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.

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.

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.

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.

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.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

Gene expression profiles in whole blood and associations with metabolic dysregulation in obesity

2018 ◽  
Vol 12 (2) ◽  
pp. 204-213
Author(s):  
Amanda J. Cox ◽  
Ping Zhang ◽  
Tiffany J. Evans ◽  
Rodney J. Scott ◽  
Allan W. Cripps ◽  
...  
Keyword(s):  
Gene Expression ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Metabolic Dysregulation
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