Generation of An Automated Tool for the Identification of Genetics Markers and Signatures in Multiple Myeloma Risk-Stratification

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2881-2881
Author(s):  
Esteban Braggio ◽  
Jonathan J Keats ◽  
Shaji Kumar ◽  
Gregory Ahmann ◽  
Jeremy Mantei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Risk Stratification ◽  
Research Funding ◽  
Plasma Cells ◽  
Risk Group ◽  
High Risk Group ◽  
Proliferation Index ◽  
Gene Index

Abstract Abstract 2881 Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. Significant efforts have been made to identify genetic changes and signatures that can predict clinical outcome and include them in the routine clinical care. Gene expression profiling (GEP) studies have achieved a central role in the study of multiple myeloma (MM), as they become a critical component in the risk-based stratification of the disease. To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells (obtained from the immunobead selection of CD138+ cells) from 489 MM samples in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, UAMS class, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of 57 samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. A macrophage index was calculated and used as a surrogate measurement of non-plasma cell contamination. A total of 49 samples (10%) were excluded from subsequent analysis as the macrophage index indicated a significant contamination with no plasma cells, hence potentially compromising the results. The percent of high-risk disease patients identified from different signatures ranged from 26.4% by using high proliferation index to 28.8% with high centrosome signature and 31.3% with high 70-gene index. This percent of high-risk cases based on the 70-gene index is similar to what was found in Total therapy 2 (TT2) and TT3 cohorts. A third of patients (33.2%) were classified as D1 in the TC class, followed by 11q13 (19.3%), D2 (16.4%), 4p16 (13.8%), MAF (6.1%), None (4.7%), D1+D2 (4.5%) and 6p21 (1.8%). The NF-kB pathway was likely activated in 45.5% to 59.5% of cases, depending on the index used for its calculation. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02) and D2 (p=0.05) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations with unknown partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making. Disclosures: Stewart: Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

MyPRSR Molecular Subtypes of Multiple Myeloma Represent All High-Risk FISH Translocations Included in the mSMART 2.0 and R-ISS Guidelines

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3264-3264 ◽  
Author(s):  
Ryan K Van Laar ◽  
Ivan Borrelo ◽  
David Jabalayan ◽  
Ruben Niesvizky ◽  
Aga Zielinski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Research Funding ◽  
Plasma Cells ◽  
Molecular Subtypes ◽  
Risk Status ◽  
Significant Difference

Abstract Background: There is a global consensus that multiple myeloma patients with high-risk disease require additional monitoring and therapy compared to low/standard risk patients in order to maximize their chances of survival. Current diagnostic guidelines recommend FISH-based assessment of chromosomal aberrations to determine risk status (i.e. t(14;20), t(14;16), t(4;14) and/or Del17p), however, studies show FISH for MM may have a 20-30% QNS rate and is up to 15% discordant between laboratories, even when starting from isolated plasma cells. In this study we demonstrate that MyPRS gene expression profiling reproduces the key high risk translocations for MM risk stratification, in addition to having other significant advantages. Methods: Reproducibility studies show that MyPRS results are less than 1% discordant starting from isolated plasma cells and return successful results in up to 95% of cases. 270 MM patients from Johns Hopkins University (MD) and Weill Cornell Medicine (NY) had both FISH and MyPRS gene expression profiling performed between 2012 and 2016 using standard and previously published methodology, respectively. Results: Retrospective review of the matched FISH and MyPRS results showed: 25/28 (89%) patients wish FISH-identified t(4;14) were classified as MMSET (MS) subtype. 10/10 (100%) patients with t(14;16) or t(14;20) were classified as MAF-like (MF) subtype 62/67 (93%) patients with t(11;14) were assigned to the Cyclin D (1 or 2) subtype. Patients with FISH hyperdiploidy status were classified as the Hyperdiploid (HY) subtype or had multiple gains detected by the separate MyPRS Virtual Karyotype (VK) algorithm, included in MyPRS. TP53del was seen in patients with multiple molecular subtypes, predominantly Proliferation (PR) and MMSET (MS). Assessment of TP53 function by gene expression is a more clinically relevant prognostic marker than TP53del, as dysregulation of the tumor suppressor is affected by mutations as well as deletions. Analysis of the TP53 expression in the 39 patients with delTP53 showed a statistically significant difference, compared to patients without this deletion (P<0.0001). Conclusion: Gene expression profiling is a superior and more reliable method for determining an individual patients' prognostic risk status. The molecular subtypes of MM, as reported by Signal Genetics MyPRS assay, are driven by large-scale changes in gene expression caused by or closely associated with chromosomal changes, including translocations. Physicians who are managing myeloma patients and wishing to base their assessment of risk on R-ISS or mSMART Guidelines may obtain the required data points from either FISH or MyPRS, with the latter offering lower QNS rates, higher reproducibility, assessment of a larger number of cells and a substantially lower price point ($5,480 vs. $1,912; 2016 CMS data). A larger cohort study is now underway to further validate these observations. Figure GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Figure. GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Disclosures Van Laar: Signal Genetics, Inc.: Employment. Borrelo:Sidney Kimmel Cancer Institute: Employment. Jabalayan:Weill Cornell Medical Center: Employment. Niesvizky:Celgene: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Onyx: Consultancy, Research Funding, Speakers Bureau. Zielinski:Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment.

