scholarly journals Gene Expression Profiling of Structural and Functional High-Risk Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3061-3061
Author(s):  
Moritz Binder ◽  
S. Vincent Rajkumar ◽  
Martha Q. Lacy ◽  
Jessica L. Haug ◽  
Angela Dispenzieri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Research Funding ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Cell Labeling ◽  
Kaplan Meier

Introduction: High-risk multiple myeloma can be defined by the presence of specific cytogenetic abnormalities (structural) or by characteristic changes in bone marrow and peripheral blood biomarkers (functional). While both entities are characterized by therapeutic resistance, frequent disease relapses, and adverse survival outcomes, the underlying molecular mechanisms remain incompletely understood. Methods: We performed gene expression profiling (GEP) using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from 137 patients diagnosed with multiple myeloma between 2004 and 2012. All patients underwent Fluorescence In-situ Hybridization (FISH) evaluation, plasma cell labeling, International Staging System (ISS) risk stratification, and GEP prior to initiating treatment with novel agents. The presence of del(17p), t(4;14), t(14;16), and t(14;20) on FISH, a plasma cell labeling index (PCLI) > 2%, and ISS stage III were considered high-risk abnormalities: FISH-HR (n = 15, structural high-risk, at least one high-risk FISH lesion), PCLI-HR (n = 20; functional high-risk, PCLI > 2%), and ISS-HR (n = 12; functional high-risk, ISS stage III). For each HR group we sampled standard risk (SR) controls in a 4:1 ratio. After data quality control and normalization, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple comparisons using a false discovery rate-based approach for genome-wide experiments (q-value). We employed PANTHER pathway analysis for the differentially expressed genes in each HR group. We implemented a simple gene expression score (GES) by calculating the sum of quartiles of the normalized gene expression values for genes differentially expressed in more than one HR group (GES = ΣUP(quartile - 1) + ΣDN(4 - quartile)) and externally validated its prognostic significance (UAMS TT2 / TT3, GSE24080). Survival outcomes were analyzed using the methods described by Kaplan, Meier, and Cox. Computation and visualization were performed in R. Results: Median age at diagnosis was 63 years (32 - 87), 53% of the patients were male. High-risk disease was associated with inferior overall survival, regardless of the used definition (left Kaplan-Meier plots): FISH-HR (HR 4.3, 95% CI 1.9 - 9.8, p < 0.001), PCLI-HR (HR 2.7, 95% CI 1.4 - 5.3, p = 0.004), and ISS-HR (HR 2.8, 95% CI 1.2 - 6.5, p = 0.015). There were 59 (FISH-HR), 424 (ISS-HR), and 507 (PCLI-HR) differentially expressed genes (q < 0.050 for all genes, volcano plots). PCLI-HR and FISH-HR demonstrated a predominance of transcriptional up-regulation while ISS-HR had a balanced gene expression profile with a similar number of genes being up- and down-regulated. The involved cellular pathways were different across the HR groups except for anti-apoptotic signaling (bar graphs). All HR groups had distinct gene expression profiles with no complete overlap between all HR groups. There were 71 genes with overlap between two HR groups (69 up-regulated, 2 down-regulated, Venn diagrams). The median GES was 97 (18 - 206, higher numbers indicating higher expression of up-regulated and lower numbers of down-regulated high-risk genes) in 559 patients treated on UAMS TT2 / TT3 (GSE24080). Tertiles of the GES were associated with event-free survival (HR 1.4, 95% CI 1.2 - 1.6, p < 0.001) and remained independently prognostic after adjusting for age, sex, and ISS stage (HR 1.3, 95% CI 1.1 - 1.5, p < 0.001). Conclusions: High-risk multiple myeloma remains associated with inferior overall survival, regardless of the used definition (structural or functional). The subtypes of high-risk disease have distinct gene expression profiles and involve different cellular pathways, providing important clues to the underlying biology. A 71 gene signature derived from the different high-risk subtypes was of prognostic significance in a clinical trial population after adjusting for known prognostic factors. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Stewart:Takeda: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Ono: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Integrated Transcriptomic Analysis of Necrosis-related Gene in Diffuse Gliomas

2019 ◽  
Vol 80 (04) ◽  
pp. 240-249
Author(s):  
Jiajia Wang ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Prognostic Index ◽  
Gene Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Low Grade

