Gene Expression Profiling (GEP) Analysis of Plasma Cells (PC) Obtained From MRI-Defined Focal Lesions (FL) Under CT-Guided Fine-Needle Aspiration Provides Better Risk Stratification in Patients with Multiple Myeloma

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2896-2896
Author(s):  
Qing Zhang ◽  
Pingping Qu ◽  
Emily Hansen ◽  
Christoph Heuck ◽  
Saad Usmani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Risk Score ◽  
Expression Profiling ◽  
Research Funding ◽  
Risk Scores ◽  
Classification Models ◽  
Focal Lesions ◽  
Molecular Subgroup ◽  
Subgroup Classification

Abstract Abstract 2896 Background: Focal lesions (FL) are a well-recognized consequence of active multiple myeloma (MM), and distinguish it from its antecedent monoclonal gammopathy of undetermined significance (MGUS). We have recently reported on the superior performance of gene expression profiling (GEP) based on random trephine bone marrow aspirate sampling (RS) in distinguishing between 85% of patients with low-risk (LO) MM and 15% with high-risk (HI) MM compared to conventional prognostic variables. MM occasionally presents as macro-focal disease, in which cases RS may be inconclusive because of paucity of malignant cells in the sample. Here we report the comparison of GEP data from paired FL and RS samples from 106 untreated patients. Methods: We identified 106 newly diagnosed patients with paired samples, who were treated on Total Therapy (TT) protocols (3 in TT2, 19 in TT3, 75 in TT4, and 9 in TT5) in our multiple myeloma database. GEP risk scores, molecular subgroup classifications, overall survival (OS), and event-free survival (EFS) were compared and tested with the RS-derived 70-gene risk prediction and molecular subgroup classification models. Results: GEP defined molecular subgroups were correlated in 90 of 106 patients (85%). Looking at GEP-defined risk designation, we found a high degree of correlation between RS and FL samples with 95 of 106 samples showing the same designation (90%). For the 11 patients with divergent GEP designations, 8 (73%) were located at the boundary of the RS GEP risk score cutoff of +0.66 (range +0.43 to +0.84). For these risk designation-divergent patients, FL- but not RS-defined risk determined clinical outcome. Conclusion: Both the 70-gene risk prediction and molecular subgroup classification models can be used in FL-GEP samples. But, more importantly, for patients with RS-GEP risk score close to cutoff boundary (about 14% in our current data sets), FL-GEP provides better risk stratification, suggesting that the FL signal more adequately reflects to disease biology, progression and treatment response in MM. We therefore recommend that, for patients with borderline RS-based GEP risk scores, FL-GEP be used for staging and prognosis assessment in myeloma. Studies are in progress to determine, among multiple FL samples from the same patient, the variability in risk score compared to multiple RS samples. Disclosures: Shaughnessy: Myeloma Health, Celgene, Genzyme, Novartis: Consultancy, Employment, Equity Ownership, Honoraria, Patents & Royalties. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; IMF: Consultancy, Honoraria; MMRF: Consultancy; Millennium: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy; Novartis: Research Funding; NCI: Research Funding; Johnson & Johnson: Research Funding; Centocor: Research Funding; Onyx: Research Funding; Icon: Research Funding.

Gene Expression Profiling-Based Risk Stratification Scores In Multiple Myeloma Can Be Highly Sensitive Towards Sample Preparation

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1865-1865 ◽  
Author(s):  
Qing Zhang ◽  
Christoph Heuck ◽  
Pingping Qu ◽  
Saad Z Usmani ◽  
Ryan Williams ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Expression Profiling ◽  
Research Funding ◽  
Prognostic Scores ◽  
Prognostic Models ◽  
Risk Scores ◽  
P Value ◽  
Batch Effects ◽  
Paired T Test