scholarly journals Significance of Modified Risk Stratification Msmart 3.0 and Autologous Stem Cell Transplantation for Patients with Newly Diagnosed Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5593-5593
Author(s):  
Andrey Garifullin ◽  
Sergei Voloshin ◽  
Vasily Shuvaev ◽  
Irina Martynkevich ◽  
Elizaveta Kleina ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Impact ◽  
Newly Diagnosed ◽  
Genetic Abnormalities ◽  
Standard Risk

Background The risk-stratification systems are repeatedly updated in accordance with the emergence of new information about the prognostic impact of anomalies and other factors. The most extensive and modern system in this time is mSMART risk stratification involving many parameters such as genetic anomalies, albumin, beta-2-microglobulin, LDH, Plasma Cell S-phase and GEP levels. It is possible to use risk-adapted treatment programs with or without ASCT. Nevertheless, the role of complex karyotype, combination of genetic abnormalities and ASCT remains unclear. Aims To estimate the genetic abnormalities in patients with newly diagnosed multiple myeloma and define the role of risk-stratification and ASCT in prognosis of disease. Methods The study included 159 patients (median age 63 years, range 28 - 83; male: female ratio - 1:1.37) with NDMM. Metaphase cytogenetics on bone marrow samples was done by standard GTG-method. FISH analyses were performed according to the manufacturer's protocol for detection primary IgH translocations, 13q (13q14/13q34) deletion, 1p32/1q21 amplification/deletion, P53/cen 17 deletion (MetaSystems DNA probes). We additional searched the t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20) in patients with IgH translocation. All patient was treated by bortezomib-based programs (VD, CVD, VMP, PAD). ASCT was performed at 42% patients. Results The frequency of genetic abnormalities in NDMM patients was 49% (78/159). IgH translocation was detected in 26.4% (42/159) patients: t(11;14) - 16.3% (26/159), t(4;14) - 5.0% (8/159); TP53/del17p - 5.6% (9/159); 1p32/1q21 amp/del - 12% (19/159); hypodiploidy - 3.1% (5/159); hyperdiploidy - 1.25% (2/159); del5q - 0,6% (1/159); other - not found. Combination two aberrations was discovered in 11.9% (19/159) patients, complex abnormalities (>3 aberrations) - in 4.4% (7/159) patients. The median OS in "two aberration" and "complex abnormalities" groups were lower than in standard-risk mSMART 3.0 (normal, t(11;14), hypodiploidy, hyperdiploidy and other): 49 months, 26 months and was not reached, respectively (p=.00015). The median PFS for these groups was 12 months, 11 months and 30 months, respectively (p=.011). Differences between "two aberration" and "complex abnormalities" groups were not find (p> .05). We modified high-risk (gain 1q, p53 mutation, del 17p deletion, t(4;14), t(14;16), t(14;20), R-ISS stage III, double and triple hit myeloma) mSMART 3.0 by adding "two aberration" and "complex abnormalities" groups on based the OS and PFS results. The final analysis was based on the results of the complex examination of 87 patients: 53 patients in standard-risk group and 34 patients in high-risk group. The median OS in standard-risk mSMART 3.0 was not reached, in high-risk mSMART 3.0mod - 48 months; 5-years OS was 62% and 38%, respectively (p=0.0073). The median PFS was 43 and 29 months, respectively (p=.09). The best results of OS and PFS were reach in both groups of patient who performed ASCT. The median OS in standard-risk mSMART 3.0 with ASCT (n=37) was not reached, in high-risk mSMART 3.0mod with ASCT - 48 months (n=20); standard-risk mSMART 3.0 without ASCT - 40 months (n=16); in high-risk mSMART 3.0mod without ASCT - 22 months (n=14); 5-years OS was 81%, 60%, 33% and 28%, respectively (p=0.0015). The median PFS was not reached, 46, 22 and 19 months, respectively (p=.017). Conclusions The combination of two aberrations and complex abnormalities is unfavorable prognostic markers. The median OS and PFS was higher in standard-risk than high-risk group according mSMART 3.0mod. The ASCT can improve treatment's outcomes and life expectancy especially in patients with high-risk. It can be useful for update risk stratification in a future. Disclosures Shuvaev: Novartis: Consultancy; Pfize: Honoraria; Fusion Pharma: Consultancy; BMS: Consultancy.

Comprehensive High-Resolution Studies Differentiate and Characterize Hypodiploid Multiple Myeloma

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2869-2869
Author(s):  
Scott Van Wier ◽  
Esteban Braggio ◽  
Rafael Fonseca
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
High Resolution ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Proliferation Index ◽  
Comparative Genomic ◽  
Main Gene ◽  
Gene Index

