow stromal cells; and conversely, in loss-of function (LOF) studies, downregulation of SRSF1, using stable or doxy-inducible shRNA systems significantly inhibited MM cell proliferation and survival over time. We utilized SRSF1 mutants to dissect the mechanisms involved in the SRSF1-mediated MM growth induction, and observed that the growth promoting effect of SRSF1 in MM cells was mainly due to its splicing activity. We next investigated the impact of SRSF1 on allelic isoforms of specific gene targets by RNA-seq in LOF and confirmed in GOF studies. Splicing profiles showed widespread changes in AS induced by SRSF1 knock down. The most recurrent splicing events were skipped exon (SE) and alternative first (AF) exon splicing as compared to control cells. SE splice events were primarily upregulated and AF splice events were evenly upregulated and downregulated. Genes in which splicing events in these categories occurred mostly did not show significant difference in overall gene expression level when compared to control, following SRSF1 depletion. When analyzing cellular functions of SRSF1-regulated splicing events, we found that SRSF1 knock down affects genes in the RNA processing pathway as well as genes involved in cancer-related functions such as mTOR and MYC-related pathways. Splicing analysis was corroborated with immunoprecipitation (IP) followed by mass spectrometry (MS) analysis of T7-tagged SRSF1 MM cells. We have observed increased levels of SRSF phosphorylation, which regulates it's subcellular localization and activity, in MM cell lines and primary patient MM cells compared to normal donor PBMCs. Moreover, we evaluated the chemical compound TG003, an inhibitor of Cdc2-like kinase (CLK) 1 and 4 that regulate splicing by fine-tuning the phosphorylation of SR proteins. Treatment with TG003 decreased SRSF1 phosphorylation preventing the spliceosome assembly and inducing a dose dependent inhibition of MM cell viability. In conclusions, here we provide mechanistic insights into myeloma-related splicing dysregulation and establish SRSF1 as a tumor promoting gene with therapeutic potential. Disclosures Avet-Loiseau: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Munshi:OncoPep: Other: Board of director.

Download Full-text

High Levels of APOBEC3B Gene Expression Contribute to Poor Prognosis in Multiple Myeloma Patients

Blood ◽

10.1182/blood-2018-99-117067 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3897-3897

Author(s):

Valeriy V Lyzogubov ◽

Pingping Qu ◽

Cody Ashby ◽

Adam Rosenthal ◽

Antje Hoering ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Board Of Directors ◽