Abstract Background Gene expression profiling (GEP) via microarray analysis enables the measurement of expression levels for tens of thousands of genes in a single experiment, and it has been widely used in clinical practice for cancer classification, risk stratification, and treatment selection. However, results of GEP-based clinical diagnostic/prognostic tests can be highly affected by batch effects when clinical samples are processed differently (e.g. on different days, by different technicians, or using different sample protocols). Yet, this problem is rarely discussed in the literature. Understanding the role of batch effects on GEP-based conclusions is vital because GEP-based-risk treatment assignment has been used for personalized treatment to improve patients' survival: patients with low risk and favorable clinical and biological features can be treated with a less intensive, lower toxicity treatment, while patients with high risk and unfavorable features can be treated with a more aggressive, potentially more toxic therapeutic approach. Here, we investigate how sample processing discrepancies influence various GEP-based prognostic models in multiple myeloma (MM) and how to adjust for such effects during data analysis. Methods/Results In 2009, Affymetrix discontinued their One-Cycle and Two-Cycle Target Labeling and Control Reagents (hereon referred to as the 'old' kit) and replaced it with a 3' IVT Express Kit (hereon referred to as the 'new' kit). To examine the impact of the replacement kit on GEP results, we set out to process eleven CD138-enriched patient plasma cell samples using both the new and old kits side-by-side before hybridizing them separately to the Affymetrix HG-U133 Plus 2.0 arrays. Various GEP-based MM prognostic scores, including UAMS-70, UAMS-80, UAMS-17, EMC-92, IFM-15, MRC-IX-6, and MILLENNIUM-100, were calculated and compared between the matched GEP pairs with either MAS5 or RMA normalization, with and without batch effect adjustment by ComBat (Combating Batch Effects When Combining Batches of Gene Expression Microarray Data). Both the UAMS-70 and UAMS-80 scores are based on log2 ratios between unfavorable and favorable genes regarding survival, which are self-normalized. However, with MAS5 alone, the UAMS-70 score was similar between the two kits (p-value=0.37 from paired t-test) but not for UAMS-80 score, which was significantly higher under the new kit (p-value<0.001 from paired t-test, Figure 1). Furthermore, besides UAMS-17, the score values from the EMC-92, IFM-15, MRC-IX-6, and MILLENNIUM-100 models without batch effect adjustment were all affected by the kit issue. After ComBat adjustment, variation caused by batch effects markedly reduced, and as a result, correlation increased between the prognostic scores of the two different kits. For most GEP-based MM prognostic scores, kit effect was minimized by RMA plus ComBat correction, which resulted in similar risk scores between the two kits. Conclusion For GEP-based prognostic models, it is important to check for possible batch effects which may be the end result of various causes, such as differences in sample preparation and processing protocols, like the new kit/old kit issue discussed here. We found that even for self-normalized prognostic signatures, risk scores can still sometimes be significantly different because of batch effects. So, it is essential to preprocess GEP raw data carefully, minimizing variance caused by batch effects, and adjusting any GEP-based diagnostic result accordingly, e.g. cutoff values in risk-assessment models. Disclosures: Usmani: Celgene: Consultancy, Research Funding, Speakers Bureau; Onyx: Research Funding, Speakers Bureau. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; Myeloma Health, LLC: Patents & Royalties.

Intra-Tumoral Heterogeneity In Multiple Myeloma Gleaned From Gene Expression Profiling Analysis Of Plasma Cells and Bone Marrow Biopsies: Comparison Of Random Bone Marrow Samples and CT-Guided Fine Needle Aspirations Of MRI-Defined Focal Lesions

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3123-3123
Author(s):  
Bart Barlogie ◽  
Emily Hansen ◽  
Sarah Waheed ◽  
Jameel Muzaffar ◽  
Monica Grazziutti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
P Value ◽  
Focal Lesions ◽  
Molecular Subgroup