Glioblastoma multiforme (GBM), an aggressive brain tumor, is characterized histologically by the presence of a necrotic center surrounded by so-called pseudopalisading cells. Pseudopalisading necrosis has long been used as a prognostic feature. However, the underlying molecular mechanism regulating the progression of GBMs remains unclear. We hypothesized that the gene expression profiles of individual cancers, specifically necrosis-related genes, would provide objective information that would allow for the creation of a prognostic index. Gene expression profiles of necrotic and nonnecrotic areas were obtained from the Ivy Glioblastoma Atlas Project (IVY GAP) database to explore the differentially expressed genes.A robust signature of seven genes was identified as a predictor for glioblastoma and low-grade glioma (GBM/LGG) in patients from The Cancer Genome Atlas (TCGA) cohort. This set of genes was able to stratify GBM/LGG and GBM patients into high-risk and low-risk groups in the training set as well as the validation set. The TCGA, Repository for Molecular Brain Neoplasia Data (Rembrandt), and GSE16011 databases were then used to validate the expression level of these seven genes in GBMs and LGGs. Finally, the differentially expressed genes (DEGs) in the high-risk and low-risk groups were subjected to gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway, and gene set enrichment analyses, and they revealed that these DEGs were associated with immune and inflammatory responses. In conclusion, our study identified a novel seven-gene signature that may guide the prognostic prediction and development of therapeutic applications.

In Silico Prediction of Novel Drug Combinations to Combat Bortezomib-Resistant Multiple Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1344-1344
Author(s):  
Holly A. F. Stessman ◽  
Tian Xia ◽  
Aatif Mansoor ◽  
Raamesh Deshpande ◽  
Linda B. Baughn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Lines ◽  
In Silico ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mouse Cell ◽  
Resistant Mouse ◽  
Novel Therapeutic

Abstract Abstract 1344 Bortezomib/VELCADE® (Bz) is a proteasome inhibitor that has been used successfully in the treatment of multiple myeloma (MM) patients. However, acquired resistance to Bz is an emerging problem. Thus, there is a need for novel therapeutic combinations that enhance Bz sensitivity or re-sensitize Bz resistant MM cells to Bz. The Connectivity Map (CMAP; Broad Institute) database contains treatment-induced transcriptional signatures from 1,309 bioactive compounds in 4 human cancer cell lines. An input signature can be used to query the database for correlated drug signatures, a technique that has been used previously to identify drugs that combat chemoresistance in cancer (Wei, et al. Cancer Cell (2006) 10:331). In this study we used in silico bioinformatic screening of gene expression profiles from isogenic pairs of Bz sensitive and resistant mouse cell lines derived from the iMycCα/Bcl-xL mouse model of plasma cell malignancy to identify compounds that combat Bz resistance. We established Bz-induced kinetic gene expression profiles (GEPs) in 3 pairs of Bz sensitive and resistant mouse cell lines over the course of 24 hours. GEPs were collected in the absence of large-scale cell death. The 16 and 24 hour time points were averaged and compared between each Bz sensitive and resistant pair. Genes in the sensitive cell line with a fold change greater than 2, relative to the resistant line, were given the binary distinction of “up” or “down” depending on the direction of change. Genes that met these criteria were assembled into signatures, and then used as inputs for CMAP queries to identify compounds that induce similar transcriptional responses. In all pairs, treatment of the Bz sensitive line correlated with GEPs of drugs that target the proteasome, NF-κB, HSP90 and microtubules, as indicated by positive connectivity scores. However eight compounds, all classified as Topoisomerase (Topo) I and/or II inhibitors, were negatively correlated to our input signature. A negative connectivity score could have two interpretations: (1) this could indicate simply that Topos are upregulated by Bz treatment in Bz sensitive lines, which has been previously reported (Congdan, et al. Biochem. Pharmacol. (2008) 74: 883); or (2) this score could be interpreted as Topos are inhibited in Bz resistant cells upon Bz treatment. This led us to ask whether Topo inhibitors could target Bz resistant MM cells and re-sensitize them to Bz. Indeed, we found that multiple Topo inhibitors were significantly more active against Bz resistant cells as single agents and restored sensitivity to Bz when combined with Bz as a cocktail regimen. This work demonstrates the potential of this in silico bioinformatic approach for identifying novel therapeutic combinations that overcome Bz resistance in MM. Furthermore, it identifies Topo inhibitors – drugs that are already approved for clinical use – as agents that may have utility in combating Bz resistance in refractory MM patients. Disclosures: Stessman: Millennium: The Takeda Oncology Company: Research Funding. Van Ness:Millennium: The Takeda Oncology Company: Research Funding.