Research Funding ◽

Poor Prognosis ◽

Target Genes ◽

Plasma Cells ◽

Nuclear Fraction ◽

Advisory Committees ◽

Significant Difference

Abstract Introduction: Poor prognosis and drug resistance in multiple myeloma (MM) is associated with increased mutational load. APOBEC3B is a major contributor to mutagenesis, especially in myeloma patients with t(14;16) MAF subgroup. It was shown recently that presence of the APOBEC signature at diagnosis is an independent prognostic factor for progression free survival (PFS) and overall survival (OS). We hypothesized that high levels of APOBEC3B gene expression at diagnosis may also have a prognostic impact in myeloma. To consider APOBEC3B as a potential target for therapy more studies are necessary to understand how APOBEC3B expression is regulated and how APOBEC3B generates mutations. Methods: Gene expression profiling (GEP, U133 Plus 2.0) of MM patients was performed. APOBEC3B gene expression levels were investigated in plasma cells of healthy donors (HD; n=34), MGUS (n=154), smoldering myeloma (SMM; n=219), MM low risk (LR; n=739), MM high risk (HR; n=129), relapsed MM (RMM; n=74), and primary plasma cell leukemia (pPCL; n=19) samples. The samples from relapse were taken on or after the progression/relapse date but within 30 days after progression/relapse from Total Therapy trials 3, 4, 5 & 6. GEP70 score was used to separate samples into LR and HR groups. We also investigated APOBEC3B expression in different MM molecular subgroups and used logrank statistics with covariate frequency distribution to determine an optimal cut off APOBEC3B expression value. Gene expression was compared in cases with low expression of APOBEC3B (log2<7.5) and high expression of APOBEC3B (log2>10), and an optimal cut-point in APOBEC3B expression was identified with respect to PFS. To explore the role of MAF and the non-canonical NF-ĸB pathway we performed functional studies using a cellular model of MAF downregulation. TRIPZ lentiviral shRNA MAF knockdown in the RPMI8226 cell lines was used to explore MAF-dependent genes. NF-ĸB proteins, p52 and RelB, were investigated in the nuclear fraction by immunoblot analysis. Results: Expression of APOBEC3B in HD control samples (log2=10.9) was surprisingly higher than in MGUS (log2=9.51), SMM (log2=9.09), and LR (log2=9.40) and was comparable to HR (log2=10.4) and RMM (log2=10.6) groups. Expression levels of APOBEC3B were gradually increased as disease progressed from SMM to pPCL. The high expression of APOBEC3B in HD places plasma cells at risk of APOBEC induced mutagenesis where the regulation of APOBEC3B function is compromised. The correlation between APOBEC3B expression and GEP70 score in MM was 0.37, and there was a significant difference in APOBEC3B expression between GEP70 high and low risk groups (p=0.0003). An optimal cut-point in APOBEC3B expression of log2=10.2 resulted in a significant difference in PFS (median 5.7 yr vs.7.4 yr; p=0.0086) and OS (median 9.1 yr vs. not reached; p<0.0001), between high and low expression. The highest APOBEC3B expression was detected in cases with a t(14;16). We analyzed t(14;16) cases with the APOBEC mutational signature and compared them to t(14;16) cases without the APOBEC signature and found elevated MAF (2-fold) and APOBEC3B (2.7-fold) gene expression in samples with the APOBEC signature. No APOBEC signature was detected in SMM cases, including those with a t(14;16). High APOBEC3B levels in myeloma patients was associated with overexpression of genes related to response to DNA damage and cell cycle control. Significant (p<0.05) increases of NF-κB target genes was seen in high APOBEC3B cases: TNFAIP3 (4.4-fold), NFKB2 (1.7-fold), NFKBIE (1.9-fold), RELB (1.4-fold), NFKBIA (2.0-fold), PLEK (2.5-fold), MALT1 (2.5-fold), WNT10A (2.4-fold). However, in t(14;16) cases there was no significant increase of NF-κB target genes except BIRC3 (2.5-fold) and MALT1 (2.0-fold). MAF downregulation in RPMI8226 cells did not lead to changes in NF-κB target gene expression but MAF-dependent genes were identified, including ETS1, SPP1, RUNX2, HGF, IGFBP2 and IGFBP3. Analysis of nuclear fraction of NF-ĸB proteins did not show significant changes in expression of p52 and RelB in RPMI8226 cells after MAF downregulation. Conclusions: Increased expression of APOBEC3B is a negative prognostic factor in multiple myeloma. MAF is a major factor regulating expression of APOBEC3B in the t(14;16) subgroup. NF-ĸB pathway activation is most likely involved in upregulation of APOBEC3B in non-t(14;16) subgroups. Disclosures Davies: TRM Oncology: Honoraria; MMRF: Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding; Takeda: Consultancy, Honoraria.

Download Full-text

Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽

10.1182/blood-2018-99-113169 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1882-1882 ◽

Cited By ~ 1

Author(s):

Samuel A Danziger ◽

Mark McConnell ◽

Jake Gockley ◽

Mary Young ◽

Adam Rosenthal ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Bone Marrow ◽

Board Of Directors ◽

Research Funding ◽

Expression Profiles ◽

Cell Types ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Download Full-text

MLN4924, a Novel Investigational NEDD8 Activating Enzyme Inhibitor, Exhibits Preclinical Activity In Multiple Myeloma and Waldenström's Macroglobulinemia through Mechanism Distinct From Existing Proteasome Inhibitors

Blood ◽

10.1182/blood.v116.21.2988.2988 ◽

2010 ◽

Vol 116 (21) ◽

pp. 2988-2988

Author(s):

Douglas W. McMillin ◽

Zachary Hunter ◽

Jake Delmore ◽

Val Monrose ◽

Peter G Smith ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Board Of Directors ◽