Abstract Abstract 2869 Background: Chromosome abnormalities are universal in multiple myeloma (MM) and will ultimately categorize patients into hyperdiploid and non-hyperdiploid MM. Among non-hyperdiploid patients those that exhibit hypodiploidy have the most aggressive clinical phenotype. What genetic features are unique to hypodiploidy are not fully described. Therefore, we performed a comprehensive high-resolution analysis to differentiate and characterize hypodiploid MM. Materials and methods: MM patients were analyzed using a combination of array-based comparative genomic hybridization (aCGH) (n=275) and gene expression profiling (GEP) (n=239). Agilent 244K and Affymetrix U133A Plus 2.0 arrays were used in the aCGH and GEP experiments, respectively. Hypodiploid MM was differentiated using pseudokaryotyping based on aCGH findings. Samples estimated to have less than or equal to 44 chromosomes were designated hypodiploid, 45–47 chromosomes were nonhyperdiploid and greater than or equal to 48 and less than 74 chromosomes were considered hyperdiploid. Using GEP the main gene indices and signatures associated with outcome were determined including the translocation and cyclin D (TC) classification, UAMS 70-gene index, proliferation index, centrosome signature and NF-kB index. Differentially expressed genes were also investigated. Results: A total of 53 (19%) MM patients were classified into the hypodiploid group, mainly characterized by monosomies of chromosomes 13 (83%), 14 (42%), 22 (23%) and × (50%) (females) with p and/or q-arm aberrations including gains of 1q (51%) and 8q (25%) and losses of 1p (49%), 4p (21%), 4q (23%), 6q (38%), 8p (34%), 12p (25%), 12q (26%), 14q (32%), 16p (25%), 16q (51%) and 17p (25%). Patients with loss of 1p were associated with 4p- (p <0.029), 4q- (p<0.0001), 12p- (p<0.007), 12q- (p<0.0002), any 14 (p<0.022), 16p- (p<0.005), 16q- (p<0.001), and monosomy 22 (p<0.024). Patients with loss of 17p were associated with 12p- (p<0.025), 12q- (p<0.039) and 16q- (p<0.031). The main gene indices and signatures in MM showed that nearly one half of the hypodiploid patients having high-risk disease, ranging from 45% with a high 70-gene index, 47% with high centrosome signature and 51% with a high proliferation index. In addition, hypodiploid patients also displayed a translocation type signature in the TC classification defined by 11q13 (24%), 4p16 (24%) and maf (12%). Overall, 253 genes have >2 fold expression change comparing hypodiploid vs. hyperdiploid including a five fold decrease in the heparin-degrading endosulfatase gene SULF2, a decrease of genes in the TGF-b signaling pathway (MYC, ID3, SMAD1, LTBP1) and those involved in Wnt signaling (DKK1, FRZB). Up regulated genes included those from the p53 signaling pathway and cell cycle (CCND2, CDKN1C, RPRM), cell adhesion molecules (ITGB8, CD28) and tight junction pathway (RRAS2, RRAS, CSDA). Conclusion: This represents the most comprehensive genomic characterization of hypodiploid MM to date. These cases exhibit a high propensity for high-risk gene expression profiles and have a high prevalence of −13, −14, 1q gain and 1p loss as predicted. Given our findings it is likely that hypodiploid is not a separate category but rather the genetic “phenotype” of a more advanced clone. Today, using these two platforms together in a routine setting would provide the most comprehensive genetic analysis, important for individualized therapeutics. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

scholarly journals Circulating Plasma Cells as a Biomarker to Predict Newly Diagnosed Multiple Myeloma Prognosis: Developing Nomogram Prognostic Models

2021 ◽  
Vol 11 ◽  
Author(s):  
Qianwen Cheng ◽  
Li Cai ◽  
Yuyang Zhang ◽  
Lei Chen ◽  
Yu Hu ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Plasma Cells ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Newly Diagnosed ◽  
Training Cohort ◽  
Entire Cohort

Background: To investigate the prognostic value of circulating plasma cells (CPC) and establish novel nomograms to predict individual progression-free survival (PFS) as well as overall survival (OS) of patients with newly diagnosed multiple myeloma (NDMM).Methods: One hundred ninetyone NDMM patients in Wuhan Union Hospital from 2017.10 to 2020.8 were included in the study. The entire cohort was randomly divided into a training (n = 130) and a validation cohort (n = 61). Univariate and multivariate analyses were performed on the training cohort to establish nomograms for the prediction of survival outcomes, and the nomograms were validated by calibration curves.Results: When the cut-off value was 0.038%, CPC could well distinguish patients with higher tumor burden and lower response rates (P &lt; 0.05), and could be used as an independent predictor of PFS and OS. Nomograms predicting PFS and OS were developed according to CPC, lactate dehydrogenase (LDH) and creatinine. The C-index and the area under receiver operating characteristic curves (AUC) of the nomograms showed excellent individually predictive effects in training cohort, validation cohort or entire cohort. Patients with total points of the nomograms ≤ 60.7 for PFS and 75.8 for OS could be defined as low-risk group and the remaining as high-risk group. The 2-year PFS and OS rates of patients in low-risk group was significantly higher than those in high-risk group (p &lt; 0.001).Conclusions: CPC is an independent prognostic factor for NDMM patients. The proposed nomograms could provide individualized PFS and OS prediction and risk stratification.

scholarly journals Prevalence of Smoldering Multiple Myeloma: Results from the Iceland Screens, Treats, or Prevents Multiple Myeloma (iStopMM) Study

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 151-151
Author(s):  
Sigrun Thorsteinsdottir ◽  
Gauti Kjartan Gislason ◽  
Thor Aspelund ◽  
Sæmundur Rögnvaldsson ◽  
Jon Thorir Thorir Oskarsson ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Risk Stratification ◽  
Light Chain ◽  
Research Funding ◽  
Plasma Cells ◽  
Population Based ◽  
M Protein ◽  
Screening Study

