scholarly journals Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

Leukemia ◽  
2014 ◽  
Vol 28 (11) ◽  
pp. 2229-2234 ◽  
Author(s):  
S B Amin ◽  
W-K Yip ◽  
S Minvielle ◽  
A Broyl ◽  
Y Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Complete Response

Novel Murine Model To Study Modulation of Genes and Molecular Pathways Induced Following In Vivo Interaction between Multiple Myeloma Cells and Human BM Milieu.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3409-3409
Author(s):  
Paola Neri ◽  
Pierfrancesco Tassone ◽  
Masood Shammas ◽  
Mariateresa Fulciniti ◽  
Yu-Tzu Tai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Cell Growth ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Murine Model ◽  
Cell Signalling

Abstract Interaction between multiple myeloma (MM) cells and the bone marrow (BM) microenvironment plays a critical role in promoting MM cell growth, survival, migration and development of drug resistance. This interaction within the bone marrow milieu is unique and its understanding is important in evaluating effects of novel agents in vitro and in vivo. We here describe a novel murine model that allows us to study the expression changes in vivo in MM cells within the human BM milieu. In this model, the green fluorescent protein (INA-6 GFP+) transduced IL-6-dependent human MM cell line, INA-6, was injected in human bone chip implanted into SCID mice. At different time points the bone chip was retrieved, cells flushed out and GFP+ MM cells were purified by CD138 MACS microbeads. Similar isolation process was used on INA-6 GFP+ cells cultured in vitro and used as control. Total RNA was isolated from these cells and gene expression profile analyzed using the HG-U133 array chip (Affymetrix) and DChip analyzer program. We have identified significant changes in expression of several genes following in vivo interaction between INA-6 and the BM microenvironment. Specifically, we observed up-regulation of genes associated with cytokines (IL-4, IL-8, IGFB 2–5) and chemokines (CCL2, 5, 6, 18, 24, CCR1, 2, 4), implicated in cell-cell signalling. Moreover genes implicated in DNA transcription (V-Fos, V-Jun, V-kit), adhesion (Integrin alpha 2b, 7, cadherin 1 and 11) and cell growth (CDC14, Cyclin G2, ADRA1A) were also up-regulated and genes involved in apoptosis and cell death (p-57, BCL2, TNF1a) were down-regulated. Using the Ingenuity Pathway Analysis the most relevant pathways modulated by the in vivo interaction between MM cells and BMSCs were IL-6, IGF1, TGF-beta and ERK/MAPK-mediated pathways as well as cell-cycle regulation and chemokine signalling. These results are consistent with previously observed in vitro cell signalling studies. Taken together these results highlight the ability of BM microenvironment to modulate the gene expression profile of the MM cells and our ability to in vivo monitor the changes. This model thus provides us with an ability to study in vivo effects of novel agents on expression profile of MM cells in BM milieu, to pre-clinically characterize their activity.

Resistance to Lenalidomide in Multiple Myeloma Is Associated with a Switch in Gene Expression Profile

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1789-1789 ◽  
Author(s):  
Michael D. Amatangelo ◽  
Paola Neri ◽  
Maria Ortiz ◽  
Chad C. Bjorklund ◽  
Anita K. Gandhi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Research Funding ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Newly Diagnosed ◽  
Employment Equity ◽  
Equity Ownership