Cell Lines ◽

Research Funding ◽

Normal Donor ◽

Advisory Committees ◽

Safety Studies ◽

Ubiquitin Proteasome ◽

Efficacy Studies

Abstract Abstract 2988 Background: Multiple myeloma (MM) and Waldenström Macroglobulinemia (WM) have both shown clinical responses to Bortezomib therapy which blocks the elimination of ubiquitin tagged regulatory proteins by the proteasome. The NEDD8 activating enzyme (NAE)-inhibitor MLN4924 is a novel agent which demonstrates selective inhibition of the proteins for degradation in the ubiquitin pathway and may offer benefits to MM and WM patients through the more targeted approach. Methods: A panel of human MM and WM cell lines were tested for their in vitro response to MLN4924 using MTT colorimetric survival assays. MM and WM cell lines tested exhibited dose and time dependent decrease of their viability upon exposure to MLN4924 (IC50=25-150 nM). In addition, miRNA and gene expression studies in response to MLN4924 were compared to treatment of the same cells with bortezomib. In vivo safety studies were performed in mice and animal efficacy studies are ongoing in both MM and WM engrafted mice. Results: A panel of MM and WM cells were treated with MLN4924 for 72hrs and compared to the colon carcinoma line HCT116 and normal cell lines HS-5 (stroma) and THLE-3 (hepatocytes). In addition, a longitudinal assessment of viability of MM1S (MM) and BCWM1 (WM) cells during a 72hr incubation with MLN4924 (500nM) showed commitment to death <48hrs. This result, coupled with the observation that normal donor peripheral blood mononuclear cells (PBMCs) and HS-5 stromal cells were less sensitive (IC50 >1000 nM) than the MM or WM cell lines tested, suggest that this compound exhibits a rapid, tumor-selective effect at clinically relevant conditions. We also evaluated primary MM (CD138+) and WM (CD19+) patient bone marrow cells and observed sub-μ M activity by MLN4924. In addition, we tested a series of combinations of MLN4924 with dexamethasone, doxorubicin and bortezomib in both MM1S and BCWM1 cells lines and observed additive activity or greater with MLN4924. Gene expression profiling revealed distinct signatures, in MM1S and BCWM1 lines, as well as distinct patterns of gene expression changes which were induced by MLN4924 vs. bortezomib. For instance, while bortezomib potently induces a compensatory upregulation of transcripts for ubiquitin/proteasome and heat shock protein genes which, in MM1S or BCWM1 cells, were not observed in response to MLN4924 treatment. Additional studies with the proteasome inhibitor MLN9708 revealed similar patterns of expression as bortezomib. These results indicate that MLN4924 does not induce pronounced proteotoxic stress in MM or WM cells, highlighting the distinct effect of MLN4924 on the ubiquitin/proteasome pathway compared to inhibitors which target the 20S proteasome subunit. Longitudinal miRNA profiling revealed a distinct pattern of miRNA expression in MLN4924-treated vs. bortezomib-treated MM and WM cells. Lastly, animal safety studies showed that MLN4924 was tolerated at doses up to 60mg/kg 2x daily for 1 week. Efficacy studies in MM and WM are ongoing. Conclusions: MLN4924 induces cell killing at sub-μ M concentrations for both MM and WM cells with higher sensitivity of tumor cells compared to normal tissues, exhibits selective gene expression and miRNA regulation and can be safely administered to mice. These studies provide the framework for the clinical investigation of MLN4924 in MM and WM. Disclosures: McMillin: Axios Biosciences: Equity Ownership. Smith:Millennium: Employment. Birner:Millennium: Employment. Richardson:Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees. Anderson:Millennium Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Treon:Millennium Pharmaceuticals, Genentech BiOncology, Biogen IDEC, Celgene, Novartis, Cephalon: Consultancy, Honoraria, Research Funding; Celgene Corporation: Research Funding; Novartis Corporation: Research Funding; Genentech: Consultancy, Research Funding. Mitsiades:Millennium: Consultancy, Honoraria; Novartis Pharmaceuticals: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Merck &Co.: Consultancy, Honoraria; Kosan Pharmaceuticals: Consultancy, Honoraria; Pharmion: Consultancy, Honoraria; Centrocor: Consultancy, Honoraria; PharmaMar: Patents & Royalties; OSI Pharmaceuticals: Research Funding; Amgen Pharmaceuticals: Research Funding; AVEO Pharma: Research Funding; EMD Serono: Research Funding; Sunesis: Research Funding; Gloucester Pharmaceuticals: Research Funding.