The Transcriptome of Immunomodulator-Resistant Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1772-1772
Author(s):  
Moritz Binder ◽  
S. Vincent Rajkumar ◽  
Martha Q. Lacy ◽  
Jessica L. Haug ◽  
Angela Dispenzieri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Overall Survival ◽  
Differentially Expressed Genes ◽  
Research Funding ◽  
Differentially Expressed ◽  
Resistant Disease ◽  
Kaplan Meier ◽  
Gene Coding ◽  
Cellular Pathways

Introduction: While the molecular target of immunomodulators such as pomalidomide (POM) and lenalidomide (LEN) has been identified, the mechanisms underlying therapeutic resistance remain incompletely understood. The uniformly emerging resistance to therapy over time in the absence of identifiable cereblon pathway mutations in the majority of patients raises questions about alternative mechanisms including aberrant gene expression. Methods: We performed gene expression profiling using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from patients with relapsed / refractory (RRMM) and newly diagnosed (NDMM) multiple myeloma prior to initiating treatment. Patients were treated on two phase II clinical trial protocols (MC0789: POM ± dexamethasone in RRMM; MC0884: LEN ± dexamethasone in NDMM) between 2007 and 2012. We categorized patients based on their IMWG response as non-responders (SD) and responders (VGPR+). We selected 15 responders and 15 non-responders from MC0789 (n = 30) and compared overall survival, gene expression patterns, and involved cellular pathways between the two groups. We selected 5 responders and 5 non-responders from MC0884 (n = 10) for targeted validation of differentially expressed candidate genes. After data quality control and normalization of gene expression values, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple testing in the discovery set using a false discovery rate-based approach for genome-wide experiments (q-value). We used Gene Ontology and PANTHER pathway analysis for functional annotation of differentially expressed genes. Overall survival estimates were calculated using the Kaplan-Meier method. Computation and visualization were performed in R. Results: Median age at treatment initiation on MC0789 was 65 years (40 - 82), 65% of the patients were male. Pomalidomide resistance was associated with an increase in mortality (median overall survival 1.6 versus 6.4 years, p = 0.009, Kaplan-Meier plot). There were 1076 differentially regulated genes between responders and non-responders (521 up- and 555 down-regulated, q < 0.050 for all genes, volcano plot). Expression of CRBN was 1.5-fold down-regulated in non-responders (q = 0.005). Supervised hierarchical clustering of the top 500 differentially expressed genes demonstrated distinct patterns in pomalidomide-resistant disease (heatmap). Gene ontology analysis revealed protein synthesis as one of the most enriched biological processes (bar graph). Pathway analysis showed a 6-fold enrichment (FDR = 0.007) of the ubiquitin proteasome pathway in pomalidomide-resistant disease. Differentially expressed genes involved key protein degradation pathways, epigenetic modifiers, and transcription factors. Targeted validation in MC0884 revealed 13 common genes with at least 1.5-fold differential expression (5 up- and 8 down-regulated), 12 of which have previously been implicated in the regulation of apoptosis, tumor glucose metabolism, Rho and Wnt signaling, miRNA-driven resistance to chemotherapy, and ubiquitin-dependent protein degradation (Table and Sankey diagram). The most up-regulated gene in non-responders was MYRIP, a gene coding for a vesicle trafficking protein associated with platinum resistance and suppression of pro-apoptotic BCL-2 family members in solid malignancies. The most down-regulated gene was FRZB, a gene coding for a negative regulator of Wnt signaling, previously implicated in the progression of monoclonal gammopathy of undetermined significance to multiple myeloma. Conclusions: Overall survival of patients with pomalidomide-resistant RRMM remains poor. Pomalidomide resistance was associated with differential gene expression in several potentially targetable cellular pathways beyond the known drug target cereblon. Targeted validation of candidate genes revealed common cellular pathways in immunomodulator-resistant disease. Elucidating the exact molecular mechanisms underlying immunomodulator resistance is of considerable interest for biomarker development and the identification of novel therapeutic targets and warrants further exploration. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Alnylam: Research Funding; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Akcea: Consultancy; Takeda: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Effect of Imids on Gene Expression Profiles of Fresh Human Myeloma Cells

