scholarly journals Development and Validation of a High Risk Multiple Myeloma Gene Expression Index from RNA Sequencing: An Mmrf Commpass Analysis

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1895-1895
Author(s):  
Daniel Penaherrera ◽  
Sheri Skerget ◽  
Austin Christofferson ◽  
Jessica Aldrich ◽  
Sara Nasser ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Microarray Data ◽  
Chromosome 1 ◽  
Test Set ◽  
Cox Regression Analysis ◽  
Hazard Ratios ◽  
Rnaseq Data ◽  
Gene Index

Abstract Multiple Myeloma (MM) is a genetically heterogeneous disease of plasma cells that generally exhibits chromosomal abnormalities and distinct gene expression signatures. Previous studies have sought to identify gene expression indices using microarray technology to discern genes associated with survival outcomes to predict whether a newly diagnosed patient has an aggressive form of the disease. One such MM-specific index is the UAMS 70 gene index, which is composed of 51 over- and 19 under-expressed genes. This index was developed using Affymetrix U133Plus2.0 microarray data from 532 MM patients at diagnosis by computing log-rank test statistics on gene expression quartiles. Despite consistently achieving a high performance across a variety of MM datasets, issues arise when applying this index to RNAseq data. Here we address those issues, deriving an independent index based on the RNAseq data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study (NCT01454297), and benchmark its performance to an implementation of the UAMS 70 gene index. UAMS index scores are computed by taking the difference between the average log2-scale expression of the 51 over- and 19 under-expressed genes. We applied this calculation to RNAseq data analyzed using Sailfish, Salmon v7.2, and HTseq counts collected from 41 Multiple Myeloma Genomics Initiative samples and compared the results to scores from matching GCRMA, MAS5, RMA, and PLIER16 Affymetrix U133Plus2.0 microarray data. Differences in the distribution of index values across data types led to nonconforming classification of high-risk individuals. Additionally, when applied to RNAseq data, several Affymetrix probesets did not uniquely match to gene annotations from Ensembl-v74. This reduced the number of genes upon which our UAMS score was calculated to 61 genes. Of the original 51 over-expressed probes, only 44 uniquely mapped genes remained after 7 multi-mapped probes are removed and similarly, out of the 19 under-expressed genes only 17 were uniquely mapped. Given the complication of probe-gene mismatch and inconsistencies identifying high-risk individuals when applied to RNAseq data, we developed an independent index using the baseline RNAseq data from the MMRF CoMMpass Study IA13 dataset. From a training set (n=375) of RNAseq data measuring 56430 genes, we performed univariate log-rank tests on expression quartiles associated with disease-related survival while controlling for an FDR of 2.5%, resulting in 23 under- and 332 over-expressed genes. Subsequent multivariate Cox regression analysis and backward stepwise selection culminated in the identification of the CoMMpass RNAseq index, which is based on the ratio of mean expression values of 87 genes (19 under- and 68 over-expressed) predictive of high risk (hazard ratio [HR] = 8.7341, 95% CI = 5.615-13.58, p < 0.001). Validation on the test set (n=251) yielded a HR of 5.612 (95% CI = 3.066-10.27, p < 0.001) as compared to a HR of 4.753 (95% CI = 2.688-8.403, p < 0.001) achieved with the adapted UAMS index. Adjusting for a patient's International Staging System (ISS) stage revises these hazard ratios to 6.236 (95% CI = 3.345-11.627, p < 0.001) and 3.6420 (95% CI = 1.9726-6.724, p < 0.001) for the CoMMpass RNAseq and the adapted UAMS indices, respectively. Furthermore, the distribution of CoMMpass RNAseq index values across the training and test set show no observable bias with respect to three main therapy arms, suggesting it is predictive of high risk independent of treatment. Our newly derived CoMMpass RNAseq index shares one gene in common with the UAMS 61 gene index (CENPW) and recovers two over-expressed genes (FABP5, TAGLN2), which were removed from the UAMS 70 gene index due to probe multimapping. When the recovered genes are added back to the UAMS index, the unadjusted and adjusted hazard ratios measured for the test set are 5.173 (CI = 2.926-9.146, p < 0.001) and 4.022 (CI = 2.1840-7.408, p < 0.001), respectively. Of the original 70 genes in the UAMS index, 21 (30%) map to chromosome 1, which frequently exhibits copy number gains in MM. Only 11 of the 87 (13%) genes in our proposed index map to chr1, which indicates that, given its performance, the newly derived list of genes may represent a more diverse index to predict, and provide novel insights into, high risk MM. Altogether, the CoMMpass RNAseq index identifies a high risk signature in 13% of MM patients and outperforms the UAMS index. Disclosures Lonial: Amgen: Research Funding.