Download Full-text

Response Adaptive Salvage Treatment with Carfilzomib-Thalidomide-Dexamethasone for Newly Diagnosed Transplant Eligible Multiple Myeloma Patients Failing Front-Line Bortezomib-Based Induction Therapy - Final Analysis from the Australasian Leukemia and Lymphoma Group (ALLG) MM17 Trial

Blood ◽

10.1182/blood-2021-152034 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1663-1663

Author(s):

Rose Turner ◽

Hang Quach ◽

Noemi Horvath ◽

Ian H Kerridge ◽

Flora Yuen ◽

...

Keyword(s):

Multiple Myeloma ◽

Board Of Directors ◽

Induction Therapy ◽

Research Funding ◽

Progressive Disease ◽

Progression Free Survival ◽

Publicly Traded ◽

Advisory Committees ◽

Free Survival ◽

Depth Of Response

Abstract BACKGROUND Survival rates in multiple myeloma (MM) have significantly improved in recent decades with the advent of high-dose chemotherapy conditioned autologous stem cell transplantation (ASCT) and the availability of novel agents for induction therapy (Kumar SK et al. Blood 2008). Failure to respond to front-line bortezomib-based induction therapy remains a significant clinical challenge in transplant eligible (TE) newly diagnosed multiple myeloma (NDMM), and is associated with poor outcomes with shortened progression free survival (PFS) and overall survival (OS) (Lee SE et al. Ann Hematol. 2014). In combination with immunomodulatory agents (IMiDs), carfilzomib, a second generation proteosome inhibitor, has been shown to be highly effective in the context of MM induction with high rates of negativity for minimal residual disease (MRD) and few dose limiting toxicities (Langren O et al. Leukemia 2019). The ALLG MM17 trial is a multicentre single arm study of carfilzomib-thalidomide-dexamethasone (KTd) in TE NDMM patients refractory or with suboptimal response to bortezomib-based induction therapy, designed to evaluate the efficacy of early response adaption with a switch to an intensive salvage strategy. METHOD Eligible patients included those with TE NDMM, aged 18 years and older, demonstrating sub-optimal response to bortezomib-based induction therapy (failure to achieve a minimal response after 2 cycles, partial response [PR] after 4 cycles, or disease progression within 60 days of completing induction). Salvage therapy consisted of 100mg daily oral thalidomide, with 20 mg of oral dexamethasone and 20mg/56mg of IV carfilzomib on days 1, 2, 8, 9, 15, and 16, with of each 28-day cycle. Following 4 cycles, patients in stringent complete response (sCR) proceeded to melphalan conditioned ASCT whereas those in less than sCR received a further 2 cycles of KTd prior to ASCT. Consolidation therapy consisted of a further 2 cycles of KTd, followed by maintenance 100mg daily thalidomide and 40mg weekly dexamethasone (Td) continuing until progressive disease, unacceptable toxicity, or 12 months of therapy. Primary objectives were to determine the overall response rate (ORR) and safety profile of treatment with KTd salvage therapy, with secondary objectives