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.

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.

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.

A 13-Glycosylation Gene Signature in Multiple Myeloma Can Predicts Survival and Identifies Candidates for Targeted Therapy (GiMM13)

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4423-4423 ◽  
Author(s):  
Caoilfhionn Connolly ◽  
Alokkumar Jha ◽  
Alessandro Natoni ◽  
Michael E O'Dwyer
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Research Funding ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Rank Test ◽  
P Value ◽  
Log Rank Test ◽  
Novel Approach

Abstract Introduction Advances in genomics have highlighted the potential for individualized prognostication and therapy in multiple myeloma (MM). Previously developed gene expression signatures have identified patients with high risk (Kuiper et al, Blood 2016) however, they provide few insights into underlying disease biology thereby limiting their use in informing treatment decisions. Glycosylation is deregulated in MM (Glavey et al), and potential consequences include altered cell adhesion, signaling, immune evasion and drug resistance. In this study we have utilized RNA sequencing data from the IA7 CoMMpass cohort to characterize the expression profile of genes involved in glycosylation. This represents a novel approach to identify a distinct molecular pathway related to outcome, which is potentially actionable. Methods A pathway based approach was adopted to evaluate genes implicated in glycosylation, including the generation of selectin ligands. A literature review and KEGG pathway analysis of pathways relating to O-glycans, N-glycans, sialic acid metabolism, glycolipid synthesis and metabolism was completed. RNA Cufflinks-gene level FPKM expression of 458 patients enrolled in the IA7 cohort of the Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) were analysed as derivation cohort. We developed expression cut-offs using a novel approach of adjusted existing linear regression model to define the gene expression cut-off by applying 3rd Quartile data (q1+q2/2-qmin). The analysis of overall survival (OS) was completed using adjusted 'kpas' R-package according to our cut-off model. Association between individual transcripts and OS was analyzed with log-rank test. Genes with p-value <0.2 were used in subsequent prioritization analysis. This cut-off methodology was employed to define the nearest neighbor for a gene for Gene Set Enrichment Analysis (GSEA). As far as 4th neighbor above and below the cut off was used to have centrally driven gene selection method for prioritization. The gene signature was validated in GSE2658 (Shaughnessy et al) dataset. Results Initial analysis yielded 184 prospective genes. 147 were significant on univariate analysis. Following further prioritization of these genes, we identified thirteen genes that had significant impact upon outcomes (GiMM13). Figure 1 reveals that GiMM13 signature has a significant correlation with inferior OS (HR 4.66 p-value 0.022). The prognostic impact of stratifying GiMM13 positive (High risk) or GiMM13 negative (Low risk) by ISS stage was evaluated. In Table 1. Kaplan Meier estimates generated for GiMM13 (High) or GiMM13 (Low) stratified by ISS are compared statistically using the log rank test. The prognostic ability of GiMM13 to synthesize distinct subgroups relative to each ISS stage is shown in Figure 2. ISS1-Low is the the lowest risk group with best prognosis. Hazard ratios relative to the ISS1-Low group were 1.8, p-value 0.029 (ISS2-Low), 2.1, p-value 0.031 (ISS3-Low), 4.3, p-value 0.04 (ISS1-HR), 5.9, p-value 0.039 (ISS2-HR) and 3.1, p-value 0.001 (ISS3-HR). The GiMM13 signature enhances the prognostic ability of ISS to identify patients with inferior or superior outcomes respectively. Conclusion While the therapeutic armamentarium for MM has expanded considerably, the significant molecular heterogeneity in the disease still poses a significant challenge. Our data suggests aberrant transcription of glycosylation genes, involved predominantly in selectin ligand synthesis, is associated with inferior survival outcomes and may help identify patients likely to benefit from treatment with agents targeting aberrant glycosylation, e.g. E-selectin inhibitor. Consistent with recent findings in chemoresistant minimal residual disease (MRD) (Paiva et al, Blood 2016), it would appear that O-glycosylation, rather than N-glycosylation is most significantly implicated in this biological processes conferring inferior outcomes. In conclusion, using a novel pathway-based approach to identify a 13-gene signature (GiMM13), we have developed a robust tool that can refine patient prognosis and inform clinical decision-making. Acknowledgment These data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiatives (https://research.themmrf.org and www.themmrf.org). Disclosures O'Dwyer: Glycomimetics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding.

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.

Validation of two gene-expression risk scores in a large colon cancer cohort and contribution to an improved prognostic method.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3509-3509 ◽  
Author(s):  
Arnaud Roth ◽  
Antonio Fabio Di Narzo ◽  
Sabine Tejpar ◽  
Fred Bosman ◽  
Vlad Calin Popovici ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Cox Regression ◽  
Tnm Staging ◽  
Stage Ii ◽  
Stage Iii ◽  
Prognostic Models ◽  
Risk Scores ◽  
P Value ◽  
Prognostic Information

3509 Background: Prognosis prediction for resected primary colon cancer is currently based on the tumor, nodes, metastasis (TNM) staging system. Gene expression based risk scores have been proposed, but need to be validated and integrated with clinical and TNM variables. We performed an independent assessment of the individual recurrence signatures developed for commercial use by Veridex and Genomic Health. Methods: The Veridex (aVDS) and Genomic Health (aGHS) risk scores were applied to existing gene expression data of 580 stage III and 108 stage II tumors from the PETACC-3 trial. Association of the scores with relapse-free (RFS) and overall survival (OS) of the patients was assessed singularly, and in a combination with TNM and other clinico-pathological variables, by univariate and multivariate Cox regression models and logrank methods. Results: Both risk scores were significantly associated with RFS in univariate and multivariate models (Table) for the stage III cohort. The scores contributed different and additive prognostic information, and reached highest effect sizes (approximate p-value 0.001 and HR per interquartile range 1.4 each) in a combined model (Table) that includes both scores as well as T-stage, N-stage and MSI status as significant factors. Analysis in the stage II cohort gave similar effect estimates. Conclusions: This study confirms in an independent colon cancer patient cohort that gene expression based risk scores improve current prognostic models. The best prognostic model was obtained by using clinico-pathological variables and both gene-expression risk-scores. [Table: see text]

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.

