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.

Identification of Patients At High Risk for Recurrent Venous Thromboembolism by Whole Blood Gene Expression Analysis

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2305-2305
Author(s):  
Thomas L. Ortel ◽  
Michele Beckman ◽  
W Craig Hooper ◽  
Deborah A Lewis ◽  
Jen-Tsan A. Chi ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Whole Blood ◽  
Antiphospholipid Antibodies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
Risk Patients ◽  
Patient Groups ◽  
Recurrent Vte

Abstract Abstract 2305 Background. Recurrent venous thromboembolism (VTE) occurs in ∼30% of patients with spontaneous VTE after completion of a standard course of anticoagulant therapy. D-dimer levels and selected clinical parameters have been used to identify patients at low risk for recurrent VTE, who may safely discontinue antithrombotic therapy. We have used gene expression profiles to distinguish patients with a single VTE from patients with recurrent VTE. The purpose of this study was to extend this initial report and identify unique gene expression patterns from whole blood that correlate with different risk profiles for VTE recurrence. Methods. Patients with ≥1 prior VTE, with the first event occurring at age 18 years or older and >3 months from the most recent event were recruited for this study. Patients were allocated into 4 groups: (1) ‘low-risk’ patients had sustained ≥1 provoked VTE; (2) ‘moderate-risk’ patients had sustained 1 unprovoked VTE (with or without provoked VTE); (3) ‘high-risk’ patients had sustained ≥2 unprovoked VTE and had no evidence for antiphospholipid antibodies; and (4) antiphospholipid syndrome (APS) patients met established consensus criteria for APS. A similar number of individuals with no prior history of VTE were enrolled as a control population. Citrated plasma, serum and PAXgene RNA tubes were collected, processed and stored at −80°C until shipped to the CDC for analysis. Antiphospholipid testing was performed on all participants to confirm correct group distribution. Total RNA was isolated from whole blood drawn into PAXgene tubes. Following sample labeling and normalization, cRNA samples were hybridized to Illumina HT-12 Beadchips to assay whole genome gene expression with over 47,000 probes against human transcripts. Two hundred and twenty six unique samples passed initial quality control measures. Quality assessment of raw data was done using GenomeStudio. The raw data files were converted to a text file using the IlluminaExpression FileCreator in GenePattern and then log transformed, normalized and median-centered using Cluster. Both unsupervised (hierarchical clustering using Cluster) and supervised analyses (SAM) were used to identify genes that were differentially expressed between the groups. GATHER was used to help understand the biological processes and gene ontology of the gene lists generated by Cluster and SAM. Results. A total of 226 participants were enrolled into the study. Characteristics of the patient groups are summarized in the Table. Demographically, the groups were similar except that patients in the high-risk group tended to be older and were more likely male. The number of events per patient, and the proportion on anticoagulant therapy, increased with the risk group. Antiphospholipid antibodies were detected in several patients in each of the 3 non-APS VTE patient groups, but in most cases this was a single test positive; antiphospholipid antibodies were present in the majority of patients with APS, typically with more than one test positive (37 of 45 with complete testing, 82%). Preliminary analysis of the gene expression profiles using an unsupervised clustering by gene on the high-risk and low-risk groups identified multiple genes that distinguished the two groups, including 18 immune response genes identified by GATHER. These two patient groups were also distinguished by SAM analysis, and multiple genes in the MAPK signaling pathway that separated the two groups were identified by the KEGG pathways in GATHER. Additional analyses are being performed on all of the groups. Conclusions. Whole blood gene expression profiling can be used to develop profiles that distinguish patients with VTE who differ based on their risk of recurrent events. Individual genes identified in these profiles may provide biological insights into the molecular basis for recurrent VTE. Disclosures: Heit: Daiichi Sankyo: Honoraria; Ortho-McNeil Janssen: Honoraria; Covidien: Honoraria. Manco-Johnson:Octapharma AG: Consultancy; Bayer: Research Funding.

scholarly journals A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaotong Chen ◽  
Lintao Liu ◽  
Mengping Chen ◽  
Jing Xiang ◽  
Yike Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
Score Model

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.

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.

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.

scholarly journals Dose-dense and less dose-intense Total Therapy 5 for gene expression profiling-defined high-risk multiple myeloma

2016 ◽  
Vol 6 (7) ◽  
pp. e453-e453 ◽  
Author(s):  
Y Jethava ◽  
A Mitchell ◽  
M Zangari ◽  
S Waheed ◽  
C Schinke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Progression Free Survival ◽  
Drug Dosing ◽  
High Risk Patients ◽  
Major Predictor ◽  
Risk Patients ◽  
Total Therapy ◽  
Dose Dense

Abstract Multiple myeloma (MM) is a heterogeneous disease with high-risk patients progressing rapidly despite treatment. Various definitions of high-risk MM are used and we reported that gene expression profile (GEP)-defined high risk was a major predictor of relapse. In spite of our best efforts, the majority of GEP70 high-risk patients relapse and we have noted higher relapse rates during drug-free intervals. This prompted us to explore the concept of less intense drug dosing with shorter intervals between courses with the aim of preventing inter-course relapse. Here we report the outcome of the Total Therapy 5 trial, where this concept was tested. This regimen effectively reduced early mortality and relapse but failed to improve progression-free survival and overall survival due to relapse early during maintenance.

scholarly journals Identification of novel prognostic markers of survival time in high-risk neuroblastoma using gene expression profiles

Oncotarget ◽  
2020 ◽  
Vol 11 (46) ◽  
pp. 4293-4305
Author(s):  
Abdulazeez Giwa ◽  
Azeez Fatai ◽  
Junaid Gamieldien ◽  
Alan Christoffels ◽  
Hocine Bendou
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Survival Time ◽  
Prognostic Markers ◽  
Expression Profiles ◽  
Gene Expression Profiles

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.

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.

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