to determine the maximal depth of response, progression free survival (PFS), and overall survival (OS) achieved with sequential treatment with KTd salvage, ASCT, post-ASCT consolidation, and maintenance Td therapy. Efficacy assessments were performed via serum protein electrophoresis, serum free light chain and bone marrow evaluation. Next generation flow (NGF) cytometry MRD evaluation of bone marrow aspirate was undertaken pre-ASCT, at day 100 post-ASCT, after 2 cycles of consolidation KTd, and following completion of Td using standardized 8-colour EuroFlow platform. RESULTS 50 patients were recruited across 6 Australian sites between September 2016 and April 2018. Overall response rate to KTd salvage was 78% (Credible Interval 95%: 64.4-87.1%), with dual proof of concept criteria met (observed ORR ≥ 50% and posterior probability that the true ORR exceeds 30% is ≥ 0.90). Response rates included 12% sCR, 6% CR, 38% VGPR, and 22% PR. Sixteen patients discontinued treatment (32%) including 10 cases (20%) of progressive disease, and 2 patient deaths without progression. NGF MRD negativity was found to be 32%, 36% and 55% at the pre-ASCT, post-ASCT and post-consolidation time-points. At the cut-off date, estimated median follow-up for disease status was 38.6 months and median PFS and OS had not been reached. At 36 months PFS and OS were 63.9% (95%CI: 49.0 - 75.5%) and 79.9% (95%CI: 65.8 - 88.6%) respectively (Figure 1). KTd was found to be well tolerated with 44% of patients experiencing a grade 3 of higher adverse event (AE). Most common AEs included upper respiratory infection (48%), peripheral neuropathy (36%), musculoskeletal pain (32%), dyspnoea (28%), fatigue or lethargy (28%), and constipation (28%). Significant cardiac toxicity was not observed at this higher dose level of carfilzomib. CONCLUSION Results demonstrate that response-adaptive utilisation of KTd salvage, ASCT, and consolidation therapy induces high response rates, improving depth of response with high levels of sequential MRD negativity, and durable responses with an acceptable toxicity profile in TE NDMM patients failing bortezomib-based induction therapy. Figure 1 Figure 1. Disclosures Quach: Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; CSL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen/Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Kalff: Amgen: Honoraria; Bristol Myers Squibb: Honoraria; Celgene: Honoraria; Pfizer: Honoraria; Janssen: Honoraria; Roche: Honoraria; CSL: Honoraria; Sandoz: Honoraria. Bergin: Amgen: Other: Travel to workshop; Celgene: Consultancy. Reynolds: Novartis AG: Current equity holder in publicly-traded company; Alcon: Current equity holder in publicly-traded company; Abbvie: Research Funding. Spencer: Celgene: Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria, Research Funding, Speakers Bureau; Amgen: Honoraria, Research Funding; Bristol Myers Squibb: Research Funding; Takeda: Honoraria, Research Funding, Speakers Bureau; STA: Honoraria.