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.

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.

scholarly journals Whole-genome optical mapping of bone-marrow myeloma cells reveals association of extramedullary multiple myeloma with chromosome 1 abnormalities

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eva Kriegova ◽  
Regina Fillerova ◽  
Jiri Minarik ◽  
Jakub Savara ◽  
Jirina Manakova ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Copy Number ◽  
Optical Mapping ◽  
Chromosome 1 ◽  
Gene Copy ◽  
Whole Genome ◽  
Myeloma Cells ◽  
Genomic Architecture

AbstractExtramedullary disease (EMM) represents a rare, aggressive and mostly resistant phenotype of multiple myeloma (MM). EMM is frequently associated with high-risk cytogenetics, but their complex genomic architecture is largely unexplored. We used whole-genome optical mapping (Saphyr, Bionano Genomics) to analyse the genomic architecture of CD138+ cells isolated from bone-marrow aspirates from an unselected cohort of newly diagnosed patients with EMM (n = 4) and intramedullary MM (n = 7). Large intrachromosomal rearrangements (> 5 Mbp) within chromosome 1 were detected in all EMM samples. These rearrangements, predominantly deletions with/without inversions, encompassed hundreds of genes and led to changes in the gene copy number on large regions of chromosome 1. Compared with intramedullary MM, EMM was characterised by more deletions (size range of 500 bp–50 kbp) and fewer interchromosomal translocations, and two EMM samples had copy number loss in the 17p13 region. Widespread genomic heterogeneity and novel aberrations in the high-risk IGH/IGK/IGL, 8q24 and 13q14 regions were detected in individual patients but were not specific to EMM/MM. Our pilot study revealed an association of chromosome 1 abnormalities in bone marrow myeloma cells with extramedullary progression. Optical mapping showed the potential for refining the complex genomic architecture in MM and its phenotypes.

scholarly journals Erratum: Dose-dense and less dose-intense total therapy 5 for gene expression profiling-defined high-risk multiple myeloma

2016 ◽  
Vol 6 (9) ◽  
pp. e471-e471 ◽  
Author(s):  
Y Jethava ◽  
A Mitchell ◽  
M Zangari ◽  
S Waheed ◽  
C Schinke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Total Therapy ◽  
Dose Dense

scholarly journals Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yun Zhong ◽  
Zhe Liu ◽  
Dangchi Li ◽  
Qinyuan Liao ◽  
Jingao Li
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Test Set ◽  
Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

scholarly journals F18-fluorodeoxyglucose positron emission tomography in the context of other imaging techniques and prognostic factors in multiple myeloma

Blood ◽  
2009 ◽  
Vol 114 (10) ◽  
pp. 2068-2076 ◽  
Author(s):  
Twyla B. Bartel ◽  
Jeff Haessler ◽  
Tracy L. Y. Brown ◽  
John D. Shaughnessy ◽  
Frits van Rhee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Positron Emission Tomography ◽  
Multiple Myeloma ◽  
High Risk ◽  
Expression Profiling ◽  
Imaging Techniques ◽  
Emission Tomography ◽  
Fluorodeoxyglucose Positron Emission Tomography ◽  
Positron Emission ◽  
Fluorodeoxyglucose Positron Emission

Abstract F18-fluorodeoxyglucose positron emission tomography (FDG-PET) is a powerful tool to investigate the role of tumor metabolic activity and its suppression by therapy for cancer survival. As part of Total Therapy 3 for newly diagnosed multiple myeloma, metastatic bone survey, magnetic resonance imaging, and FDG-PET scanning were evaluated in 239 untreated patients. All 3 imaging techniques showed correlations with prognostically relevant baseline parameters: the number of focal lesions (FLs), especially when FDG-avid by PET-computed tomography, was positively linked to high levels of β-2-microglobulin, C-reactive protein, and lactate dehydrogenase; among gene expression profiling parameters, high-risk and proliferation-related parameters were positively and low-bone-disease molecular subtype inversely correlated with FL. The presence of more than 3 FDG-avid FLs, related to fundamental features of myeloma biology and genomics, was the leading independent parameter associated with inferior overall and event-free survival. Complete FDG suppression in FL before first transplantation conferred significantly better outcomes and was only opposed by gene expression profiling-defined high-risk status, which together accounted for approximately 50% of survival variability (R2 test). Our results provide a rationale for testing the hypothesis that myeloma survival can be improved by altering treatment in patients in whom FDG suppression cannot be achieved after induction therapy.