Abstract Background Smoldering multiple myeloma (SMM) is an asymptomatic precursor condition to multiple myeloma (MM). Emerging data from clinical trials indicate that - compared to watchful monitoring - initiation of therapy at the SMM stage might be indicated. Currently, there is no established screening for SMM in the general population and therefore patients are identified incidentally. Here, we define for the first time, epidemiological and clinical characteristics of SMM in the general population based on a large (N&gt;75,000) population-based screening study. Methods The iStopMM study (Iceland Screens Treats or Prevents Multiple Myeloma) is a nationwide screening study for MM precursors where all residents in Iceland over 40 years of age and older were invited to participate. Participants with a positive M-protein on serum protein electrophoresis (SPEP) or an abnormal free light chain (FLC) analysis entered a randomized controlled trial with three arms. Participants in arm 1 continued care in the Icelandic healthcare system as though they had never been screened. Arms 2 and 3 were evaluated at the study clinic with arm 2 receiving care according to current guidelines. In arm 3 bone marrow testing and whole-body low-dose CT (WBLDCT) was offered to all participants. SMM was defined as 10-60% bone marrow plasma cells on smear or trephine biopsy and/or M-protein in serum ≥3 g/dL, in the absence of myeloma defining events. Participants in arm 3 were used to estimate the prevalence of SMM as bone marrow biopsy was performed in all participants of that arm when possible. The age- and sex-specific prevalence was determined with a fitted function of age and sex, and interaction between those. Diagnosis at baseline evaluation of the individuals in the study was used to define the point prevalence of SMM. Results Of the 148,704 individuals over 40 years of age in Iceland, 75,422 (51%) were screened for M-protein and abnormal free light chain ratio. The 3,725 with abnormal screening were randomized to one of the three arms, and bone marrow sampling was performed in 1,503 individuals. A total of 180 patients were diagnosed with SMM, of which 109 (61%) were male and the median age was 70 years (range 44-92). Of those, a total of 157 (87%) patients had a detectable M-protein at the time of SMM diagnosis with a mean M-protein of 0.66 g/dL (range 0.01-3.5). The most common isotype was IgG in 101 (56%) of the patients, 44 (24%) had IgA, 2 (1%) had IgM, and 5 (3%) had biclonal M-proteins. A total of 24 (13%) patients had light-chain SMM. Four patients (2%) had a negative SPEP and normal FLC analysis at the time of SMM diagnosis despite abnormal results at screening. A total of 131 (73%) patients had 11-20% bone marrow plasma cells at SMM diagnosis, 32 (18%) had 21-30%, 9 (5%) had 31-40%, and 8 (4%) had 41-50%. Bone disease was excluded with imaging in 167 (93%) patients (MRI in 25 patients, WBLDCT in 113 patients, skeletal survey in 27 patients, FDG-PET/CT in 1 patient), 13 patients did not have bone imaging performed because of patient refusal, comorbidities, or death. According to the proposed 2/20/20 risk stratification model for SMM, 116 (64%) patients were low-risk, 47 (26%) intermediate-risk, and 17 (10%) high-risk. A total of 44 (24%) had immunoparesis at diagnosis. Using the PETHEMA SMM risk criteria on the 73 patients who underwent testing with flow cytometry of the bone marrow aspirates; 39 (53%) patients were low-risk, 21 (29%) patients were intermediate-risk, and 13 (18%) patients were high-risk. Out of the 1,279 patients randomized to arm 3, bone marrow sampling was performed in 970, and 105 were diagnosed with SMM (10.8%). The prevalence of SMM in the total population was estimated to be 0.53% (95% CI: 0.49-0.57%) in individuals 40 years of age or older. In men and women, the prevalence of SMM was 0.70% (95% CI: 0.64-0.75%) and 0.37% (95% CI: 0.32-0.41%), respectively, and it increased with age in both sexes (Figure). Summary and Conclusions Based on a large (N&gt;75,000) population-based screening study we show, for the first time, that the prevalence of SMM is 0.5% in persons 40 years or older. According to current risk stratification models, approximately one third of patients have an intermediate or high risk of progression to MM. The high prevalence of SMM has implications for future treatment policies in MM as treatment initiation at the SMM stage is likely to be included in guidelines soon and underlines the necessity for accurate risk stratification in SMM. Figure 1 Figure 1. Disclosures Kampanis: The Binding Site: Current Employment. Hultcrantz: Daiichi Sankyo: Research Funding; Amgen: Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Curio Science LLC: Consultancy; Intellisphere LLC: Consultancy. Durie: Amgen: Other: fees from non-CME/CE services ; Amgen, Celgene/Bristol-Myers Squibb, Janssen, and Takeda: Consultancy. Harding: The Binding Site: Current Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Landgren: Janssen: Research Funding; Janssen: Other: IDMC; Celgene: Research Funding; Takeda: Other: IDMC; Janssen: Honoraria; Amgen: Honoraria; Amgen: Research Funding; GSK: Honoraria. Kristinsson: Amgen: Research Funding; Celgene: Research Funding.

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

Blood ◽  
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.

Bortezomib-Based Induction Treatments Improve Outcomes of Newly Diagnosed Multiple Myeloma Patients with High-Risk Cytogenetic Abnormalities

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 781-781 ◽  
Author(s):  
Michele Cavo ◽  
Sara Bringhen ◽  
Carolina Terragna ◽  
Paola Omedè ◽  
Giulia Marzocchi ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Board Of Directors ◽  
Plasma Cells ◽  
Risk Group ◽  
High Risk Group ◽  
Newly Diagnosed ◽  
Negative Group ◽  
Advisory Committees ◽  
Cytogenetic Abnormalities