Download Full-text

Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Blood ◽

10.1182/blood-2019-127362 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4370-4370

Author(s):

Michael J Mason ◽

Carolina D. Schinke ◽

Christine Eng ◽

Fadi Towfic ◽

Fred Gruber ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

Download Full-text

Clinical Validation of Treatment Response Predictions Using a Genomics Driven Computational Biology Modelling Multiple Myeloma Algorithm

Blood ◽

10.1182/blood-2018-99-118686 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1893-1893

Author(s):

Ravi Vij ◽

Justin King ◽

Mark A. Fiala ◽

Neeraj Kumar Singh ◽

Mohammed Sauban ◽

...

Keyword(s):

Multiple Myeloma ◽

Computational Biology ◽

Clinical Response ◽

Treatment Response ◽

Board Of Directors ◽

Research Funding ◽

Patient Specific ◽

Similar Response ◽

Advisory Committees ◽

Combination Regimens

Abstract Background: Multiple myeloma (MM) is an incurable and heterogeneous haematological malignancy in which immune suppression and complex biology affect the disease and its response to treatment. Several new treatments have been approved for MM in recent years providing numerous options for patients with relapsed/refractory disease. However, there is no validated method for selecting the best treatment combination for each patient, making patient management difficult. The ability to predict treatment response based on disease characteristics could improve clinically outcomes. Aim: This was a validation of a genomics-informed response prediction using computational biology modelling (CBM) in patients with relapsed/refractory MM. Methods: Input data from fluorescence in-situ hybridization (FISH), karyotype, and a MM specific next generation sequencing capture array were analysed using CBM. This was a retrospective review of patients which were treated with different combinations based on patient/physician choice. The CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated pathways. The specific drug combination for each patient was simulated and the quantitative drug effect was measured on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The predicted outcomes were then compared to the clinical response (≥PR or < PR per IMWG) to assess the accuracy of this CBM predictive approach. Results: 27 patients were selected for the study; 3 failed CBM due to missing inputs and in 3 clinical response was not able to be assessed, leaving 21 eligible for the analysis. The median age at presentation was 57 years (range 37-76) and 52% were male. The median prior lines of MM therapy was 5 (range 1-15). 38% were refractory to bortezomib, 62% to lenalidomide, 52% to carfilzomib, 57% to pomalidomide, and 43% to daratumumab. 81% had a prior autologous stem cell transplant. The treatments modelled included IMiD-based regimens (n = 9), PI-based regimens (n = 6), chemo-based regimens (n = 3), selinexor (n = 2), PI/IMiD combination regimens (n = 1). Sixteen were clinical responders and 5 were non-responders. CBM predictions matched for 17 of 21 treatments overall, 15 of 16 clinical responders and 2 of 5 non-responders. The statistics of prediction accuracy against clinical outcome are presented in Table 1. Interestingly, the CBM identified drugs within the combination regimens which may not have impacted efficacy. For example, the CBM predicted that one patient treated with bortezomib, venetoclax, and dexamethasone would have had similar response if venetoclax had been omitted from the regimen. Conclusion: We have demonstrated that a CBM approach, which incorporates genomics, can help predict response in patients with relapsed or refractory MM. Prospective studies using the CBM as part of treatment decision-making will help determine its application into clinical settings. Disclosures Vij: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees. Singh:Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Husain:Cellworks Research India Private Limited: Employment. Lakshminarayana:Cellworks Research India Private Limited: Employment. Talawdekar:Cellworks Research India Private Limited: Employment. Mitra:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment.

Download Full-text

Changes in Uninvolved Immunoglobulins during Multiple Myeloma Therapy

Blood ◽

10.1182/blood.v128.22.3251.3251 ◽

2016 ◽

Vol 128 (22) ◽

pp. 3251-3251

Author(s):

Praful Ravi ◽

Shaji Kumar ◽

Wilson I Gonsalves ◽

Francis K Buadi ◽

Martha Q. Lacy ◽

...

Keyword(s):