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 5013-5013
Author(s):  
Ines Tagoug ◽  
Adriana Plesa ◽  
Julie Vendrell ◽  
Charles Dumontet
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Family Member ◽  
Magnetic Beads ◽  
Expression Profiles ◽  
Calf Serum ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Myeloma Cells ◽  
Human Myeloma Cells

Abstract Abstract 5013 Immunomodulatory drugs represent a major therapeutic advance in the treatment of patients with multiple myeloma. While these agents appear to exert various effects on the microenvironment, including effect on immune cells and angiogenesis, a direct effect on the tumor cells themselves is also likely. To describe and compare the effect of the three clinically available agents (thalidomide, lenalidomide, pomalidomide) we analyzed the gene expression profiles of fresh human myeloma cells exposed to thalidomide, lenalidomide or pomalidomide, using high density DNA arrays. Fresh human myeloma samples were obtained from bone marrow aspirates of patients with myeloma, and myeloma cells were immunopurified using anti CD138 magnetic beads. Purified myeloma cells (1.106 cells/ml) were incubated for 24 hours in RPMI 1640 medium supplemented with 10% fetal calf serum under each of the four following conditions: 1) DMSO; 2) thalidomide 40 microM; 3) lenalidomide 1 microM; 4) pomalidomide 100 nM. These levels are achievable in the plasma of MM pts. Pangenomic array experiments were performed usingWhole Human Genome 4 × 44K Agilent one-color microarrays. Data were normalized using the quantile normalization method. Samples were analysed for differentially expressed genes, taking into account both the level of significance and the fold-change. Ten evaluable samples were processed. Exposure to thalidomide, lenalidomide and pomalidomide induced differential expression of 36, 50 and 75 genes, respectively, in comparison to DMSO-exposed controls, the total list including 101 genes. Twelve of these were found to be differentially expressed after exposure to all of the three agents, including trophoblast glycoprotein, WAS protein family member 1, dickkopf homolog 1, pentraxin-related gene, CD28, interleukin 12B, tissue factor pathway inhibitor 2, phospholipase A2, dehydrogenase/reductase (SDR family) member 9, hypothetical LOC145788 and betacellulin. These commonly altered genes could be mechanistically involved in themultiple activities of these agents in multiple myeloma or may represent epiphenoma mechanistically unrelated to drug-induced cell death. Genes differentially expressed between the treatment with each of these agents could be indicative of the different and non-overlapping actions these agents have in multiple myeloma. An example of this is the recent demonstration that pomalidomide is clinically active in lenalidomide refractory patients. These results suggest that exposure to IMIDs induce various intracellular signalization pathways in myeloma cells which might be involved in the cytotoxic activity of these compounds. Disclosures: Dumontet: Celgene: Research Funding.

scholarly journals Integrative DNA Methylation and Gene Expression Analysis Reveals Candidate Biomarkers Associated with Dichotomized Response to Chemoimmunotherapy in Diffuse Large B-Cell Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 22-22
Author(s):  
Ellen K. Kendall ◽  
Manishkumar S. Patel ◽  
Sarah Ondrejka ◽  
Agrima Mian ◽  
Yazeed Sawalha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Gene Sets