Abstract Intra-tumoral heterogeneity (ITH) is increasingly viewed as the Achilles heel of treatment failure in malignant disease including multiple myeloma (MM). Most MM patients harbor focal lesions (FL) that are recognized on MRI long before bone destruction is detectable by conventional X-ray examination. Serial MRI examinations show that eventually 60% of patients will achieve resolution of FL (MRI-CR). However, this will lag behind the onset of a clinical CR by 18 to 24 months, thus attesting to the biological differences between FL and diffuse MM growth patterns. Consequently, we performed concurrent gene expression profiling (GEP) analyses of plasma cells (PC) from both random bone marrow (RBM) via iliac crest and FL. Our primary aims were to first compare the molecular profiles of FL vs. RBM, second to determine if ITH existed (as defined molecular subgroup and risk), and finally to investigate if the bone marrow micro-environment (ME) contained a biologically interesting signature. A total of 176 patients were available for this study with a breakdown of: TT3 (n=23), TT4 for low-risk (n=131) and TT5 for high-risk MM (n=22). Regarding the molecular analyses of PCs, GEP-based risk (GEP-70, GEP-5) and molecular subgroup correspondence were examined for commonalties and differences between RBM and FL. A “filtering” approach for ME genes was also developed and bone marrow biopsy (BMBx) GEP data derived from this method is under analysis. PC risk correspondence between FL and RBM was 86% for GEP70 and 88% for the GEP5 model. Additionally, 82% had a molecular subgroup concordance, however, they did differ among subgroups (p=0.020) by Fisher's Exact Test. A lower concordance was noted in the CD2, LB, and PR subgroups (67%, 69%, 73%, respectively). GEP70 and GEP5 risk concordance between RBM and FL samples by molecular subgroup was also examined. The overall correlation coefficients were 0.619 (GEP70) and 0.597 (GEP5). The best correspondence was noted for CD1, MF and PR subgroups especially for the GEP5 model. HY, LB and MS showed intermediate correlations, while CD2 fared worst with values of only 0.322 for GEP70 and 0.267 for GEP5 model. Figure 1 portrays these data in more detail for the GEP70 and GEP5 models. Good correlations were noted between RBM and FL based risk scores in case of molecular subgroup concordance (left panels) in both GEP5 and GEP70 risk models, whereas considerable scatter existed in case of subgroup discordance (right panels). The clinical implications in TT4 regarding RBM and FL derived risk and molecular subgroup information, viewed in the context of standard prognostic baseline variables are portrayed in Table 1. High B2M levels at both cut-points imparted inferior OS and PFS as did low hemoglobin. Although present in 42% of patients, cytogenetic abnormalities (CA) did not affect outcomes. FL-based GEP5-defined high-risk designation conferred poor OS and PFS. B2M>5.5mg/L and FL-derived GEP5 high-risk MM, pertaining to 29% and 11% of patients, survived the multivariate model for both OS and PFS. Next, in examining PC-GEP differences among RBM and FL sites, 199 gene probes were identified with a false discovery rate (FDR) of 1x10-6. Additionally, 55 of the 199 belong to four molecular networks of inter related genes associated with: lipid metabolism, cellular movement, growth and proliferation, and cell-to-cell interactions. Multivariate analysis identified the GEP5 high risk designation of focal lesion PCs to be significantly prognostic with a HR=3.73 (p=0.023).Table 1Cox regression analysis of variables linked to overall and progression-free survival in TT4.Overall SurvivalProgression-Free SurvivalVariablen/N (%)HR (95% CI)P-valueHR (95% CI)P-valueMultivariateB2M > 5.5 mg/L38/130 (29%)3.71 (1.49, 9.22)0.0053.84 (1.58, 9.31)0.003FL GEP5 High Risk14/130 (11%)3.68 (1.19, 11.41)0.0243.73 (1.20, 11.62)0.023HR- Hazard Ratio, 95% CI- 95% Confidence Interval, P-value from Wald Chi-Square Test in Cox RegressionNS2- Multivariate results not statistically significant at 0.05 level. All univariate p-values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.A multivariate p-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. Disclosures: No relevant conflicts of interest to declare.

SWOG S0120 Observational Trial for MGUS and Asymptomatic Multiple Myeloma (AMM): Imaging Predictors of Progression for Patients Treated At UAMS,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3955-3955
Author(s):  
Christoph Heuck ◽  
Rachael Sexton ◽  
Madhav Dhodapkar ◽  
Qing Zhang ◽  
Saad Usmani ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Research Funding ◽  
Cox Regression ◽  
Time Dependent ◽  
M Protein ◽  
Risk Scores ◽  
Imaging Studies ◽  
Focal Lesions ◽  
Pet Ct