Multiple Myeloma ◽

Overall Survival ◽

Treatment Response ◽

Board Of Directors ◽

Light Chain ◽

Research Funding ◽

Advisory Committees ◽

Percentage Difference ◽

Lower Likelihood

Abstract Background Suppression of uninvolved immunoglobulins is a common finding in multiple myeloma and the preservation of uninvolved immunoglobulins at diagnosis is associated with improved progression-free and overall survival. However, little is known about the impact of myeloma treatment on levels of uninvolved immunoglobulins, and the link between changes in uninvolved immunoglobulins during therapy and treatment response, disease progression and survival. Methods We identified patients who received therapy for newly diagnosed multiple myeloma at our institution between 2001 and 2014, and who had data available on absolute lymphocyte count (ALC) and quantitative uninvolved immunoglobulins (Ig) before commencing treatment. The ALC and levels of uninvolved Ig after 4 cycles of therapy were abstracted from the electronic medical record; patients who switched or stopped treatment, or died, before this time point were excluded. To assess change in ALC, the percentage difference in ALC between baseline and 4 cycles was calculated; for uninvolved Ig, the average of the percentage difference between baseline and 4 cycles for each uninvolved Ig (IgA and IgM for IgG myeloma, IgG and IgM for IgA myeloma, IgG and IgA for IgM and IgD myeloma, and IgG, IgM and IgA for light-chain only myeloma) was calculated. Treatment response at 4 cycles was retrospectively assigned according to International Myeloma Working Group criteria. Time to treatment failure (TTF) was defined as time from start of initial therapy to start of next line of therapy or death (if no additional treatment was received). A landmark analysis was used to calculate overall survival (OS) from the date of follow-up after 4 cycles of therapy. The Kruskal-Wallis, Chi-Square, and log rank tests were used to detect differences in medians, proportions, and survival times respectively. Results A total of 421 patients were included in this analysis. The median age was 63 years (range 33-91), 254 patients (60.3%) were male and median follow-up was 6.5 years (95% CI 5.6-7.3); the vast majority of patients had IgG (n=247 [58.7%]), IgA (n=98 [23.3%]) or light-chain only myeloma (n=68 [16.2%]). First line therapy comprised of pulse-dose dexamethasone (DEX, n=92 [21.9%]), lenalidomide-dexamethasone (RD, n=176 [41.8%]), bortezomib-dexamethasone (VD, n=22 [5.2%]), bortezomib-cyclophosphamide-dexamethasone (VCD, n=84 [20.0%]), and bortezomib-lenalidomide-dexamethasone (VRD, n=47 [11.2%]). Across the entire cohort, the median change in ALC and uninvolved Ig after 4 cycles of treatment was -11.0% (range: -92.7 to +718.8) and +9.0% (-77.7 to +1094.4) respectively; treatment with VCD was associated with the greatest median declines in ALC (DEX: -0.1%; RD: -9.9%; VD: -20.8%; VCD: -40.9%; VRD: -15.3%) and uninvolved Ig (DEX: -0.5%; RD: +15.5%; VD: +44.0%; VCD: -14.0%; VRD: +76.0%, both p<0.001). Conversion from suppression to normalization of the primary uninvolved Ig (IgA in IgG myeloma, and IgG in all other myeloma types) after 4 cycles was seen more frequently with the use of RD (13.1%) and VRD (12.8%) compared to DEX (4.7%), VCD (1.3%), or VD (4.8%), χ2=21.8, p=0.040. When considering only patients in whom the primary uninvolved Ig remained suppressed between baseline and 4 cycles, a ≥25% reduction in the primary uninvolved Ig occurred more frequently with the use of DEX (51.5%) and VCD (34.5%) compared to RD (24.8%), VD (23.5%) or VRD (25.7%), χ2=15.1, p=0.005 (Table 1). Although an average reduction in uninvolved Ig between baseline and 4 cycles (ΔIg<0) was independently associated with a lower likelihood of achieving very good partial response (VGPR) of better on multivariate analysis adjusting for age, sex and treatment regimen (OR=0.40 [0.24-0.63], p<0.001), there were no differences in TTF (2.0yrs vs. 2.0yrs, p=0.783) or OS (8.0yrs vs. 8.0yrs, p=0.721) between patients with ΔIg<0 (n=169) and those with ΔIg≥0 (n=222). Conclusions Myeloma treatments produce differential impacts on immune parameters, with VCD causing the greatest reduction in lymphocytes and uninvolved Ig, implying general targeting of plasma cells, in comparison to lenalidomide, which appeared to be more tumor-specific with relative sparing of lymphocytes and uninvolved Ig. While an average decrease in uninvolved Ig was an independent predictor of a lower likelihood of achieving VGPR or better after 4 cycles of therapy, it was not associated with a shorter TTF or poorer OS. Disclosures Kumar: BMS: Consultancy; Glycomimetics: Consultancy; Onyx: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Kesios: Consultancy; Millennium: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; AbbVie: Research Funding. Dispenzieri:GSK: Membership on an entity's Board of Directors or advisory committees; Prothena: Membership on an entity's Board of Directors or advisory committees; pfizer: Research Funding; Jannsen: Research Funding; Alnylam: Research Funding; Celgene: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Amgen: Research Funding; Celgene: Research Funding; Takeda: Research Funding. Bergsagel:Amgen, BMS, Novartis, Incyte: Consultancy; Novartis: Research Funding.

Download Full-text

High Risk Multiple Myeloma Cases Are Identified In An MMRC Led Study By The SKY92 Gene Signature (MMprofiler)

Blood ◽

10.1182/blood.v122.21.1854.1854 ◽

2013 ◽

Vol 122 (21) ◽

pp. 1854-1854

Author(s):

Erik H van Beers ◽

Martin H. Van Vliet ◽

Kenneth C. Anderson ◽

Ajai Chari ◽

Sundar Jagannath ◽

...

