Impact of gene expression profiling-based risk stratification in patients with myeloma receiving initial therapy with lenalidomide and dexamethasone.
ABSTRACT: Detection of specific chromosomal abnormalities by FISH and metaphase cytogenetics allows risk stratification in multiple myeloma; however, gene expression profiling (GEP) based signatures may enable more specific risk categorization. We examined the utility of 2 GEP-based risk stratification systems among patients undergoing initial therapy with lenalidomide in the context of a phase 3 trial. Among 45 patients studied at baseline, 7 (16%) and 10 (22%), respectively, were high-risk using the GEP70 and GEP15 signatures. The median overall survival for the GEP70 high-risk group was 19 months versus not reached for the rest (hazard ratio = 14.1). Although the medians were not reached, the GEP15 also predicted a poor outcome among the high-risk patients. The C-statistic for the GEP70, GEP15, and FISH based risk stratification systems was 0.74, 0.7, and 0.7, respectively. Here we demonstrate the prognostic value for GEP risk stratification in a group of patients primarily treated with novel agents. This trial was registered at www.clinicaltrials.gov as #NCT00098475.
Project description:Detection of specific chromosomal abnormalities by FISH and metaphase cytogenetics allows risk stratification in multiple myeloma (MM); however, gene expression profiling (GEP) based signatures may enable more specific risk categorization. We examined the utility of two GEP-based risk stratification systems among patients undergoing initial therapy with lenalidomide in the context of a phase 3 trial. The E4A03 clinical trial randomized patients with previously untreated MM to lenalidomide and either standard-dose dexamethasone. Baseline bone marrow samples were obtained from consenting patients. The marrow aspirates were subjected to a fully automated ROBOSEP cell separation system that utilizes immunomagnetic technology to positively select for CD 138+ cells. The purity of the sorting was confirmed by 3-color immunofluorescent slide based assessment on the sorted cells. The plasma cell gene expression proﬁles were analyzed using U133 Plus 2.0 array. All samples were run individually with no pooling. The GEP-70 signature was determined as previously described, using log2 transformed raw MAS 5.0 signals. The GEP15 classification was performed as previously described, with the patients in highest quartile for the risk score being considered as high risk.
Project description:All cases of clinical myeloma (CMM) are preceded by an asymptomatic monoclonal gammopathy (AMG), classified as either monoclonal gammopathy of undetermined significance (MGUS) or asymptomatic multiple myeloma (AMM). We analyzed data from AMG patients (n = 331) enrolled in a prospective, observational clinical trial (S0120). Baseline data from clinical variables, gene expression profiles (GEP) of purified tumor cells, and findings of magnetic resonance imaging (MRI) were correlated with the risk of progression to CMM requiring therapy. GEP of purified tumor cells revealed that all molecular subtypes of CMM are also represented in the AMG phase. An increased risk score (>-0.26) (based on a 70-gene signature, GEP70) was an independent predictor of the risk of progression to CMM. Combination of elevated serum free light chain, M-spike, and GEP70 risk score identified a subset with high risk (67% at 2 years) of progression to CMM requiring therapy. Importantly, absence of these factors in AMM patients predicted low risk similar to MGUS. Detection of multiple (>1) focal lesions by MRI also conferred an increased risk of progression. These data demonstrate that signatures associated with high-risk CMM impact disease risk and support inclusion of genomic analysis in the clinical management of AMGs.
Project description:Gene expression profiling (GEP) of purified plasma cells 48 hours after thalidomide and dexamethasone test doses showed these agents' mechanisms of action and provided prognostic information for untreated myeloma patients on Total Therapy 2 (TT2). Bortezomib was added in Total Therapy 3 (TT3), and 48 hours after bortezomib GEP analysis identified 80 highly survival-discriminatory genes in a training set of 142 TT3A patients that were validated in 128 patients receiving TT3B. The 80-gene GEP model (GEP80) also distinguished outcomes when applied at baseline in both TT3 and TT2 protocols. In context of our validated 70-gene model (GEP70), the GEP80 model identified 9% of patients with a grave prognosis among those with GEP70-defined low-risk disease and 41% of patients with favorable prognosis among those with GEP70-defined high-risk disease. PMSD4 was 1 of 3 genes common to both models. Residing on chromosome 1q21, PSMD4 expression is highly sensitive to copy number. Both higher PSMD4 expression levels and higher 1q21 copy numbers affected clinical outcome adversely. GEP80 baseline-defined high risk, high lactate dehydrogenase, and low albumin were the only independent adverse variables surviving multivariate survival model. We are investigating whether second-generation proteasome inhibitors (eg, carfilzomib) can overcome resistance associated with high PSMD4 levels.