Abstract Abstract 3955 Background: MGUS counts for the majority of monoclonal gammopathies and can be found in approximately 3% of adults older than 50 years. MGUS progresses to active Multiple Myeloma (MM) at a rate of 1–2% per year, thus imparting an average risk of 25% for progression (PRO) over a lifetime once diagnosed. Unfortunately no single laboratory, molecular or imaging variable can reliably predict PRO. S0120 accrued 363 patients at 69 sites across the US between January 1, 2004 and November 1, 2011, of whom 166 had MGUS and 190 AMM, defined according to IMWG criteria, on whom laboratory, gene expression and imaging studies were collected in a prospective fashion. Here we report the results of imaging studies as predictors of progression. Methods: 262 patients with evaluable follow-up were enrolled at the University of Arkansas for Medical Sciences (UAMS) site. MRI and PET-CT studies were performed at baseline and serially thereafter until PRO to symptomatic MM defined by standard variables of M-protein, bone marrow findings and CRAB criteria, according to protocol. Lab studies were performed at three months, six months and one year after registration, then every 12 months for a total of 5 years from registration as well as within 14 days of decision to discontinue observation or within 14 days of progression. MRI parameters included the number of focal lesions (FL) recognized by short TI inversion recovery (STIR) analysis of the axial bone marrow along with an account of bone marrow background intensity compared to adjacent muscles (hypo-, iso-, hyper-intense). PET-CT parameters included number of FDG-avid focal lesions (PET-FL), SUVmax of PET-FL, presence of extra-medullary disease (EMD) as well as the FDG avidity score at L5 (SUV-L5). Evaluable baseline MRI and PET studies were available for 235 and 224 patients, respectively. Results: In the 262 eligible patients enrolled and followed at UAMS, the two subgroups of MGUS and AMM differed by definition in M-protein and bone marrow plasmacytosis; in addition, IgA subclass and Hyperdiploidy molecular subgroup were overrepresented in the AMM group. Patients in the AMM group also had higher risk scores defined by the GEP 70-gene risk model (GEP70). At 24 months from study entry, 18.8% of all patients had progressed to MM (25.6% of AMM patients and 8.2% of MGUS patients) and 11.5% had begun MM therapy (15.8% of AMM patients and 4.5% of MGUS patients). Univariate Cox regression strongly indicated that age ≥ 65, serum albumin <3.5g/dL, B2M >+3.5mg/L, detection of any cytogenetic abnormalities (CA), and suppression of uninvolved light chains were adversely associated with time to PRO. The AMM-constituting features, bone marrow plasmacytosis >10%, M-protein >30g/L, and abnormal K/L ratio also conferred greater hazard of PRO. Risk scores > −0.26 and >1.5 for GEP70 and GEP80, respectively, as well as detection of focal lesions by MRI at baseline carried an elevated HR for PRO. A multivariate Cox regression showed only elevated M-protein, abnormal K/L ratio and GEP70 risk scores > =0.26 to be strongly associated with time to PRO. In the context of this MV model, disease subtype (AMM v MGUS) was insignificant. Inclusion of development of MRI-FL or and PET-FL as time-dependent variables showed that they were associated with time to PRO with HRs of 27.12 and 32.18 respectively. Abnormal K/L ratio and elevated M-protein were lost in this MV model. Analyzing variables linked to initiation of MM therapy, abnormal K/L ratio, elevated BM plasmacytosis, elevated M-protein, GEP70 risk scores >-0.26 as well as detection of MRI-FL at baseline (≥1 FL: HR=4.90; ≥3FL: HR=10.00) were univariately significant. On multivariate analysis, abnormal K/L ratio, elevated M-protein and GEP70 risk scores > – 0.26 were associated with time to treatment for MM. Inclusion of development of MRI-FL or PET-FL as a time dependent variable were associated with time to treatment with HRs of 29.12 and 36.50 respectively. Conclusion: To our knowledge, this is the first comprehensive effort that has used available imaging modalities along with established laboratory and pathology investigations in an attempt to distinguish features predictive of PRO from MGUS to active MM. In addition to the established “high-risk” MGUS/AMM features, we found that presence of MRI-FL at baseline, presence of CA and GEP70 scores >-0.26 carry a higher risk of PRO. Disclosures: Shaughnessy: Myeloma Health, Celgene, Genzyme, Novartis: Consultancy, Employment, Equity Ownership, Honoraria, Patents & Royalties. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; IMF: Consultancy, Honoraria; MMRF: Consultancy; Millennium: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy; Novartis: Research Funding; NCI: Research Funding; Johnson & Johnson: Research Funding; Centocor: Research Funding; Onyx: Research Funding; Icon: 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.