Keyword(s):

Clinical Trial ◽

Multiple Myeloma ◽

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Rna Extraction ◽

Hazard Ratio ◽

Advisory Committees ◽

Equity Ownership ◽

Standard Risk

Abstract Introduction Multiple Myeloma is not a single disease. There is increasing support for risk classification in combination with treatment decision making because of its impact on clinical outcomes. Here we demonstrate additional evidence of the prognostic value of SKY92, an established genetic marker of high risk Multiple Myeloma in a multicenter collection of samples with undisclosed treatments. Materials Methods A public GEP dataset (MMRC, MMGI portal) contained 114 cases of untreated Multiple Myeloma and was used for SKY92 high risk OS prediction (Kuiper et al. Leukemia 2012). In collaboration with MMRC, OS (with a minimum of at least 2 year follow-up) was collected for 91 of 114 cases for the purpose of this analysis. Briefly, CD138-positive plasma cells had been purified prior to total RNA extraction and subsequent gene expression profiling on Affymetrix U133Plus2.0 GeneChips. The 91 cases represented 9 different clinical sites and their CEL files were normalized as a single batch against a reference cohort of 329 cases after which the SKY92 risk scores were determined as either standard risk or high risk. Results SKY92 resulted in 19 high risk (20.9%) versus 72 standard risk (79.1%) cases in the unselected 91 case-cohort. Comparisons with other high risk GEP signatures will be performed. The OS analysis (Figure 1) shows that the HR cases have significantly shorter survival (Hazard Ratio 11, p = 7 x 10-5). Table 1 shows that high risk patients had more elevated B2M (26.3% vs 13.9%), more low albumin (26.3% vs 16.7%) and more high creatinine (26.3% vs 11.0%). There was no difference between high and standard groups in diagnosis dates (not shown). Cause of the 16 (84.2%) deaths among the high risk cases, and 21 (29.1%) deaths among the standard risk cases indicates that high risk contains less disease progression deaths (57.1% vs 31.3%), and more unknown deaths (56.3% vs 23.8%). Conclusions The SKY92 classifier identified 19 of 91 cases (21%) as high risk, recapitulating the percentage of high risk in previously studied cohorts (Kuiper et al. 2012). Moreover the hazard ratio of 11 when events up to 24 months or 8.18 when all events are considered, emphasizes the unmet medical need of high risk cases identified with SKY92 as 69% of all deaths within 2 years (9/13 death events) were in this category. Acknowledgments This research was performed within the framework of CTMM, the Center for Translational Molecular Medicine, project BioCHIP grant 03O-102. Rafael Fonseca is a Clinical Investigator of the Damon Runyon Cancer Research Fund. This work is supported by grants R01 CA83724, ECOG CA 21115T, Predolin Foundation, Mayo Clinic Cancer Center and the Mayo Foundation. Disclosures: van Beers: Skyline Diagnostics: Employment. Van Vliet:Skyline Diagnostics: Employment. Anderson:celgene: Consultancy; onyx: Consultancy; gilead: Consultancy; sanofi aventis: Consultancy; oncopep: Equity Ownership; acetylon: Equity Ownership. Jagannath:Celgene: Honoraria; Millennium: Honoraria. Jakubowiak:BMS: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau. Kumar:Celgene: Clinical Trial Support Other, Membership on an entity’s Board of Directors or advisory committees; Cephalon: Clinical Trial Support, Clinical Trial Support Other; Millennium: Clinical Trial Support, Clinical Trial Support Other, Membership on an entity’s Board of Directors or advisory committees; Novartis: Clinical Trial Support, Clinical Trial Support Other; Onyx: Clinical Trial Support Other, Membership on an entity’s Board of Directors or advisory committees. Lebovic:Celgene: Speakers Bureau; Onyx: Speakers Bureau. Lonial:Millennium: Consultancy; Celgene: Consultancy; Novartis: Consultancy; BMS: Consultancy; Sanofi: Consultancy; Onyx: Consultancy. Reece:Onyx: Honoraria; Novartis: Honoraria; Millennium: Research Funding; Merck: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Research Funding. Siegel:Celgene: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Millennium: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Onyx: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau. Vij:Celgene: Honoraria, Research Funding, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Onyx: Honoraria, Research Funding, Speakers Bureau. Zimmerman:Celgene: Honoraria; Millennium: Honoraria; Onyx: Honoraria. Fonseca:Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties.

Download Full-text