Abstract Background: Cereblon (CRBN) is a substrate receptor of the Cullin 4 E3 ubiquitin ligase complex CRL4CRBN and is the molecular target of the IMiD® immunomodulatory drug lenalidomide. It has been shown that cereblon is required for the anti-proliferative activity of lenalidomide in multiple myeloma (MM) and that reduction of CRBN expression is associated with resistance to IMiD® compounds. Methods: RNA-seq analysis was performed on 12 paired MM patients samples of sorted CD138+ cells obtained prior to lenalidomide treatment initiation and after development of resistance. Transcriptome sequence data was generated on an Ion Torrent Proton sequencer with at least 70 million reads per sample. The STAR aligner was used to align raw reads to the Ensembl74 reference annotation. The HTseq and eXpress algorithms were used to quantify gene and transcript counts, respectively, and the Sailfish algorithm was used to validate eXpress transcript counts. The Deseq2 algorithm was used to determine differential expression at gene and transcript levels between paired samples. Results: Of 272 genes observed to change significantly in expression at relapse (FDR < 0.05), a majority (169) were up-regulated. Inter-pathway similarity analysis based on gene set enrichment analysis (GSEA; canonical pathways) suggested 4 distinct processes were down-regulated at relapse, including Notch Signaling, Interferon Signaling and G-coupled protein receptor signaling. Conversely, patients exhibited a single dominant up-regulated process associated with proliferation. Additional GSEA analysis on more specific gene categories revealed up-regulation of the Proliferation gene cluster described in the University of Arkansas for Medical Science (UAMS) classification for newly diagnosed MM (6 of 2599 gene sets tested; FDR<0.01), which is associated with poor prognosis. This suggests that specific gene expression profiles (GEPs) identified in newly diagnosed MM patients may be enriched in relapsed samples. Further analysis of differential gene expression was performed to assess correspondence against the 10 MM GEP subgroups identified by Broyl, et al. (Blood, 2010) from newly diagnosed multiple myeloma. The output revealed significant enrichment of the Proliferation and MMSET/FGFR3 subgroup classifications (FDR<0.01) and a corresponding decrease in the NFkB subgroup classification at relapse (FDR<0.01), indicating a switch in GEP enrichment in relapse samples. Significantly changed genes common between Proliferation-MMSET/FGFR3 and NFkB classifications and contributing to the switch in GEP included BUB1B (FDR<0.001), HMMR (FDR<0.001), TAGAP (FDR<0.001), SMC4 (FDR=0.002), RRM2 (FDR=0.005) and KLF6 (FDR=0.029). Examination of genes commonly associated with lenalidomide mechanism of action revealed that in this cohort CRBN RNA was down-regulated by more than 2-fold in one patient and an enrichment of CRBN transcript lacking exon 10 was observed in another patient. Interestingly, high levels of CRBN transcripts that retain introns 6, 7 and 8 and do not encode for protein were detected in both diagnostic and relapse samples, which might explain previously observed discordant expression between CRBN mRNA and protein. Furthermore, no significant changes in gene expression at relapse was observed for Aiolos, Ikaros, c-myc or IRF4, although there was a trend for c-myc up-regulation. Ikaros and Aiolos are known to undergo extensive splicing, however, we were unable to detect changes in Aiolos or Ikaros splicing in patients at relapse. Conclusions: Taken together, this data suggests that lenalidomide resistance in patients is associated with a switch in gene expression profile from NFkB to Proliferation and MMSET/FGFR3 subgroups identified by Broyl, et al (Blood 2012). Given these GEPs were obtained from newly diagnosed patients, this yields the hypothesis that lenalidomide treatment induces a reduction in MM cells with an NFkB gene expression profile and expansion of cells exhibiting a Proliferation and/or MMSET/FGFR3 associated GEP, which appear to be resistant to therapy. Future studies to understand how individual genes in the GEP subgroups identified contribute to lenalidomide sensitivity/resistance are on-going. Disclosures Amatangelo: Celgene Corporation: Employment, Equity Ownership. Neri:Celgene: Research Funding. Ortiz:Celgene Corporation: Employment. Bjorklund:Celgene Corporation: Employment, Equity Ownership. Gandhi:Celgene: Employment, Equity Ownership. Klippel:Celgene Corporation: Employment, Equity Ownership. Bahlis:Amgen: Consultancy; Johnson & Johnson: Consultancy; Johnson & Johnson: Speakers Bureau; Johnson & Johnson: Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Daniel:Celgene Corporation: Employment, Equity Ownership. Chopra:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment. Thakurta:Celgene Corporation: Employment, Equity Ownership.

A detailed exploration of using RNA-Seq data in established multiple myeloma gene expression profile microarray based risk scores

2019 ◽  
Vol 19 (10) ◽  
pp. e57-e58
Author(s):  
Michael Bauer ◽  
Cody Ashby ◽  
Christopher Wardell ◽  
Gareth Morgan ◽  
Brian Walker
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Risk Scores ◽  
Rna Seq

The Location of Multiple Myeloma Adhesion Variants in Diverse Niches within the Bone Marrow Affects Plasma Cells Enumeration.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 5063-5063
Author(s):  
Liat Nadav ◽  
Ben-Zion Katz ◽  
Shoshana Baron ◽  
Lydia Lydia ◽  
Aaron Polliack ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Flow Cytometry ◽  
Gene Expression Profile ◽  
Plasma Cell ◽  
Expression Profile ◽  
Plasma Cells ◽  
Response To Therapy ◽  
Morphological Evaluation

