Identification of a Meta-Gene Network Associated with Metformin Sensitivity and Clinical Outcomes in Double Hit and Double Expressor Lymphomas

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2431-2431 ◽  
Author(s):  
Reem Karmali ◽  
Annesha Basu ◽  
Jeffrey A Borgia ◽  
Leo I. Gordon ◽  
Parameswaran Venugopal ◽  
...  

Abstract Background The "double hit" (DH) lymphomas that harbor a c-myc mutation and BCL2 translocation, or "double protein expressor" (DP) lymphomas that over-express c-myc and BCL2 proteins in the absence of a detectable mutation, have amongst the worst clinical outcomes as compared to patients with diffuse large B-cell lymphomas (DLBCL) that lack upregulation of the c-myc oncogene. Metformin can down-regulate translation of c-myc, making it an appropriate anti-cancer drug to explore in c-myc+ lymphomas. Furthermore, amethod to identify DH/DP patients most likely to benefit from metformin treatment has clinical relevance. Methods Within a publicly available gene expression array data set of R-CHOP treated DLBCL (n=232; GSE10846), a subset of DH/DP patients were defined as having above median expression of myc and BCL2 and below median expression of BCL6 as previously published by Horn et al. Survival analysis, significance analysis of microarrays (SAM) and gene set analysis (GSA) were performed characterizing the clinical, individual gene and biological ontology differences between DH/DP and non-DH/DP populations. Expression array data from a study testing metformin effects on THP-1 monocyte cells was reanalyzed using SAM and GSA as well. Changes in individual gene expression and overlapping ontological themes common to both GSA analyses of metformin effects on THP-1 cells and DH/DP characterization were identified. Genes with differential expression (DE) in both groups were evaluated topologically using a protein-protein interaction database to determine if any gene products had previously observed direct interactions. Network community detection identified tightly coupled signaling modules linking co-expression to mechanism. The resulting metformin-DH/DP network metagene was evaluated by k-means, clustering tumor samples into two groups over the metagene members in an independent data set of R-CHOP treated DLBCL patients (n=249; GSE32918) with differences in overall survival (OS) determined by log-rank. Results Of the 232 DLBCL patients treated with R-CHOP, 26 fit the criteria for DH/DP and had significantly lower OS (HR = 2.96; p < 0.001). In DH/DP tumors, 2780 genes had DE (2208 up-regulated; 572 down-regulated), enriched for biological processes related to transcription, metabolism and cytokine production and down-regulated for processes related to immune response, cell signaling, vascular development and proliferation (Fig. 1A). Analysis of metformin treated THP-1 cells relative to control identified 7123 genes with DE. Biological themes common to metformin treatment and DH/DP specific biology were identified including mitochondrial biogenesis, alternate splicing, and hormone secretion (Fig. 1A-B; highlighted in red). The intersection of genes with DE in metformin treated and DH/DP data sets identified 102 genes with direct interaction within a protein interaction network. Of the 19 communities detected by analyzing the resulting network topology, 3 showed significant correlation to survival in the GSE10846 data set (Fig. 2A, in red), forming a metformin-DH/DP metagene (Met-DH/DP-MG; n = 29 genes total). This metagene was validated by applying it to an independent cohort of R-CHOP treated DLBCL patients (n = 249), demonstrating 2 cluster groups (cluster 1, n=178; cluster 2, n=71; Fig. 2B) with differences in OS (HR = 1.61; p < 0.001; Fig. 2C). Conclusion We have identified a metagene of interacting proteins associated with both metformin therapeutic effect and OS in DH/DP patients. This offers a potential method for selecting patients most likely to benefit from metformin therapy and identifies mechanistic avenues by which metformin treatment may specifically benefit DH/DP patients. As such, in vitro studies using DH cell lines and a phase I/II clinical trial exploring chemo-immunotherapy with metformin as an adjunct in DH/DP lymphomas are underway. Disclosures No relevant conflicts of interest to declare.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1563-1563
Author(s):  
Paola E. Leone ◽  
Brian A. Walker ◽  
David Gonzalez ◽  
Matthew Jenner ◽  
Fiona M. Ross ◽  
...  