Gene Expression Profiling (GEP) of Purified Plasma Cells at Baseline and 48hr after-Dexamethasone (D) or Thalidomide (T) Improve Outcome Predicition of Baseline GEP Alone in Patients with Multiple Myeloma (MM) Treated with Total Therapy 2 (TT2).

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 502-502
Author(s):  
Bart Burington ◽  
John Shaughnessy ◽  
Bart Barlogie ◽  
Crowley John
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Prediction Models ◽  
Single Agent ◽  
Baseline Risk ◽  
P Value ◽  
P Values ◽  
Cytogenetic Abnormalities ◽  
Change Models ◽  
Hazard Ratios

Abstract The prognosis of patients with MM varies widely. High risk is best captured by cellular and molecular genetic features. Objective: to determine whether predictive power of baseline GEP and metaphase cytogenetic abnormalities (CA) could be improved by availability of GEP data obtained 48hr after single agent D or T, pre-therapy. A total of 668 patients were enrolled on TT2, 323 randomized to T and 345 without T (ASCO 05). When randomized to T/no T, Baseline and 48 hr GEP samples were obtained from 32/41 receiving a test dose of T/D vs 10/14 receiving full VAD+T/VAD regimen. A total of 97 baseline/early treatment GEP pairs were analyzed. Combined baseline expression and 48hr expression changes of 151 genes predicted EFS at a false discovery rate (FDR) of 10%. The table compares baseline EFS high-risk dysregulation to the direction of 48 hour changes, confering improved EFS. Decreases over 48 hours are associated with improved EFS in 74 of 78 genes (upregulated expression confers poor survival at baseline). In the remaining 4, perturbation in the direction of an additional increase may be a marker of early response. With EFS-associated genes, we trained 15 EFS prediction models using baseline expression and 15 prediction models using the change in expression between baseline and 48 hours. Training sets were random splits of 97 patients and baseline and change models separately predicted an EFS risk index in the remaining validation patients (standardized to a variance of 1). Risk indices were compared to an indicator of cytogenetic abnormalities (CA) among validation patients using multivariate proportional hazards analyses. The table shows median hazard ratios and p-values for competing predictors in 15 validation sets. Without cytogenetics, combined GEP baseline and change indices were significant predictors in all 15 validation sets (median combined P-value of 0.002). The table shows median performing GEP model of 15 in a multivariate analysis including cytogenetics for all 97 patients. 48 hour changes in gene expression in newly diagnosed myeloma patients can significantly predict EFS in validated prediction models, alone and in combination with baseline GEP. After adjustment for baseline and 48-hour GEP change indices, metaphase cytogenetics is no longer a significant predictor in independent patient samples. Baseline EFS risk and 48 hour changes associated with good outcome in 151 EFS-associated genes. Improved EFS Decrease (HR&gt;=1 Increase (HR &lt;1) Baseline High Risk Downregulated (HR&lt;1) 6 67 Upregulated (HR&gt;+ 1) 74 4 Median Hazard Ratios and P-values for Multivariate Models in 15 Validation Sets HR P # of p-values below .05 (of 15) GEP baseline Risk 2.1 0.037 10 EP 48 hr change risk 1.9 0.052 7 CA 1.6 0.310 0 Median validation set overall P-value 0.0003 Median GEP EFS baseline/48 hour EFS prediction model n=97 HR P GEP baseline Risk 2.0 0.004 GEP 48 hr change risk 2.6 0.001 CA 1.4 0.330