Gene Expression Profiling (GEP) of Whole Bone Marrow Biopsies in Complete Remission (BMB-CR) of Multiple Myeloma (MM) Patients Treated On Total Therapy Protocols – Normalization of GEP Signature in Comparison with Normal Donor BMB (BMB-NL) and Consequences for Progression-Free Survival (PFS)

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 198-198
Author(s):  
Christoph Heuck ◽  
Pingping Qu ◽  
Sarah Waheed ◽  
Saad Z Usmani ◽  
Frits van Rhee ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Research Funding ◽  
Time Course ◽  
Plasma Cells ◽  
Progression Free Survival ◽  
Risk Scores ◽  
Normal Donor ◽  
Gene Score ◽  
Molecular Subgroup ◽  
Bone Marrow Biopsies

Abstract Abstract 198 GEP of purified plasma cells (PC) provides superb risk discrimination for PFS and overall survival (OS). It is well established that MM engages the BM micro-environment (ME) and subjugates it toward its survival and progression. International Myeloma Working Group- and flow-based CR have been linked to PFS and OS, but do not provide guidance vis-à-vis the duration of post-transplant consolidation and maintenance therapy. We reasoned that ME may still be altered in flow-CR as it may remain MM-impregnated long after CR is established; conversely, ME normalization may happen much earlier. In a first step, 20 BMB-NL were compared with 331 untreated patients with MM (BMB-MM), leading to the discovery of a 65 gene score (GEP-65), which was then applied to 83 patients with MGUS/asymptomatic multiple myeloma (AMM) and 88 MM patients in CR. GEP-65 risk scores were similar in BMB-CR and BMB-MGUS-AMM contrasting with significantly lower values for BMB-MM. Next we examined PFS for CR patients according to whether BMB-CR signature normalized (BMB-CR-NL, n=14) or failed to normalize (BMB-CR-MM, n=74). A borderline significantly superior PFS was observed in the former group suffering 2 events versus 31 in the remainder (p=0.08). We conclude that (1) BMB-MM differs markedly from BMB-NL, (2) BMB-CR resembles BMB-MGUS-AMM, and (3) BMB-CR-NL can be distinguished from BMB-CR-MM resulting in PFS differences. Availability of several hundred BMB-CR samples and nearly 100 BMB-NL will have been evaluated at the time of the meeting to address the following: (a) can these results be validated, (b) what is the time course of BMB-CR-NL and pattern to BMB-CR-MM and BMB-MM, (c) are there MM GEP molecular subgroup- and risk-dependent differences. Disclosures: Dhodapkar: Celgene: Research Funding; KHK: Research Funding.

scholarly journals Flow Cytometry Defined Cytoplasmic Immunoglobulin Index Is a Major Prognostic Factor for Progression of Asymptomatic Monoclonal Gammopathies to Clinical Multiple Myeloma

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2079-2079
Author(s):  
Xenofon Papanikolaou ◽  
Adam Rosenthal ◽  
Rashid Z Khan ◽  
Joshua Epstein ◽  
Christoph Heuck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Flow Cytometry ◽  
Board Of Directors ◽  
Expression Profiling ◽  
Research Funding ◽  
Plasma Cells ◽  
Time To Progression ◽  
Advisory Committees ◽  
Discrimination Method