Abstract Deletions on chromosome 13 are thought to be one of the most important prognostic features in Multiple Myeloma (MM). The biology underlying this is, however, uncertain. Chromosome 13 abnormalities have been evaluated conventionally by FISH using probes for 13q14, covering the retinoblastoma gene (RB1) region. Typically, for recurrent regions of loss of heterozygosity (LOH) it is possible to map a minimally deleted region within which an important gene may be located. This should be the case with 13q−, or alternatively there may be linkage with another genetic lesion, which could be contributing to the poor prognosis. Following the implementation of high-density single-nucleotide polymorphism (SNP) array, it is now possible to genotype the whole human genome with a mapping resolution of less than 50 Kb. Thus, the SNP array approach offers an opportunity to analyze both copy number abnormalities and LOH simultaneously. The aim of this study was to determine the numerical alterations, LOH and changes in the gene expression profile of the chromosome 13 in MM, and its possible association with other genetic events. For this purpose, we analyzed 17 patients included on the Myeloma IX trial with deletion of 13q14 compared with 22 samples without deletion, using Affymetrix 50K SNP arrays and Affymetrix U133 Plus 2 expression array. IGH translocations and 13q deletion were determined by FISH. dChipSNP and WGSA programs were used to analyze the data. With respect to 13q14, there was 100% correlation between FISH and SNP array results. 16 out of 17 cases with deletion of the RB1 gene by FISH analysis showed loss of 13q arm by SNP array, demonstrating that loss of the whole chromosome 13 is responsible for 13q deletions found in MM in &gt;90% of cases, with only one case showing a defined region of deletion of chromosome 13 (13q14.11–13q21.2). Using gene expression arrays we could not define a specific pattern characteristic of expression loss in genes at 13q. Lower RB1 expression levels were not only restricted to cases with del(13). However, samples containing IGH translocations (t(11;14) and t(4;14)) without del(13) showed up to 4 times more RB1 expression, suggesting that MM evolution in cases containing IGH translocations is independent of RB1 expression. Interestingly, the hyperdiploid cases with and without del(13) expressed similar level of RB1. We also investigated whether other key cell cycle regulatory genes were associated with del(13); in particular, 4 cases showed 9p21 LOH by SNP array and no different gene expression levels, which suggest that LOH does not seem to be a mechanism of lost of expression of CDKN2A, CDKN2B and p14/ARF. We could not find any significant correlation with del(13) and expression of cell cycle regulatory genes, apart from 8/17 samples with del(13) that had low expression of p53 gene, including 6 t(4;14) cases and 2 t(11;14) cases. Also, 2 cases without monosomy 13 (1 with t(4;14) and 1 with t(11;14)), showed low p53 expression levels. However, SNP array data did not show any deletion at 17p in 38 cases, with the exception of a case with monosomy 13 and t(11;14) in which SNP array data showed loss at 17pter-17q21.2 and FISH detected p53 deletion. Further investigation between the association of p53 and del(13) are ongoing and maybe useful in defining the biology of this poor subgroup of patients.


2019 ◽  
Vol 37 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Chulin Sha ◽  
Sharon Barrans ◽  
Francesco Cucco ◽  
Michael A. Bentley ◽  
Matthew A. Care ◽  
...  

Purpose Biologic heterogeneity is a feature of diffuse large B-cell lymphoma (DLBCL), and the existence of a subgroup with poor prognosis and phenotypic proximity to Burkitt lymphoma is well known. Conventional cytogenetics identifies some patients with rearrangements of MYC and BCL2 and/or BCL6 (double-hit lymphomas) who are increasingly treated with more intensive chemotherapy, but a more biologically coherent and clinically useful definition of this group is required. Patients and Methods We defined a molecular high-grade (MHG) group by applying a gene expression–based classifier to 928 patients with DLBCL from a clinical trial that investigated the addition of bortezomib to standard rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. The prognostic significance of MHG was compared with existing biomarkers. We performed targeted sequencing of 70 genes in 400 patients and explored molecular pathology using gene expression signature databases. Findings were validated in an independent data set. Results The MHG group comprised 83 patients (9%), with 75 in the cell-of-origin germinal center B-cell-like group. MYC rearranged and double-hit groups were strongly over-represented in MHG but comprised only one half of the total. Gene expression analysis revealed a proliferative phenotype with a relationship to centroblasts. Progression-free survival rate at 36 months after R-CHOP in the MHG group was 37% (95% CI, 24% to 55%) compared with 72% (95% CI, 68% to 77%) for others, and an analysis of treatment effects suggested a possible positive effect of bortezomib. Double-hit lymphomas lacking the MHG signature showed no evidence of worse outcome than other germinal center B-cell-like cases. Conclusion MHG defines a biologically coherent high-grade B-cell lymphoma group with distinct molecular features and clinical outcomes that effectively doubles the size of the poor-prognosis, double-hit group. Patients with MHG may benefit from intensified chemotherapy or novel targeted therapies.