Abstract Abstract 781 Aim of the present study was to evaluate the impact of bortezomib-based induction treatments on clinical outcomes of newly diagnosed multiple myeloma (MM) patients with unfavorable cytogenetic abnormalities. For this purpose, we analyzed 590 bortezomib-treated patients who were screened at diagnosis for the presence of del(13q), t(4;14) and del(17p) by fluorescence in situ hybridization (FISH) on highly purified bone marrow plasma cells. Patients were stratified into the following 3 groups based on 1) the absence of any cytogenetic abnormality (n=261, or 44%) or 2) the presence of del(13q) alone (n=175, or 30%) or 3) positivity for t(4;14) and/or del(17p) (n=154, or 26%). In the great majority of the patients, loss of 17p was detected in more than 70% of bone marrow plasma cells, a finding which precluded a comparison with patients carrying del(17p) in a lower percentage of plasma cells. After diagnosis, 218 patients received induction therapy with bortezomib-thalidomide-dexamethasone (VTD), while the remaining 372 patients were treated with bortezomib-melphalan-prednisone (VMP) (n=181) or VMP plus thalidomide (VMPT) (n=191). The median number of bortezomib infusions (1.3 mg/m2) actually received was 24. Baseline characteristics of the 3 groups of patients were comparable, with the exception of a higher frequency of ISS stage 3 among patients with t(4;14) and/or del(17p) as compared with the cytogenetic-negative group (29% vs 17%, respectively; p=0.003). The rates of absence or presence of del (13q), t(4;14) and/or del(17p) were comparable among patients receiving VTD or VMP or VMPT treatments. Best CR to overall treatment protocols was 39% for the cytogenetic-negative group and 44% for high-risk patients carrying t(4;14) and/or del(17p). With a median follow-up of 27.5 months, median PFS was 40.5 months for patients without cytogenetic abnormalities as compared with 34 months for the high-risk group (p=0.7), while it was not reached after 38 months in the group with del(13q) alone (p not statistically significant for comparison with the other two groups). Overall, the frequency of events was 31% for patients without cytogenetic abnormalities or with del(13q) alone in comparison with 38% for those with high-risk cytogenetic profiles (p=0.15). Median OS was not reached in any of the 3 groups. Forty-month projected OS rates were 89% for the cytogenetic-negative group, 81% for the group with del(13q) alone (p=0.6) and 77% for the high-risk group (p=0.003 for comparison between this latter and the cytogenetic-negative group). Patients with t(4;14) and/or del(17p) had a shorter OS after relapse in comparison with the cytogenetic-negative group (20-month projected rates: 60% vs 76%, respectively; p=0.01). To more carefully evaluate the prognostic relevance of high-risk cytogenetic abnormalities, we stratified patients in the high-risk group into the following 3 subgroups: 1) t(4;14)-positive but del(17p)-negative (84 patients); 2) del(17p)-positive in the absence of t(4;14) (54 patients); t(4;14)-positive and del(17p)-positive (16 patients). Median PFS was not reached after 40 months for patients with t(4;14) alone, while it was 33 months for patients with del(17p) alone (p=0.1) and was 18.5 months for those who carried both these abnormalities (p=0.0008 for comparison between these latter patients and t(4;14)-positive patients). Overall, the frequency of events was 30% and 41% for patients carrying either t(4;14) or del(17p), respectively (p=0.13), while it was as high as 69% for patients with both these abnormalities. The 40-month projected OS rates for these 3 subgroups were 79%, 82% and 64%, respectively (p not significant). In conclusion, the present analysis of a large series of newly diagnosed MM patients receiving bortezomib-based induction treatments showed that: 1) del(13q) alone had no adverse effect on both PFS and OS; 2) the presence of t(4;14) and/or del(17p) did not adversely influence PFS, but was associated with a shorter OS, due at least in part to a worse outcome after relapse; 3) in comparison with t(4;14), del(17p) alone did not predicted for shorter PFS and OS, possibly as a result of the relatively long-term exposure to bortezomib); 4) the presence of both del(17p) and t(4;14) was likely to confer a particularly dismal clinical outlook, a finding which needs to be confirmed in larger series of patients. Disclosures: Cavo: Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium Pharmaceuticals: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Off Label Use: Use of bortezomib-based treatment for newly diagnosed multiple myeloma. Petrucci:CELGENE: Honoraria; JANSSEN-CILAG: Honoraria. Boccadoro:NOVARTIS: Honoraria; CELGENE: Honoraria; JANSSEN-CILAG: Honoraria. Palumbo:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau, no; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, no.

scholarly journals Prediction of Primary Treatment Outcome Using Gene Expression Profiling of Pre-Treatment Biopsies Obtained from Childhood and Adolescent Hodgkin Lymphoma Patients

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 175-175 ◽  
Author(s):  
Anja Mottok ◽  
Rebecca Lea Johnston ◽  
Fong Chun Chan ◽  
David W Scott ◽  
Debra L. Friedman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Treatment Outcome ◽  
High Risk ◽  
Risk Stratification ◽  
Predictive Model ◽  
Expression Profiling ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Pre Treatment