Abstract Progression of Asymptomatic Monoclonal Gammopathies to Myeloma requiring treatment -Clinical Multiple Myeloma (CMM)- is an important issue in current clinical investigation toward secondary prevention, i.e. treating high-risk AMG. Several predictive models have been published, including one that incorporated gene expression profiling (GEP) of plasma cells (PC) where a GEP70 score ≥0.26 was linked to higher AMG-CMM progression in a multivariate model (Dhodapkar, Blood 2014). We have applied 2-parameter flow cytometry of DNA and cytoplasmic immunoglobulin (FDC) of bone marrow aspirates as part of baseline staging of all patients with plasma cell dyscrasia. A modification introduced in August 2006 on the doublet discrimination method increased accuracy and reproducibility of FDC results considerably and allowed for the detection of a plasma cell population with a low CI<2.8 as an independent predictor of PFS and OS in newly studies of newly diagnosed CMM treated with TT3b even in the context of GEP70 risk (Papanikolaou, ASH 2012). Realizing the importance of the FDC derived CI in CMM, we assessed whether FDC data from the observational AMG protocol S0120 could identify a population of patients with a high risk for progression to CMM. Out of 252 eligible patients, 130 had an FDC sample taken within 30 days prior to enrollment date and analyzed under the modified doublet discrimination method. FDC identified a light chain restricted population (LCR) in 121 patients. The number of distinct DNA stem lines in the flow cytometry assay, the percentage of LCR plasma cells (LCR%), their ploidy status and respective CI, were evaluated alone and in relation with clinical, laboratory and genetic parameters known to affect progression of AMG to CMM. For continuous FDC variables, the running log-rank statistics were used to determine the optimal cut-off points. In univariate analysis, the existence of at least two distinct DNA stem lines (HR: 3.3, P=0.002), a FDC LCR population of plasma cells >17% (HR: 6.76, P<0.001) and the presence of a LCR population with a CI<3.6 (HR: 6.42, P<0.001) (Figure 1) were statistically significant along with other clinical factors of established prognostic value. A M component> 3g/dL (HR: 12.5, P<0.001), an involved light chain level >10mg/dL (HR: 2.8, P=0.019), a GEP70 score ≥0.26 (HR: 8.22, P<0.001) and the presence of a LCR population with a CI < 3.6 (HR:4.15, P=0.002) survived in the multivariate analysis. To further confirm importance of a low CI to CMM progression, we compared the CI of S0120 with the TT3b patients. The CI was significantly lower in TT3b cases regardless of when the comparison was made for all patients or for strictly aneuploidy cases (P<0.0001) to exclude the possibility that the CI difference reflects the lower percentage of normal plasma cells found in CMM. In conclusion, FDC is an easily applicable, fast and low-cost test, which offers valuable prognostic information even in the era of gene expression profiling and other cytogenetic testing strategies. The identification of a low CI as a risk factor suggests that, progression of AMG to CMM is characterized by the emergence of a low immunoglobulin producing myeloma cell population. Figure 1: Time to progression requiring myeloma therapy by CI, S0120 Figure 1:. Time to progression requiring myeloma therapy by CI, S0120 Disclosures Heuck: Celgene: Honoraria; Foundation Medicine: Honoraria; Millennium: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Dhodapkar:Celgene: Research Funding. Zangari:Norvartis: Membership on an entity's Board of Directors or advisory committees; Onyx: Research Funding; Millennium: Research Funding. Morgan:Celgene Corp: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Myeloma UK: Membership on an entity's Board of Directors or advisory committees; International Myeloma Foundation: Membership on an entity's Board of Directors or advisory committees; The Binding Site: Membership on an entity's Board of Directors or advisory committees; MMRF: Membership on an entity's Board of Directors or advisory committees.

Proteomic Characterization of An Isogenic Multiple Myeloma Cell Line Model of Bortezomib Resistance

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1820-1820
Author(s):  
*Catriona A. Hayes ◽  
*Paul Dowling ◽  
Joseph Negri ◽  
Michael Henry ◽  
Leutz Buon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Line ◽  
Expression Profiling ◽  
Research Funding ◽  
Advisory Board ◽  
Differentially Expressed ◽  
Transcript Levels ◽  
Cell Line Model ◽  
Ht 29