Abstract Background - The diagnosis of multiple myeloma (MM) is based on clinical and laboratory criteria combined with bone marrow (BM) plasmocytosis, estimated by inspection of bone marrow aspirates. Recent advances in flow-cytometry (FCM) have provided an additional tool for the diagnosis of MM and for monitoring response to therapy. However, significant discrepancy has been reported regarding the enumeration of plasma cells in marrow samples of MM patients using these two methods. Aims - In this study we compared the bone marrow plasmocytosis by microscopic examination of BM aspirates, to the flow cytometry results in samples obtained form MM patients. We tested whether the noted discrepancy between these two methods applies only to MM, or represents a trend in other hematopoietic malignancies as well. We defined this discrepancy and explained it. Methods - The number of plasma cells or blasts from BM aspirates of 41 MM or seven acute myeloid leukemia (AML) patients respectively were analyzed simultaneously by morphological evaluation and by FCM. Each sample was assessed independently by two qualified laboratory specialists and/or hemato-pathologist. In MM we found plasma cell fractions that were characterized by FCM and gene expression profile. Results - In MM it was evident that FCM under-estimated the number of BM plasma cells samples by an average of 60%, compared with conventional morphological evaluation. On the other hand in AML there was a good correlation between the morphological and FCM assessments of the blast cell population, indicating that the discrepancy observed in the MM BM samples may be related to unique characteristics of the malignant plasma cells. Since flow cytometry is performed on the bone marrow fluid which is depleted of fat tissue-adhesive plasma cells, we disrupted spicules from MM BM samples (by repeated passages through 21g needle) and found a 40% increase in plasma cell compared with the fluid of the same BM samples. In order to determine the FCM profile of the cells in these two fractions, we isolated BM derived spicules from aspirates of MM patients and treated them with extracellular matrix (ECM) degrading enzymes followed by mechanical shearing. This combination released the highly adhesive plasma cells from the spicules. The released myeloma cells displayed a different FCM profile and in particular had a higher level of CD138 expression. Gene expression profile, which was performed on similar adhesion variants of cultured MM cells, demonstrated distinct oncogenic and transcriptional programs. Summary - We have shown a major discrepancy between the percentage of MM cells obtained by routine BM morphology and flow cytometry counts. It is possible that this discrepancy is partially attributable to the two distinct microenvironmental components occupied by MM cells in the BM sample - the lipid spicules, and the fluid phase. MM cells located in different niches of the BM also differ in their FCM and gene expression profile. This study indicates that multiple myeloma patients contain heterogeneous populations of malignant plasma cells. These sub-populations may play distinct roles in the biological and clinical manifestations of the disease and differ in their response to anti-myeloma therapy.

Gene Expression Profile Alone Is Inadequate In Predicting Complete Responses In Multiple Myeloma

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 306-306
Author(s):  
Samir B. Amin ◽  
Stephane Minvielle ◽  
Bret Hanlon ◽  
Parantu K Shah ◽  
Cheng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Treatment Response ◽  
Board Of Directors ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Predictive Value ◽  
Response Prediction ◽  
Current Therapy ◽  
Advisory Committees

Abstract Abstract 306 Current therapy for multiple myeloma (MM) remains empiric. With advancement in the understanding of its molecular basis, newer therapies are emerging faster than ever with increasing the difficulty in the selection of treatment regime to maximize response and minimize the rising cost of therapy. In recent years, treatment response prediction using gene expression profiling is being evaluated to identify expression signature that can classify patients likely to benefit from chemotherapy, e.g., there are several multi-gene expression assays available to predict treatment responses. However, expression signatures and predictive power vary significantly among these assays. Here, we have assessed the ability of gene expression profile to predict complete response (CR) in patients with MM. We evaluated 128 newly-diagnosed patients with MM enrolled on IFM protocol and treated uniformly with high-dose melphalan followed by autologous stem cell transplant. Seventy one of 128 patients (56%) had achieved CR while the rest 57 (44%) had partial response (PR) or less to this therapeutic intervention. CD138+ MM cells collected at the time of diagnosis were profiled for gene expression and processed using the dChip and aroma.affymetrix module in R software. We have used all common machine learning packages in R/Bioconductor and BRB-array Tools software to build response signature models; the packages used but not limited to were: Decision tree, Support Vector Machines (SVM), Prediction Analysis of Microarray (PAM), K-Nearest Neighbors, Bayesian Additive Regression Trees (BART), Lasso, Ridge regression, amongst others. For accurate assessment of model prediction ability, the dataset was split into training and test sets. Classifier gene models were built, trained and evaluated using K-fold cross-validation followed by model selection based on minimum prediction error. We built several models using different classification methods and experimented with gene inclusion criteria in our datasets according to those features most differentially expressed between CR and non-CR patients. Final model from each of these methods was applied to test dataset to predict CR vs non-CR, and prediction results were evaluated using area under the ROC curve (AUC) as a predictive measure. The maximum AUC among all the training-testing splits was 0.63. The true positive rate (Sensitivity) to correctly predict CR case reached maximum 70% or more at the cost of higher false negative, which is to misclassify a patient as non-CR who might have responded to the treatment. Among the number of methods employed, our best predictive capability provided 66% sensitivity, 60% specificity, 67% positive predictive value and 59% negative predictive value. Importantly, comparing real CR proportion (71/128 = 56%) with that of predicted by the best model (66%), no statistically significant difference was observed (Chi-square; p-value: 0.09). We observe similar results using two independent datasets available in public data repository. Based on our analysis, we recognize and in fact foresee that the expression profile alone has limited ability to predict treatment response especially when response rate is high. This lack of predictability using current approach of response prediction with gene expression alone may be related to several limitations, like alternate splicing, miRNA-based gene regulation, post-translational modifications, binary distribution of response status, inherent variability of new samples, and developing unified signature without consideration of myeloma subtypes. A comprehensive model needs to be developed using global genomic changes to have meaningful output for clinical application. Disclosures: Munshi: Millennium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Onyx: Membership on an entity's Board of Directors or advisory committees.

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