Author(s):  
Golzar Farhadi ◽  
Jamal Fayazi ◽  
Hedayat Allah Roshanfekr ◽  
Mahmoud Nazari ◽  
Elham Behdani

Background: Oocyte maturation begins at the embryonic stage and continues throughout life. The effect of Follicle- Stimulating hormone (FSH) on gene of genes was evaluated using GEO access codes for the data set GSE38345. Materials and Methods: The microarray data containing the gene expression information from cow oocytes show that their maturation is influenced by FSH. Data analysis was performed using GEO2R. After identifying the genes and examining the different genes expressed, two gene groups with increased and decreased expression were identified. The interaction of each of the gene groups was examined using the STRING database, based on the co-expression information. The meaningful sub networks were explored using the Clusterone software. Gene ontology was performed using the comparative GO database. The miRNA-mRNA interaction network was also studied based on the miRWalk database. Finally, meaningful networks and subnets obtained by the Cytoscape software, were designed. Results: Comparison of oocyte gene expression data between the pre-maturation and postmaturation stages after treatment with FSH revealed 5958 genes with increased expression and 4275 genes with decreased expression. Examination of the protein interaction network among the set of increased and decreased expression genes based on string information revealed 262 genes with increased expression and 147 genes with decreased expression (high confidence (0.7) data). RPS3, NUSAP1, TBL3, and ATP5H showed increased expression and were effective in the positive regulation of rRNA processing, cell division, mitochondrial ATP synthesis coupled proton, and in oxidative phosphorylation and progesterone-mediated functions. WDR46 and MRPL22 showed decreased expression, which were important in the regulation of SRP-dependent co-translational proteins targeting the membrane, RNA secondary structure, unwinding, and functional pathways of ribosomal and RNA polymerase. The most important miRNA genes in the protein network of increased and decreased gene expression were bta-miR-10b-5p and miR-29b-2-5p. Conclusion: Examination of the genes expressed in the oocyte maturation pathway revealed nuclear, mitochondrial, and miRNA genes. Increasing and decreasing gene expression helps maintain equilibrium, which can be a biological marker.


2021 ◽  
Vol 1 ◽  
Author(s):  
Niloofar Aghaieabiane ◽  
Ioannis Koutis

High-throughput technologies such as DNA microarrays and RNA-sequencing are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed to Gene Co-expression Networks (GCNs). In a GCN, nodes correspond to genes, and the weight of the connection between two nodes is a measure of similarity in the expression behavior of the two genes. In general, GCN construction and analysis includes three steps; 1) calculating a similarity value for each pair of genes 2) using these similarity values to construct a fully connected weighted network 3) finding clusters of genes in the network, commonly called modules. The specific implementation of these three steps can significantly impact the final output and the downstream biological analysis. GCN construction is a well-studied topic. Existing algorithms rely on relatively simple statistical and mathematical tools to implement these steps. Currently, software package WGCNA appears to be the most widely accepted standard. We hypothesize that the raw features provided by sequencing data can be leveraged to extract modules of higher quality. A novel preprocessing step of the gene expression data set is introduced that in effect calibrates the expression levels of individual genes, before computing pairwise similarities. Further, the similarity is computed as an inner-product of positive vectors. In experiments, this provides a significant improvement over WGCNA, as measured by aggregate p-values of the gene ontology term enrichment of the computed modules.


PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e30629 ◽  
Author(s):  
Jean-Baptiste Veyrieras ◽  
Daniel J. Gaffney ◽  
Joseph K. Pickrell ◽  
Yoav Gilad ◽  
Matthew Stephens ◽  
...  

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