Abstract Abstract 1820 Proteasome inhibitors such as Bortezomib (Bort) represent a key drug class for the therapeutic management of multiple myeloma (MM). However, MM patients, even those who initially achieve complete clinical and biochemical remission with bortezomib-based regimens eventually relapse, and bortezomib-refractory disease is associated with short overall survival. Identifying the molecular basis of resistance to bortezomib is therefore crucial for the rational development of novel therapies to hopefully improve clinical outcome for advanced MM. To address this question, we generated an isogenic cell line model of bortezomib resistance by successive rounds of in vitro exposure of bortezomib-sensitive MM.1R cells to progressively increasing bortezomib concentrations. Serial dose-response analyses confirmed the generation of several clones with variable reduction in bortezomib-sensitivity (IC50 range 40–80nM vs. <10nM for parental MM.1R). The proteomic profile of one of these clones, provisionally termed MM.1VDR, was compared to its isogenic bortezomib-sensitive lines MM.1S and MM.1R, using Liquid Chromatography-Mass Spectrometry (Orbitrap XL). Fold change, Mascot scores (as a measure of confidence for the identity of a given protein) and ANOVA scores for differentially expressed proteins were determined using the Progenesis LC-MS software. 386 proteins were determined to be differentially expressed, of which 154 demonstrated a 2–32 fold change, with p values <0.05. We reasoned that proteins or transcripts differentially expressed in multiple isogenic bortezomib-resistance models may be implicated in molecular mechanism(s) of this resistance. We therefore cross-referenced the list of proteins differentially expressed in MM.1VDR cells with a reanalysis of publically available gene expression profiling datasets of other isogenic models of bortezomib-sensitivity vs. resistance. These included the HT-29 colon cancer cell line (GSE29713) and the mantle cell lymphoma (MCL) lines HBL2-BR and JEKO-BR (GSE20915). In this integrative molecular profiling analysis, we identified no protein/transcript that was concordantly up- or down-regulated in all 4 isogenic models studied. However, we identified that eleven proteins differentially expressed in MM.1VDR cells had concordant differential expression of their transcripts in Bort-resistant HT-29, and either HBL2-BR or JEKO-BR MCL cells. Upregulated markers included FH, DDX46, PSMB4, AKR1A1, EIF3B, BLVRA, PSMB2, RPA3, HPRT1 and PSME3; while PPFIBP2 was down-regulated. These differentially expressed molecules are known to be involved in proteasome structure and function (PSMB4, PSMB2, PSME3, DDX46), mRNA binding and translation initiation (EIF3B/EIF3S9), DNA repair (RPA3), as well as drug metabolism and chemo-resistance (AKR1A1), while several are differentially expressed in diverse neoplasias vs. normal cells (e.g. PPFIB2P is differentially expressed in endometrial cancer, Colas et al, Int J Cancer 2011). Importantly, in gene expression profiling studies in CD138+ tumor cells from selected bortezomib-treated MM patients (treated as part of the APEX and/or SUMMIT clinical trials), high transcript levels for FH, EIF3B, PSMB4 or AKR1A1 or low transcript levels for PPFIBP2 correlate with shorter overall survival (p<0.05, log-rank tests), suggesting that these molecules may have a functional link with the mechanisms responsible for emergence of clinical resistance to bortezomib. Our data, taken together, therefore indicate that the mechanisms of bortezomib resistance are likely multifactorial and potentially tumor-type/specificity-dependent. However, through integrative proteogenomic analyses, we identified functionally and potentially clinically relevant candidate markers of bortezomib resistance. These markers may represent novel targets in efforts to overcome bortezomib-resistance and further improve outcome of MM patients. *Authors contributed equally. Disclosures: Hayes: Pfizer: Research Funding; Amgen: Research Funding. van de Donk:Celgene: Research Funding. Richardson:Johnson & Johnson: Advisory Board; Millennium: Advisory Board; Celgene: Advisory Board; Bristol-Myers Squibb: Advisory Board; Novartis: Advisory Board. Anderson:Millennium: Consultancy, Research Funding. Mitsiades:Millennium Pharmaceuticals: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Novartis Pharmaceuticals: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Merck &Co.: Consultancy, Honoraria; Kosan Pharmaceuticals: Consultancy, Honoraria; Pharmion: Consultancy, Honoraria; Centocor: Consultancy, Honoraria; Amnis Therapeutics: Consultancy, Honoraria; PharmaMar.: Licensinig royalties; OSI Pharmaceuticals: Research Funding; Amgen Pharmaceuticals: Research Funding; AVEO Pharma: Research Funding; Sunesis: Research Funding; Gloucester Pharmaceuticals: Research Funding; Genzyme: Research Funding; Johnson & Johnson: Research Funding.

scholarly journals The Clinical Impact of Macrofocal Disease in Multiple Myeloma Differs Between Presentation and Relapse