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. While 60% of DLBCL patients achieve complete remission with frontline therapy, relapsed/refractory (R/R) DLBCL patients have a poor prognosis with median overall survival below one year, necessitating investigation into the biological principles that distinguish cured from R/R DLBCL. Recent analyses have identified unfavorable molecular signatures when accounting for gene expression, copy number alterations and mutational profiles in R/R DLBCL. However, an integrative analysis of the relationship between epigenetic and transcriptomic changes has yet to be described. In this study, we compared baseline methylation and gene expression profiles of DLBCL patients with dichotomized clinical outcomes. Methods: Diagnostic DLBCL biopsies were obtained from two patient cohorts: patients who relapsed or were refractory following chemoimmunotherapy ("R/R"), and patients who entered durable clinical remission following therapy ("cured"). The median age for R/R and cured cohorts were 62 (range 35-86) years vs. 64 (range 28-83) years (P= 0.27). High-intermediate or high IPI scores were present in 14 vs. 6 patients (P= 0.08) in the R/R and cured cohorts, respectively. All patients were treated with frontline R-CHOP or R-EPOCH. DNA and RNA were extracted simultaneously from formalin-fixed, paraffin embedded biopsy samples. An Illumina 850k Methylation Array was used to identify DNA methylation levels in 29 R/R patients and 20 cured patients. RNA sequencing was performed on 9 R/R patients and 7 cured patients at diagnosis using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Results: At the time of diagnosis, we found significant epigenetic and transcriptomic differences between cured and R/R patients. Comparing cured to R/R samples, there were 8,159 differentially methylated probes (FDR&lt;0.05). Differentially methylated regions between R/R and cured cohorts overlap with genes previously identified as mutation hotspots in DLBCL. Upon comparing transcriptomic profiles between R/R and cured, 267 genes were found to be differentially expressed (Log2FC&gt;|1| and FDR&lt;0.05). Gene set enrichment analysis revealed gene sets related to cell cycle, membrane trafficking, Rho and Rab family GTPase function, and transcriptional regulation were upregulated in the R/R samples. Gene sets related to innate immune signaling, Type I and II interferon signaling, fatty acid and carbohydrate metabolism were upregulated in the cured samples. To identify genes likely to be regulated by specific changes in methylation, we selected genes that were both differentially expressed and differentially methylated between the R/R and cured cohorts. In the R/R samples, 13 genes (ARMC5, ARRDC1, C12orf57, CCSER1, D2HGDH, DUOX2, FAM189B, FKBP2, KLF5, MFSD10, NEK8, NT5C, and WDR18) were significantly hypermethylated and underexpressed when compared to cured specimens, suggesting that epigenetic silencing of these genes is associated with lack of response to chemoimmunotherapy. In contrast, 12 genes (ATP2B1, C15orf41, FAM102B, FAM3C, FHOD3, FYTTD1, GPR180, KIAA1841, LRMP, MEF2A, RRAS2, and TPD52) were significantly hypermethylated and underexpressed in cured patients, suggesting that epigenetic silencing of these genes is favorable for treatment response. Many of these epigenetically modified genes have been previously implicated in cancer biology, including roles in NOTCH signaling, chromosomal instability, and biomarkers of prognosis. Conclusions: This is the first integrative epigenetic and transcriptomic analysis of diagnostic biopsies from cured and R/R DLBCL patients following chemoimmunotherapy. At the time of diagnosis, both the methylation and gene expression profiles significantly differ between patients that enter durable remission as opposed to those who are R/R to therapy. Soon, the hypomethylating agent CC-486 (i.e. oral azacitidine) will be explored in combination with mini-R-CHOP for older DLBCL patients in whom DNA methylation is likely increased. These data support the use of hypomethylating agents to potentially restore sensitivity of DLBCL to chemoimmunotherapy. Disclosures Hsi: Eli Lilly: Research Funding; Abbvie: Research Funding; Miltenyi: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; CytomX: Consultancy, Honoraria. Hill:Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding.

scholarly journals Gene Expression Profiles of Tumor Biology Provide a Novel Approach to Prognosis and May Guide the Selection of Therapeutic Targets in Multiple Myeloma

2009 ◽  
Vol 27 (25) ◽  
pp. 4197-4203 ◽  
Author(s):  
Ariel Anguiano ◽  
Sascha A. Tuchman ◽  
Chaitanya Acharya ◽  
Kelly Salter ◽  
Cristina Gasparetto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Tumor Biology ◽  
Gene Expression Profiles ◽  
Staging System ◽  
Gene Signature ◽  
Molecular Defects

PurposeMonoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) comprise heterogeneous disorders with incompletely understood molecular defects and variable clinical features. We performed gene expression profiling (GEP) with microarray data to better dissect the molecular phenotypes and prognoses of these diseases.MethodsUsing gene expression and clinical data from 877 patients ranging from normal plasma cells (NPC) to relapsed MM (RMM), we applied gene expression signatures reflecting deregulation of oncogenic pathways and tumor microenvironment to highlight molecular changes that occur as NPCs transition to MM, create a high-risk MGUS gene signature, and subgroup International Staging System (ISS) stages into more prognostically accurate clusters of patients.ResultsMyc upregulation and increasing chromosomal instability (CIN) characterized the evolution from NPC to RMM (P < .0001 for both). Studies of MGUS revealed that some samples shared biologic features with RMM, which comprised the basis for a high-risk MGUS signature. Regarding MM, we subclassified ISS stages into clusters based on shared features of tumor biology. These clusters differentiated themselves based on predictions for prognosis (eg, in ISS stage I, one cluster was characterized by increased CIN and a poor prognosis).ConclusionGEP provides insight into the molecular defects underlying plasma cell dyscrasias that may explain their clinical heterogeneity. GEP also may also refine current prognostic and therapeutic models for MGUS and MM.

Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome

2008 ◽  
Vol 26 (29) ◽  
pp. 4798-4805 ◽  
Author(s):  
Olivier Decaux ◽  
Laurence Lodé ◽  
Florence Magrangeas ◽  
Catherine Charbonnel ◽  
Wilfried Gouraud ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Multiple Myeloma ◽  
High Risk ◽  
Prognostic Markers ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
High Risk Patients ◽  
Risk Patients

Purpose Survival of patients with multiple myeloma is highly heterogeneous, from periods of a few weeks to more than 10 years. We used gene expression profiles of myeloma cells obtained at diagnosis to identify broadly applicable prognostic markers. Patients and Methods In a training set of 182 patients, we used supervised methods to identify individual genes associated with length of survival. A survival model was built from these genes. The validity of our model was assessed in our test set of 68 patients and in three independent cohorts comprising 853 patients with multiple myeloma. Results The 15 strongest genes associated with the length of survival were used to calculate a risk score and to stratify patients into low-risk and high-risk groups. The survival-predictor score was significantly associated with survival in both the training and test sets and in the external validation cohorts. The Kaplan-Meier estimates of rates of survival at 3 years were 90.5% (95% CI, 85.6% to 95.3%) and 47.4% (95% CI, 33.5% to 60.1%), respectively, in our patients having a low risk or high risk independently of traditional prognostic factors. High-risk patients constituted a homogeneous biologic entity characterized by the overexpression of genes involved in cell cycle progression and its surveillance, whereas low-risk patients were heterogeneous and displayed hyperdiploid signatures. Conclusion Gene expression–based survival prediction and molecular features associated with high-risk patients may be useful for developing prognostic markers and may provide basis to treat these patients with new targeted antimitotics.

Prognostic Impact of Genetic Subgroups and Development of Gene Classifiers for Response, PFS and OS In Multiple Myeloma Patients Treated with Bortezomib or Conventional Agents In HOVON65/GMMG-HD4 Trial

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 445-445
Author(s):  
Annemiek Broyl ◽  
Rowan Kuiper ◽  
Martin H. van Vliet ◽  
Erik H. van Beers ◽  
Yvonne de Knegt ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Research Funding ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
Prognostic Indicators ◽  
Prognostic Impact ◽  
Ortho Biotech