Abstract Introduction: Hodgkin lymphoma (HL) is a common malignancy of children and adolescents and is highly curable with a 5-year overall survival (OS) rate of > 97%, yet dose-intensified chemotherapy regimens in combination with radiation therapy come with a high cost in form of long-term toxicity and morbidity (Castellino et al., Blood 2011). This major clinical challenge has resulted in the evaluation of risk-adapted treatment regimens in clinical trials aiming to achieve the optimal equilibrium between high survival rates and prevention of treatment-related toxicity. However, risk stratification is currently limited to the use of clinical factors as there are no validated integral biomarkers that can be employed to either improve risk stratification or as surrogate markers of treatment outcome in pediatric HL. The aim of our study was to perform gene expression profiling (GEP) to uncover disease biology underlying treatment response and develop a prognostic model to tailor first-line therapy in pediatric HL. Methods: We selected 203 formalin-fixed, paraffin-embedded tissue (FFPET) specimens from patients enrolled in a randomized phase 3 clinical trial (AHOD0031) of the Children's Oncology Group (COG) based on the availability of archived FFPET blocks. That trial was designed to assess the value of early chemotherapy response for tailoring subsequent therapy in intermediate-risk pediatric HL. We performed GEP on RNA extracted from pre-treatment FFPET biopsies using NanoString technology and a customized codeset encompassing probes for 784 genes. These genes were either previously reported to be associated with prognosis and outcome in HL or represent the cellular diversity of the tumor microenvironment. Event free survival (EFS) and OS were estimated using the Kaplan-Meier method. Gene expression data were used to develop a predictive model for EFS using penalized Cox regression with parameters trained using leave-one-out cross-validation. Results: Of the 203 tissue samples obtained from the Biopathology Center at the Cooperative Human Tissue Network, 182 (89.7%) passed quality assurance testing, similar to the pass rate achieved for adult HL samples obtained from the Eastern Cooperative Oncology Group trial E2496 (Scott et al., JCO 2013). We applied our previously published 23-gene predictor for OS (Scott et al., JCO 2013), developed using biopsies from adult HL patients to our pediatric cohort. After calibrating the new codeset, 53 patients were classified as "high-risk" and 129 as "low-risk". Importantly, the model failed to predict inferior outcomes in the "high-risk" group (5-year OS 100% vs 95%, log-rank P = 0.125; 5-year EFS 82% vs 70%, log-rank P = 0.159), with patients in the "high risk" group trending to have superior outcomes than the "low risk" patients. Moreover, only 2 genes from this model, IFNG and CXCL11, were significantly associated with EFS in univariate Cox regression analysis (P = 0.003 and 0.048, respectively) but with inverse hazard ratios in the pediatric group compared to adult patients. Therefore, we sought to develop a novel EFS predictive model for pediatric patients treated in the AHOD0031 trial. Using univariate Cox regression we identified 79 genes significantly associated with EFS (raw P < 0.05). Using the expression of these 79 genes as the input to penalized Cox regression, we developed a 16-gene model to predict EFS in our training cohort. Using an optimized cut-off for the model score, 31% of patients were designated high-risk and had significantly inferior post-treatment outcome (5-year EFS 38% vs 89%, log-rank P < 0.0001). When multivariate analyses were performed including our EFS-model score, disease stage and initial treatment response as variables, only the model score was significantly associated with EFS (P < 0.0001, HR 11.3 (95% CI 5.5-23.4)). Conclusions: Failure of the GEP-based model developed in adult HL suggests distinct biology underlies treatment failure in the pediatric age group. We describe the development of a novel predictive model for EFS in intermediate-risk pediatric HL patients that will be validated in an independent cohort of patients treated in the AHOD0031 trial. Successful validation of the model may provide a clinically relevant biomarker for pediatric and adolescent HL patients allowing refinement of risk stratification and the combination of molecular and clinical risk factors at diagnosis. Disclosures Scott: Celgene: Consultancy, Honoraria; NanoString: Patents & Royalties: Inventor on a patent that NanoString has licensed.

Prediction of High and Low-Risk Multiple Myeloma Based on the EMC92 Gene Expression Signature and the International Staging System

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3358-3358
Author(s):  
Rowan Kuiper ◽  
Martin H. Van Vliet ◽  
Erik H. van Beers ◽  
Annemiek Broijl ◽  
George Mulligan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Risk Stratification ◽  
Research Funding ◽  
Prognostic Markers ◽  
Risk Group ◽  
Validation Data ◽  
P Values ◽  
Hazard Ratios ◽  
Rank Score