Epigenetic Regulation of Cancer-Testis Antigen Expression in Multiple Myeloma and Correlation with High-Risk Cytogenetics.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2923-2923
Author(s):  
Zandra K. Klippel ◽  
Akshay Srivatsan ◽  
Andrea M. H. Towlerton ◽  
Jeffrey Chou ◽  
Tuna Mutis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
T Cell ◽  
High Risk ◽  
Specific Immunotherapy ◽  
Mononuclear Cells ◽  
T Antigen ◽  
T Cell Epitopes ◽  
Epigenetic Modulation ◽  
High Level

Abstract Abstract 2923 Cancer-testis (C-T) antigens are often expressed in advanced multiple myeloma and therefore represent attractive potential targets for immunotherapy with vaccines or adoptive T-cell transfer. To estimate the fraction of myeloma patients that might be eligible for C-T antigen-specific immunotherapy, we prospectively collected bone marrow samples from 21 multiple myeloma patients at different stages of disease, and assessed expression of 20 C-T genes in CD138-enriched mononuclear cells from these samples by real-time PCR. The 20 C-T genes selected for analysis have all been shown to encode T-cell epitopes that elicit CD8+ and/or CD4+ T-cell responses in cancer patients. Unsupervised cluster analysis of the C-T gene expression profiles revealed two distinct groups. One group, comprising 71% of the samples, showed expression of most or all of the C-T genes tested at levels comparable to that observed in testis. The remaining 29% of samples evaluated showed expression of a minority of the 20 C-T genes, typically at lower levels than those observed in the first group. Five of the 21 myeloma patients studied had high-risk cytogenetic abnormalities at the time the marrow samples were obtained, and all 5 patients were assigned to the cluster characterized by high-level C-T gene expression. The expression of C-T genes is controlled, at least in part, by methylation of CpG islands in their promoter regions. To determine if promoter methylation might also regulate expression of C-T genes in primary myeloma cells, we used methylation-specific PCR on bisulfite-treated genomic DNA from the CD138-enriched samples to assess the degree of methylation of the NY-ESO-1 and MAGE-A3 promoters. The expression of these genes was correlated with the methylation status of their promoters. Epigenetic modulation of C-T gene expression in myeloma cells could potentially broaden the applicability of C-T antigen-specific immunotherapy to a larger proportion of myeloma patients. To test the feasibility of this concept, we evaluated the effect of decitabine exposure on C-T gene expression on a panel of cell lines including four myeloma cell lines. High-level expression was seen at baseline in the U266, UM-9, and RPMI-8226 myeloma lines, and was marginally enhanced by 48-hour decitabine exposure. The L363 myeloma line, the HL-60 promyelocytic leukemia line, and the SW480 colon carcinoma line, in contrast, showed low-level C-T expression at baseline that was strongly enhanced by decitabine, and associated with significant demethylation of the NY-ESO-1 and MAGE-A3 promoters. In summary, C-T genes that are known to encode CD8+ or CD4+ T-cell epitopes are expressed in CD138-enriched bone marrow mononuclear cells from a majority of patients with multiple myeloma. Coordinated expression of multiple C-T genes is commonly observed and correlates with the presence of high-risk cytogenetic abnormalities and with incomplete promoter methylation. Epigenetic modulation with agents such as decitabine may increase the proportion of myeloma patients who are eligible for C-T antigen-specific immunotherapy. Disclosures: Mutis: genmab: Research Funding.

Persistent Improvement In Clinical Outcomes With Bortezomib-Thalidomide-Dexamethasone Vs Thalidomide-Dexamethasone Incorporated Into Double Autologous Transplantation For Multiple Myeloma: An Updated Analysis Of Phase 3 Gimema-MMY-3006 Study

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2090-2090 ◽  
Author(s):  
Michele Cavo ◽  
Monica Galli ◽  
Annalisa Pezzi ◽  
Francesco Di Raimondo ◽  
Claudia Crippa ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Board Of Directors ◽  
Low Risk ◽  
Standards Of Care ◽  
Consolidation Therapy ◽  
Phase 3 ◽  
Advisory Committees ◽  
Cox Regression Analysis