Project description:The purpose of this study was to take samples of bone marrow and blood to learn more about monoclonal gammopathy or myeloma without symptoms. Specifically, the researchers want to understand more about genetic changes in the tumor and about the response of immune systems to a tumor. Overall design: All cases of clinical myeloma (CMM) are preceded by an asymptomatic monoclonal gammopathy (AMG), classified as either monoclonal gammopathy of undetermined significance (MGUS) or asymptomatic multiple myeloma (AMM). We analyzed data from AMG patients (n 5 331) enrolled in a prospective, observational clinical trial (S0120). Baseline data from clinical variables, gene expression profiles (GEP) of purified tumor cells, and findings of magnetic resonance imaging (MRI) were correlated with the risk of progression to CMM requiring therapy. GEP of purified tumor cells revealed that all molecular subtypes of CMM are also represented in the AMG phase. An increased risk score (>-0.26) (based on a 70-gene signature, GEP70) was an independent predictor of the risk of progression to CMM. Combination of elevated serum free light chain, M-spike, and GEP70 risk score identified a subset with high risk (67% at 2 years) of progression to CMM requiring therapy. Importantly, absence of these factors in AMM patients predicted low risk similar to MGUS. Detection of multiple (>1) focal lesions by MRI also conferred an increased risk of progression. These data demonstrate that signatures associated with high-risk CMM impact disease risk and support inclusion of genomic analysis in the clinical management of AMGs.
Project description:Patients with multiple myeloma have variable survival and require reliable prognostic and predictive scoring systems. Currently, clinical and biological risk markers are used independently. Here, International Staging System (ISS), fluorescence in situ hybridization (FISH) markers, and gene expression (GEP) classifiers were combined to identify novel risk classifications in a discovery/validation setting. We used the datasets of the Dutch-Belgium Hemato-Oncology Group and German-speaking Myeloma Multicenter Group (HO65/GMMG-HD4), University of Arkansas for Medical Sciences-TT2 (UAMS-TT2), UAMS-TT3, Medical Research Council-IX, Assessment of Proteasome Inhibition for Extending Remissions, and Intergroupe Francophone du Myelome (IFM-G) (total number of patients: 4750). Twenty risk markers were evaluated, including t(4;14) and deletion of 17p (FISH), EMC92, and UAMS70 (GEP classifiers), and ISS. The novel risk classifications demonstrated that ISS is a valuable partner to GEP classifiers and FISH. Ranking all novel and existing risk classifications showed that the EMC92-ISS combination is the strongest predictor for overall survival, resulting in a 4-group risk classification. The median survival was 24 months for the highest risk group, 47 and 61 months for the intermediate risk groups, and the median was not reached after 96 months for the lowest risk group. The EMC92-ISS classification is a novel prognostic tool, based on biological and clinical parameters, which is superior to current markers and offers a robust, clinically relevant 4-group model.
Project description: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.
Project description:As part of Total Therapy (TT) 3b, baseline marrow aspirates were subjected to two-color flow cytometry of nuclear DNA content and cytoplasmic immunoglobulin (DNA/CIG) as well as plasma cell gene expression profiling (GEP). DNA/CIG-derived parameters, GEP and standard clinical variables were examined for their effects on overall survival (OS) and progression-free survival (PFS). Among DNA/CIG parameters, the percentage of the light chain-restricted (LCR) cells and their cytoplasmic immunoglobulin index (CIg) were linked to poor outcome. In the absence of GEP data, low CIg <2.8, albumin <3.5?g/dl and age ?65 years were significantly associated with inferior OS and PFS. When GEP information was included, low CIg survived the model along with GEP70-defined high risk and low albumin. Low CIg was linked to beta-2-microglobulin >5.5?mg/l, a percentage of LCR cells exceeding 50%, C-reactive protein ?8?mg/l and GEP-derived high centrosome index. Further analysis revealed an association of low CIg with 12 gene probes implicated in cell cycle regulation, differentiation and drug transportation from which a risk score was developed in TT3b that held prognostic significance also in TT3a, TT2 and HOVON trials, thus validating its general applicability. Low CIg is a powerful new prognostic variable and has identified potentially drug-able targets.
Project description:Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.
Project description:GEP class prediction in association with CI-FISH (42 candidate genes) and patient MRD stratification Combined Interphase FISH (CI-FISH) for 42 candidate genes and Single Nucleotide Polymorphism (SNP) arrays were applied in a series of 51 T-ALL patients enrolled in Italy into the AIEOP-BFM ALL2000 protocol and stratified by Minimal Residual Disease (MRD). In 40 cases gene expression profiles (GEP) (Affymetrix HU-133 Plus 2.0 arrays) were available. This submission represents the gene expression component of the study
Project description:The genome of multiple myeloma (MM) cells is extremely unstable, characterized by a complex combination of structure and numerical abnormalities. It seems that there are several "myeloma subgroups" which differ in expression profile, clinical manifestations, prognoses and treatment response. In our previous work, the list of 35 candidate genes with a known role in carcinogenesis and associated with centrosome structure/function was used as a display of molecular heterogeneity with an impact in myeloma pathogenesis. The current study was devoted to establish a risk stratification model based on the aforementioned candidate genes.A total of 151 patients were included in this study. CD138+ cells were separated by magnetic-activated cell sorting (MACS). Gene expression profiling (GEP) and Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) were performed on plasma cells (PCs). All statistical analyses were performed using freeware R and its additional packages. Training and validation cohort includes 73 and 78 patients, respectively.We have finally established a model that includes 12 selected genes (centrosome associated gene pattern, CAGP) which appears to be an independent prognostic factor for MM stratification. We have shown that the new CAGP model can sub-stratify prognosis in patients without TP53 loss as well as in IMWG high risk patients' group.We assume that newly established risk stratification model complements the current prognostic panel used in multiple myeloma and refines the classification of patients in relation to the disease risks. This approach can be used independently as well as in combination with other factors.