Abstract Introduction: The variable survival of multiple myeloma patients requires solid prognostic markers. It is unclear how currently used markers relate to each other. Here, ISS, cytogenetics and gene expression profiling (GEP) were combined to find novel risk stratifications in a discovery/validation setting. Next, novel combinations were compared with the currently existing markers. Methods: The following datasets were used: HOVON-65/GMMG-HD4, UAMS-TT2, UAMS-TT3, MRC-IX, APEX and IFM (total number of patients: 4720). In total, 20 markers were evaluated, including cytogenetic markers (deletions of 17p (del17p) and 13q (del13q), gain of 1q (add1q) and translocations t(4;14), t(11;14), t(14;16) and t(14;20)), GEP markers (EMC92, UAMS70, UAMS17, UAMS80, MRC-IX-6, IFM15, HM19 and GPI50) and ISS. In addition, the combination of del17p, t(4;14) and ISS (Avet-Loiseau et al., Leukemia, 27:711-717; 2013) and the combination of del17p, t(4;14) and add1q (Broyl et al., Blood, 121: 624-627, 2013) were included. Reevaluation of markers was performed by Cox regression analysis stratified for cohort combined with the likelihood-ratio test. As a result, cytogenetic markers t(11;14), t(14;16) and t(14;20) were excluded. Thus, 17 markers were analyzed in the combination analysis (number of marker pairs: 136). This resulted in 24 combinations after validation. The Akaike information criterion (AIC) was used to rank the 41 markers (24 novel combinations and 17 existing markers). Importantly, to avoid training bias, training data were excluded when testing markers originally developed using those data (i.e. GEP markers and FISH/ISS). Results: Reevaluation of existing markers in relation to overall survival (OS) demonstrated solid performance of poor-risk cytogenetic markers del17p, del13q, t(4;14) and add1q, but strikingly not for t(11;14), which is thought to predict for favorable outcome. The hazard ratios and p-values were 2.3 (p<1x10-15; del17p), 1.7 (p<1­x10-15; del13q), 2.2 (p<1x10-15; t(4;14)), 2.0 (p<1x10-15; add1q) and 0.9 (p=0.5; t(11;14)). Top performing GEP markers EMC92 and UAMS17 demonstrated good performance with hazard ratios and p-values of 2.8 (p<1x10-15) and 3.0 (p<1x10-15), respectively. In the subsequent pair-wise combination analysis, ISS demonstrated to be a valuable partner to both GEP and cytogenetic markers (Figure). Ranking all combinations as well as current markers showed that the ISS-GEP combinations consistently rank at the top. The EMC92-ISS combination was among the strongest predictors for OS, resulting in a four group risk stratification. In a pooled data analysis, the median survival was 24, 47 and 61 months and median not reached, for the highest-risk to the lowest-risk group, respectively. The highest risk group amounted to 17% and the lowest risk group to 38% of patients. Other high scoring combinations, ranking 2nd-5th, respectively, were ISS-UAMS17, ISS-HM19, ISS-UAMS80 and ISS-UAMS70. The compound FISH/ISS marker was in 7th position and ISS itself ranked 22nd out of 41. In general combinations of markers tended to perform better than univariate markers. The best univariate marker was EMC92 (14th out of 41). Conclusions: Both GEP and FISH markers are solid prognostic markers, with GEP markers demonstrating better predictions in unbiased comparisons than FISH markers. Prognostic markers can be improved by combining markers, as is evident when considering both existing combinations, such as the FISH/ISS marker proposed by Avet-Loiseau et al., and novel combinations. The EMC92-ISS model is a novel combination which is an improvement compared to currently used markers, offering a robust 4-group risk stratification based on biology and clinical parameters. This model is a good candidate to be validated in a clinical trial in order to stratify patients for treatment. Figure. Figure. Ranking of novel combinations and existing markers. This analysis represents the validation data, using OS.Markers are vertically ordered by rank score indicated on the horizontal axis. The rank score is based on the AIC and was determined for two markers using their largest possible intersecting subset of known data. After combining all markers (or combinations) with all others, the scores were calculated as 1 minus the proportion of observations for which a marker had the lowest AIC. In order to estimate uncertainty, the score was estimated in a bootstrapping procedure using 100 cycles. Disclosures Van Vliet: SkylineDX: Employment. van Beers:SkylineDX: Employment. Mulligan:Millennium Pharmaceuticals: Employment. Gregory:Novartis: Research Funding; Schering Health Care Ltd: Research Funding; Pharmion: Research Funding; Celgene: Research Funding; Ortho Biotech: Research Funding. Goldschmidt:Janssen-Cilag: Honoraria, Research Funding, Speakers Bureau; Polyphor: Research Funding; Celgene: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; Chugai: Research Funding, Speakers Bureau; Onyx: Consultancy, Speakers Bureau; Millenium: Consultancy, Speakers Bureau. Lokhorst:Celgene: Honoraria; Johnson-Cilag: Honoraria; Mudipharma: Honoraria. Sonneveld:Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Onyx: Honoraria, Research Funding; Millenium: Honoraria, Research Funding.

Efficacy and Safety of Carfilzomib and Dexamethasone Vs Bortezomib and Dexamethasone in Patients with Relapsed Multiple Myeloma Based on Cytogenetic Risk Status: Subgroup Analysis from the Phase 3 Study Endeavor (NCT01568866)

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 30-30 ◽  
Author(s):  
Wee-Joo Chng ◽  
Hartmut Goldschmidt ◽  
Meletios A. Dimopoulos ◽  
Philippe Moreau ◽  
Douglas Joshua ◽  
...  
Keyword(s):  
High Risk ◽  
Research Funding ◽  
Plasma Cells ◽  
Risk Group ◽  
High Risk Group ◽  
Advisory Committees ◽  
Risk Status ◽  
Grade 3 ◽  
Standard Risk ◽  
Support Research