Abstract Abstract 445 Background. In newly diagnosed myeloma patients, bortezomib treatment induces high rates of complete response (CR) and very good partial response (VGPR). Recently, we published the clustering of gene expression profiles in 320 MM patients, who were included in a large prospective, randomized, phase III transplantation trial with bortezomib (PAD) versus conventional vincristine (VAD) based induction treatment (HOVON65/GMMG-HD4). We identified 12 distinct subgroups CD-1, CD-2, MF, MS, PR, HY, LB, Myeloid, including three novel defined subgroups NFκB, CTA, and PRL3 and a subgroup with no clear gene expression profile (NP). Aim. To look at the prognostic impact of these 12 clusters in the trial and group clusters together into a high risk (HR) and low risk (LR) group in the different treatment arms. Furthermore, to define a high risk signature to identify the patients at increased risk of disease progression. Methods. Gene expression profiles of myeloma cells obtained at diagnosis of 320 HOVON65/GMMG-HD4 patients were available. Response, progression free survival (PFS) and overall survival (OS) data were available for the first 628 patients, resulting in analysis of gene expression in relation to prognosis in 229 patients. The prognostic impact of the genetic subgroups separately and grouped into high and low risk were evaluated using Kaplan Meier and Cox regression analysis using exhaustive search (R). For the high risk gene signature the HOVON65 gene expression data was used as training set with PFS as outcome measure. Two independent myeloma datasets with survival data were used as an external validation, UAMS (GSE2658) and APEX (GSE9782)). The signature was generated by a Cox proportional hazard model in combination with LASSO (Least Absolute Shrinkages and Selection Operator) for simultaneous parameter estimation and variable selection using the R package glmnet. ISS stage was implemented by adjusting the individual covariant penalization factors of the LASSO. Results. The highest CR+nCR rates were found in the PRL3 and NP clusters, i.e. 78% and 86%, respectively (VAD), and 100% (PAD). The lowest CR+nCR rate was 17% in the CD1 cluster (PAD) and 0% in the CD2, MF and PR clusters (VAD). Based on the impact of clusters on PFS and OS in the VAD arm, the MS, MF, PR and CTA clusters were included into a High Risk (HR) group. This HR group showed a median PFS of 13 months and OS of 21 months vs. the Low Risk (LR) group consisting of the remainder of clusters with a median PFS of 31 months and a median OS not reached (P<.001).In contrast, in the PAD arm, only the PR cluster conferred a poor prognosis and exclusively formed the HR group, showing a median PFS of 12 months and OS of 13 months vs. the LR groups consisting of the remainder of clusters with a median PFS of 32 months and a median OS not reached (P=.004). In the poor prognostic subgroup MF, a striking difference in outcome on PAD vs. VAD was observed, i.e. 24 vs. 3 months (PFS) and 39 vs. 4 months (OS), respectively. In the multivariate analysis, including the covariates recurrent translocations, 17p deletion, 13q loss, LDH and ISS stage, the HR group definition remained independent poor prognostic indicators for both treatment arms. A LASSO-based high-risk signature was generated (48 probe sets). External validation was performed in the data sets UAMS TT2 and APEX. In both studies, the high-risk signature identified a high-risk population of 13% with highly significant log rank p-values (7.7*10−8 and 4.2*10−9, respectively). Combination of all validation data, n=615, corrected for type of treatment and study, resulted in an overall Cox proportional hazard model's hazard ratio of 3.8 with p <2*10−16. Conclusion. Distinctive gene expression clusters affect prognosis, which differ depending on treatment. In the conventional treatment arm (VAD), MS, MF, PR and CTA clusters confer a worse prognosis while with bortezomib based treatment, only the PR cluster affects prognosis negatively. These HR groups remain independent poor prognostic indicators. Based on the HOVON65/GMMG-HD4 study population, a high-risk signature was generated with strong and highly significant predicting ability in two independent data sets. Disclosures: Mulligan: Millenium: Employment. Goldschmidt:Celgene: Membership on an entity's Board of Directors or advisory committees; Ortho Biotech: Membership on entity's Board of Directors or advisory committees; Ortho Biotech: Research Funding; Celgene: Research Funding; Chugai Pharma: Research Funding; Amgen: Research Funding. Sonneveld:Ortho Biotech: Research Funding; International Myeloma Foundation: Research Funding; Ortho Biotech: Consultancy.

Mesenchymal Stem Cells Preconditioned with Myeloma Cells from High-Risk Patients Support the Growth of Myeloma Cells from Low-Risk Patients

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3304-3304 ◽  
Author(s):  
Syed Mehdi ◽  
Sharmin Khan ◽  
Wen Ling ◽  
Randal S Shelton ◽  
Joshua Epstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
High Risk ◽  
Cell Growth ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Myeloma Cells ◽  
Risk Patients