Abstract Over the last years, incorporation of novel agents into autologous stem cell transplantation (ASCT) has improved markedly the outcomes of younger patients with newly diagnosed multiple myeloma (MM). Superior results with experimental treatments vs previous standards of care have been frequently reported after preliminary analyses and need to be confirmed with longer follow up. The randomized phase 3 GIMEMA-MMY-3006 study was designed to compare bortezomib-thalidomide-dexamethasone (VTD) vs thalidomide-dexamethasone (TD) as induction therapy before, and consolidation after, double ASCT. Data from the initial analysis, with a median follow up of 36 months, demonstrated that patients randomized to the VTD arm enjoyed superior complete/near complete response (CR/nCR) rates after both induction and consolidation therapy, and had a significantly longer PFS compared to those prospectively assigned to the TD arm. We performed an updated analysis of the study after a median follow up of 59 months and results are herein reported. A persistent TTP and PFS benefit with incorporation of VTD into ASCT was confirmed. On an intention-to-treat analysis of 236 patients randomized to the VTD arm, median TTP was 62 months and median PFS was 57 months. The median values for 238 patients randomly assigned to the TD arm were 45 months for TTP (HR=0.64, p=0.001) and 42 months for PFS (HR=0.66, p=0.001) (Fig. 1). With the longer follow up of this analysis, an initial divergence between OS curves could be appreciated after 4 years, although the difference was not yet statistically significant at 6 years (75% for VTD vs 69% for TD). Superiority of VTD over TD for TTP and PFS was retained across prespecified subgroups of patients with high risk and low risk disease. In particular, PFS benefit with VTD was seen for patients age >60 years (HR=0.62, p=0.013) and younger than 60 years (HR=0.70, p=0.026), with ISS stage 1 (HR=0.59, p=0.009) and ISS stage 2-3 (HR=0.69, p=0.018), and for those with t(4;14) and/or del(17p) (HR=0.43, p<0.001) and with t(4;14) alone [t(4;14) positivity but lack of del(17p)] (HR=0.41, p=0.001). In comparison with patients with t(4;14) positivity who were randomized to TD, those assigned to the VTD arm had significantly longer PFS (median: 24 vs 53 months, HR=0.41, p=0.0007) (Fig. 2) and a trend towards longer OS (4-year estimates: 66% vs 81%, p=0.052). By the opposite, similar PFS curves were seen for patients in the VTD group regardless of the presence or absence of t(4;14) (Fig. 3). On multivariate Cox regression analysis, randomization to the VTD arm was an independent factor predicting for prolonged PFS (HR=0.64, P=0.001). Additional disease- and treatment-related variables independently affecting PFS included attainment of CR/nCR after both induction (HR=0.64, p=0.010) and consolidation therapy (HR=0.57, p<0.001), β2-m >3.5 mg/L (HR=1.7, p<0.001) and presence of t(4;14) and/or del(17p) (HR=2.0, p<0.001). On multivariate analysis, β2-m, cytogenetic abnormalities and attainment of CR/nCR after consolidation therapy were independently associated with OS. With an updated median follow-up of 49 months from the landmark of starting consolidation therapy, median PFS was 50 months for patients receiving VTD consolidation and 38 months for those treated with TD (HR= 0.69, P=0.015) (Fig. 4). Superior PFS with VTD vs TD consolidation therapy was observed for patients who failed CR/nCR after the second ASCT (HR=0.48, P=0.003) and was retained in both low risk and high risk subgroups. Finally, duration of OS from relapse or progression was similar between the two treatment groups (median, 42 for VTD vs 35 months for TD, p=0.47), even when bortezomib was incorporated into salvage therapy. In conclusion, this updated analysis of the GIMEMA-MMY-3006 study demonstrated: 1) a persistent PFS benefit with VTD vs TD in the overall population, as well as in subgroups of patients with high risk and low risk MM; 2) the ability of VTD, but not of TD, incorporated into double ASCT to overcome the adverse prognosis related to t(4;14); 3) the significant contribution of VTD consolidation to improved outcomes seen for patients randomized to the VTD arm; 4) the lack of more resistant relapse after exposure to VTD as induction and consolidation therapy compared to TD. A longer follow up is required to assess the OS benefit, if any, with VTD plus double ASCT. Disclosures: Cavo: Bristol-Myers Squibb: Consultancy, Honoraria, 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; Millennium: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, 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. Tacchetti:Janssen and Celgene: Honoraria. Zamagni:Celgene: Honoraria; Janssen-Cilag: Honoraria. Caravita:Celgene: Honoraria, Research Funding. Brioli:Celgene: Honoraria.

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