Abstract Introduction: Single-agent carfilzomib has previously shown activity in patients with relapsed and refractory multiple myeloma (MM) who have high-risk cytogenetic abnormalities (Jakubowiak et al, Leukemia 2013;27:2351-56). In the randomized phase 3 study ENDEAVOR (NCT01568866; N=929), carfilzomib plus dexamethasone (Kd) demonstrated a clinically meaningful and statistically significant 2-fold improvement in median progression-free survival (PFS) compared with bortezomib plus dexamethasone (Vd; 18.7 vs 9.4 months; hazard ratio [HR]: 0.53; 95% confidence interval [CI]: 0.44-0.65; P<.0001) (Dimopoulos et al, J Clin Oncol 2015;33:abstr 8509; Dimopoulos et al, Haematologica 2015;100[s1]:abstr LB2071). Herein we present results of a preplanned subgroup analysis of the efficacy and safety of Kd vs Vd in the ENDEAVOR study based on baseline cytogenetic risk status. Methods: Adult patients with relapsed MM (RMM; 1-3 prior lines of therapy) were eligible. Patients in the Kd arm received carfilzomib (30-minute intravenous [IV] infusion) on days 1, 2, 8, 9, 15, and 16 (20 mg/m2 on days 1 and 2 of cycle 1; 56 mg/m2 thereafter) and dexamethasone 20 mg on days 1, 2, 8, 9, 15, 16, 22, and 23 of a 28-day cycle. Patients in the Vd arm received bortezomib 1.3 mg/m2 (IV bolus or subcutaneous injection) on days 1, 4, 8, and 11 and dexamethasone 20 mg on days 1, 2, 4, 5, 8, 9, 11, and 12 of a 21-day cycle. Cycles were repeated until disease progression, withdrawal of consent, or unacceptable toxicity. The primary end point was PFS. Secondary end points included overall survival, overall response rate (ORR), duration of response (DOR), rate of grade ≥2 peripheral neuropathy (PN), and safety. Fluorescence in situ hybridization was used to assess cytogenetic risk status. The high-risk group was defined as those patients with the genetic subtypes t(4;14) or t(14;16) in ≥10% of screened plasma cells, or deletion 17p in ≥20% of screened plasma cells based on central review of bone marrow samples obtained at study entry; the standard-risk group consisted of patients without these genetic subtypes. Results: A total of 929 patients were randomized (Kd: 464; Vd: 465). Baseline cytogenetic risk status was well-balanced between the treatment arms (high-risk: Kd, 20.9%; Vd, 24.3%; standard-risk: Kd, 61.2%; Vd, 62.6%; unknown: Kd, 17.9%; Vd, 13.1%). Efficacy end points by baseline cytogenetic risk status are presented in the Table; Kaplan-Meier PFS curves by baseline cytogenetic risk status are shown in the Figure. Median PFS in the high-risk group (n=210) was 8.8 months (95% CI: 6.9-11.3) for Kd vs 6.0 months (95% CI: 4.9-8.1) for Vd (HR: 0.646; 95% CI: 0.453-0.921). Median PFS in the standard-risk group (n=575) was not estimable for Kd (95% CI: 18.7-not estimable) vs 10.2 months (95% CI: 9.3-12.2) for Vd (HR: 0.439; 95% CI: 0.333-0.578). ORRs (≥partial response) were 72.2% (Kd) vs 58.4% (Vd) in the high-risk group and 79.2% (Kd) vs 66.0% (Vd) in the standard-risk group. In the high-risk group, 15.5% (Kd) vs 4.4% (Vd) achieved a complete response (CR) or better. In the standard-risk group, 13.0% (Kd) vs 7.9% (Vd) achieved ≥CR. Median DOR in the high-risk group was 10.2 months for Kd vs 8.3 months for Vd. Median DOR in the standard-risk group was not estimable for Kd vs 11.7 months for Vd. Grade ≥3 adverse events (AEs) were reported at higher rates with Kd vs Vd in the high- and standard-risk groups (70.1% vs 63.1% and 73.9% vs 68.3%). Rates of grade ≥3 AEs of interest by baseline cytogenetic risk status are shown in the Table. Grade ≥2 PN was reported at lower rates with Kd vs Vd in the high-risk group (3.1% vs 35.1%; odds ratio: 0.059; 95% CI: 0.018-0.198) and also in the standard-risk group (6.4% vs 33.4%; odds ratio: 0.135; 95% CI: 0.079-0.231). Conclusion: As expected, median PFS for patients with high-risk cytogenetics was lower compared with the overall population; however, patients treated with Kd had a clinically meaningful improvement in PFS compared with Vd in patients with high- or standard-risk cytogenetics. Higher response rates, a greater depth of response, and longer DOR were also observed with Kd vs Vd across cytogenetic subgroups. Kd had a favorable benefit-risk profile in patients with high-risk relapsed MM, and was superior to Vd, regardless of baseline cytogenetic risk status. Disclosures Goldschmidt: BMS: Consultancy, Research Funding; Amgen, Takeda: Consultancy; Onyx: Consultancy, Honoraria; Janssen, Celgene, Novartis: Consultancy, Honoraria, Research Funding; Chugai, Millennium: Honoraria, Research Funding. Dimopoulos:Onyx: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Janssen-Cilag: Honoraria; Genesis: Honoraria; Amgen: Honoraria; Novartis: Honoraria. Moreau:Novartis, Janssen, Celgene, Millennium, Onyx Pharmaceuticals: Consultancy, Honoraria. Joshua:Celgene: Membership on an entity's Board of Directors or advisory committees. Palumbo:Novartis, Sanofi Aventis: Honoraria; Celgene, Millennium Pharmaceuticals, Amgen, Bristol-Myers Squibb, Genmab, Janssen-Cilag, Onyx Pharmaceuticals: Consultancy, Honoraria. Facon:Onyx/Amgen: Membership on an entity's Board of Directors or advisory committees. Ludwig:Celgene Corporation: Honoraria, Speakers Bureau; Onyx: Honoraria, Speakers Bureau; Bristol Myers Squibb: Honoraria, Speakers Bureau; Janssen Cilag: Honoraria, Speakers Bureau; Takeda: Research Funding. Niesvizky:Celgene, Millennium, Onyx: Consultancy, Speakers Bureau. Oriol:Celgene, Janssen, Amgen: Consultancy, Speakers Bureau. Rosinol:Celgene, Janssen: Honoraria. Gaidano:Morphosys, Roche, Novartis, GlaxoSmith Kline, Amgen, Janssen, Karyopharm: Honoraria, Other: Advisory Boards; Celgene: Research Funding. Weisel:Takeda: Other: Travel Support; Novartis: Other: Travel Support; Onyx: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Travel Support, Research Funding; Amgen: Consultancy, Honoraria, Other: Travel Support; Celgene: Consultancy, Honoraria, Other: Travel Support, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Other: Travel Support; Noxxon: Consultancy. Gillenwater:Onyx, Amgen: Employment, Other: Stock. Mohamed:Onyx/Amgen: Employment, Other: Stock. Feng:Amgen/Onyx: Employment, Equity Ownership. Hájek:Janssen-Cilag: Honoraria; Celgene, Amgen: Consultancy, Honoraria.

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