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4431-4431 ◽  
Author(s):  
Leo Rasche ◽  
Amy Buros ◽  
Niels Weinhold ◽  
Caleb K. Stein ◽  
James E McDonald ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Research Funding ◽  
Low Risk ◽  
Disease Stage ◽  
First Line ◽  
Focal Lesions ◽  
First Line Therapy ◽  
Line Therapy

Abstract Introduction Functional imaging of Multiple Myeloma (MM) is redefining our knowledge of disease patterns. A pattern, termed macrofocal MM (macro MM), is defined by the presence of focal lesions and the absence of significant intervening bone marrow (BM) infiltration. At presentation, macro MM constitutes a distinct disease entity likely being associated with a favorable prognosis, although current evidence to support this is limited. Following first-line therapy, macrofocal patterns of disease emerge also in patients that initially presented with classical MM. In these patients the systemic BM involvement disappears in follow up examinations during treatment whereas focal lesions persist. In a third scenario, macrofocal patterns occur at overt relapse representing a patchy type of MM progression (Figure 1). The prognostic impact of a macrofocal pattern at these various disease stages is largely unknown. Therefore, we analyzed the clinical outcome and biological features of macro MM at different treatment stages. Patients and Methods 279 patients met the criteria of macro MM. Of those, 56 were at initial presentation, 48 at restaging following first line therapy, and 175 at relapse. Generally, macrofocal lesions were present in both positron emission tomography and magnetic resonance imaging. All first-line patients were treated with multi-agent induction therapy, autologous stem cell transplantation and received maintenance within prospective trials. Outcome results were compared to a set of cases with classical MM matched for age, gene-expression based (GEP) risk group, and treatment protocol. Results Macro MM at presentation is rare, constituting 6% of patients in the time period examined. The vast majority showed GEP-based low risk (94%). Age, Ig-type, and sex were not significantly different between macro MM and classical MM. With a median follow up of 8.6 years, only 10 of the macro MM patients relapsed. Compared to a matched-pair MM group, progression-free survival (PFS) and overall survival (OS) were significantly better in the macro MM group (P= 0.01 and 0.04 for PFS and OS, respectively). Thus macro MM at presentation constitutes a low risk form of MM. Focusing on the 10 macro MM cases who relapsed, no specific risk profile could be identified except >26 focal lesions on MRI was associated with a shorter PFS (P=0.04) but not with OS. Of note, although focal lesions frequently responded slowly, the time to response was not associated with outcome. To elucidate whether there are biological differences between MM cells in focal lesions and at differentially involved BM sites, we analyzed a set of 16 patients with paired samples from macrofocal lesions and iliac crest BM aspirates. No difference in a GEP based proliferation index was seen between the two sites. After correction for multiple testing we did not observe gene expression differences between them. A candidate gene study including a set of 27, myeloma relevant, adhesion molecules also did not reveal expression differences. In contrast to the situation at presentation, macrofocal patterns at restaging during initial therapy showed a 70% cumulative 24 months relapse incidence. The outcome of these cases was significantly worse in comparison to matched controls (P=0.02 and 0.02 for PFS and OS, respectively). Of note, all patients with macro MM showed an objective response at the time of imaging with 9 of 46 cases meeting the IMWG criteria for CR. Performing a similar analysis of patients with macro MM at relapse showed that 25% of patients presented with that pattern; a surprisingly high proportion. Extramedullary involvement was common (41%). Of note, 36% of patients repeatedly showed macrofocal patterns at subsequent relapses. PFS and OS at 2 years from macrofocal relapse were 24% and 39%, respectively. A matched group OS comparison was not possible since number of relapses and treatments were too different among the patients. Conclusions Macro MM at presentation seems to be an early stage of MM with an excellent prognosis. In contrast, a macrofocal pattern at restaging is associated with poor prognosis and early relapse. At this disease stage residual focal lesions may represent drug resistant clones. At overt relapse a macrofocal pattern was frequently seen, highlighting the need to integrate advanced imaging tools into the standard work up and indicating an important confounder of standard minimal residual disease diagnostics in MM. Disclosures Barlogie: Signal Genetics: Patents & Royalties. Davies:Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding; Univ of AR for Medical Sciences: Employment.

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