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
Author(s):  
Annemiek Broyl ◽  
Dirk Hose ◽  
Henk Lokhorst ◽  
Yvonne de Knegt ◽  
Justine Peeters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Medical Science ◽  
Protein Tyrosine Phosphatases ◽  
Molecular Classification ◽  
Newly Diagnosed ◽  
Expression Of Genes

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

scholarly journals Integrated Analysis of Global DNA Methylation and Transcription Reveals a Leukemia Subtype with Extreme Hypermethylation Associated with Silencing of Regulators of Differentiation

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2608-2608
Author(s):  
Claudia Gebhard ◽  
Roger Mulet-Lazaro ◽  
Lucia Schwarzfischer ◽  
Dagmar Glatz ◽  
Margit Nuetzel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Promoter Methylation ◽  
Research Funding ◽  
Promoter Hypermethylation ◽  
Ctcf Binding ◽  
P Value ◽  
Global Dna Methylation ◽  
Employment Equity ◽  
Equity Ownership

Abstract Acute myeloid leukemia (AML) represents a highly heterogeneous myeloid stem cell disorder classified based on various genetic defects. Besides genetic alterations, epigenetic changes are recognized as an additional mechanism contributing to leukemogenesis, but insight into the latter process remains minor. Using a combination of Methyl-CpG-Immunoprecipitation (MCIp-chip) and MALDI-TOF analysis of bisulfite-treated DNA in a cohort of 196 AML patients we previously demonstrated that (cyto)genetically defined AML subtypes, including CBFB-MYH11, AML-ETO, NPM1-mut, CEBPA-mut or IDH1/2-mut subtypes, express specific DNA-methylation profiles (Gebhard et al, Leukemia, 2018). A fraction of AML patients (5/196) displayed a unique abnormal hypermethylation profile that was completely distinct from any other AML subtype. These patients present immature leukemia (FAB M0, M1) with various chromosomal aberrations but very few mutations (e.g. no IDH1/2, KRAS, DNMT3A) that might explain the CpG island methylator phenotype (CIMP) phenotype. The CIMP patients showed high resemblance with a recently reported CEBPA methylated subgroup (Wouters et al, 2007 and Figueroa et al, 2009), which we confirmed by MCIp-chip and MALDI-TOF analysis. To explore the whole range of epigenetic alterations in the CIMP-AML patients we performed in-depth global DNA methylation and gene expression analyses (MCIp-seq and RNA-seq) in 45 AML and 12 CIMP patients from both studies. Principle component analysis and t-distributed stochastic neighbor embedding (t-SNE) revealed that CIMP patients express a unique DNA-methylation and gene-expression signature that separated them from all other AMLs. We could discriminate promoter methylation from non-promoter methylation by selecting MCIp-seq peaks within 3kb around TSS. Promoter hypermethylation was highly associated with repression of genes (PCC = -0.053, p-value = 0.00075). Hypermethylation of non-promoter regions was more strongly associated with upregulation of genes (PCC = 0.046, p-value = 4.613e-06). Interestingly, differentially methylated regions also showed a positive association with myeloid lineage CTCF binding sites (27% vs 18% expected, p-value < 2.2e-16 in a chi-square test of independence). Methylation of CTCF sites causes loss of CTCF binding, which has been reported to disrupt boundaries between so-called topologically associated domains (TADs), allowing enhancers located in a particular TAD to become accessible to genes in adjacent TADs and affect their transcription. Whether this is the case is under investigation. In this study we particularly focused on the role of hypermethylation of promoters in CIMP-AMLs. Promoters of many transcriptional regulators that are involved in the differentiation of myeloid lineages of which several are frequently mutated in AML were hypermethylated and repressed, including CEBPA, CEBPD, IRF8, GATA2, KLF4, MITF or MAFB. Notably, HMGA2, a critical regulator of myeloid progenitor expansion, exhibited the largest degree of CIMP promoter hypermethylation compared to the other AMLs, accompanied by a reduction in gene expression. Moreover, multiple members of the HOXB family and KLF1 (erythroid differentiation) were methylated and repressed as well. In addition, these patients frequently showed hypermethylation of many chromatin factors (e.g. LMNA, CHD7 or TET2). Hypermethylation of the TET2 promoter could result in a loss of maintenance DNA demethylation and therefore successive hypermethylation at CpG islands. We carried out regulome-capture-bisulfite sequencing on CIMP-AMLs compared to other AML samples and normal blood cell controls and confirmed methylation of the same transcription and chromatin factor promoters. We conclude that these leukemias represent very primitive HSCPs which are blocked in differentiation into multiple hematopoietic lineages, due to the absence of regulators of these lineages. Although the underlying cause for the extreme hypermethylation signature is still subject to ongoing studies, the consequence of promoter hypermethylation is silencing of key lineage regulators causing the differentiation arrest in these cells. We argue that these patients may particularly benefit from therapies that revert DNA methylation. Disclosures Ehninger: Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; GEMoaB Monoclonals GmbH: Employment, Equity Ownership; Bayer: Research Funding. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding.

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