Abstract Introduction: Each disease stage in myeloma (MM) is associated with parallel changes in both the MM clone and the bone marrow (BM) microenvironment. Mesenchymal cell lineages derived from mesenchymal stem cells (MSCs), including osteoblasts, adipocytes and pericytes play an important role in MM cell growth mediated by the modification of the MM niche in the BM. The overall goal of the study was to test and identify changes induced in MSCs by high-risk (HR) MM cells that impact MSC function and promote oncogenic pathways capable of supporting low-risk (LR) MM cells. Methods: Normal MSCs were either cultured alone ("unconditioned") or co-cultured with MM cells for 5 days. The cultured and co-cultured cells were trypsinized, replated for 40 min, followed by serial washing to remove MM cells from the adherent MSCs. More than 95% of the remaining adherent cells after co-culture were MSCs ("preconditioned"). The unconditioned and preconditioned MSCs or their 24 hrs conditioned media (CM; 50%) were tested for their ability to support the 5-days growth of CD138+ MM cells from LR (n=4) and HR (n=3) patients. To identify factors altered in MSCs by HR MM cells, the unconditioned and preconditioned MSCs and their serum-free conditioned media (n=4) underwent gene expression profiling and proteomic analysis. Whole bone biopsy gene expression profiles from newly diagnosed patients with MM enrolled in Total Therapy clinical trials were used to correlate the altered expression of factors in preconditioned MSCs with their expression in clinical samples. Results: Growth of all MM cells tested was increased by inclusion of MSCs preconditioned with HR MM cells by 2.2± 0.2 (p<0.0004) and by CM from these MSCs by 9.6±2.0 (p<0.006), compared to culture of MM cells in fresh media. In contrast, CM from unconditioned MSCs increased growth of HR MM cells by 2.6±0.6 (p<0.01) fold but had minor effect on growth of LR MM cells. CM from MSCs preconditioned with HR MM cells increased growth of LR and HR MM cells by 5.7±0.1 (p<0.0002) and 2.6±1.2 (p<0.04), compared to culture of MM cells in CM from unconditioned MSCs, respectively. Growth of LR MM cells was higher by 2.9±0.3 fold using CM from MSCs preconditioned with HR MM cells than by using CM from MSCs preconditioned with LR MM cells (p<0.005). To determine the role of cell-cell contact, we compared the effect of the preconditioned MSCs and their CM on growth of LR and HR MM cells. Growth of LR MM cells (p< 0.003) and HR MM cells (p< 0.005) was higher when cultured in CM than in co-culture with MSCs. These data imply that soluble factors from preconditioned MSCs support MM cell proliferation and that adhesion of MM cells to MSCs may restrain proliferation. Genes overexpressed in preconditioned MSCs included growth factors (e.g. IL6) and receptors (e.g. EDNRA); genes underexpressed include factors associated with activity of osteoblasts (e.g. ITGBL1) and adipocytes (IGFBP2). A proteomic analysis showed a reduced level of the secreted factors IGFBP2 and ITGBL1 and increase level of IL6 in CM from MSCs preconditioned with HR MM cells compared to CM from unconditioned MSCs. IGFBP2 mediates local bioavailability of IGF1 and IGF2 and is also involved in bone formation and angiogenesis independently of the IGF axis. ITGBL1 is involved in osteoblastogenesis whereas EDNRA is known to be expressed by pericytes. Global gene expression profiles from patient material showed that EDNRA and IGFBP2 are not expressed in MM cells but are highly expressed in cultured MSCs compared to hematopoietic cells in buffy coat BM samples. EDNRA is overexpressed (p<0.005) whereas IGFBP2 is underexpressed (p<0.005) in whole BM biopsy samples from MM patients with HR disease compared to patients with LR disease (p<0.005) and in Focal Lesion compared to random BM biopsies taken from the same patients (p<0.0000006 for EDNRA and p<0.02 for IGFBP2). IHC staining of patients' bone biopsies showed higher numbers of EDNRA+ mesenchymal-like cells in MM (n=10) than MGUS/AMM (n=10, p<0.0003) and in HR MM BM than LR MM BM (p<0.03). IHC analysis also revealed that IGFBP2 is highly expressed by immature adipocytes and that its expression in these cells is reduced in HR MM BM. Conclusion: Preconditioning of MSCs is essential for promoting growth of MM cells from LR patients. Factors altered in MSCs by HR MM cells are linked to signaling pathways known to directly stimulate MM cell growth and markers associated with distinct MSC lineages changed in HR MM niche. Disclosures Davies: Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Barlogie:Signal Genetics: Patents & Royalties. Morgan:Janssen: Research Funding; Univ of AR for Medical Sciences: Employment; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.

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