scholarly journals DNA Methylation Profiling of Myeloma Trial Patients Reveals Specific Epigenetic Changes Associated with Outcome

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 804-804
Author(s):  
David C Johnson ◽  
Dil B Begum ◽  
Sidra Ellis ◽  
Amy L Sherborne ◽  
Amy Price ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Methylated Regions ◽  
Disease Biology ◽  
High Risk Disease ◽  
Pathway Analyses ◽  
Support Research

Abstract Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified (>95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P<0.01 (log-rank). Of these, 114 DMRs were located within 10kb of a gene transcription start site (TSS). Among these, several genes implicated in myeloma disease biology, such as immune cell-cell interaction genes (e.g. CD226) or stemness-associated transcription factors (e.g. PAX4) were identified to be differentially methylated. Using pathway analysis on all 589 DMRs, Gene Ontology biologic groups were enriched for positive regulation of proliferation, cell migration and cytoskeleton organisation (FDR P<0.05). This was further supported by enrichment of proliferative E2F1 transcription factor target structures (FDR P<0.05). Matched gene expression profiles have been generated and integrated analyses correlating epigenetic with GEP and genetic risk data and individual gene level methylation-expression associations will be presented at the meeting. This data is also being integrated with drug resistance profiles from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al, 2016). Conclusion Epigenetic mechanisms play a significant role in influencing tumour cell behaviour. We have identified here differentially methylated regions that are significantly associated with patient outcome. Pathway analyses suggest an epigenetic regulation of biologic mechanisms involved in high risk disease, such as proliferation and migration. Integration of epigenetic data with matched gene expression profiles is currently ongoing to delineate independent epigenetic biomarkers associated with high risk disease behaviour. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel support; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria. Jackson:MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Kaiser:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Other: Travel Support; BMS: Consultancy, Other: Travel Support; Chugai: Consultancy.

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 FcmR Shapes BCR Signaling in IgM-Positive Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2620-2620
Author(s):  
Alexandra da Palma Guerreiro ◽  
Cornelia Dorweiler ◽  
Ismini Halmer ◽  
Olaf Merkel ◽  
Elena Maria Hartmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Research Funding ◽  
Functional Role ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Malignant Cells ◽  
Support Research

Abstract Background: The Fc receptor for IgM (FcmR/ TOSO) is significantly overexpressed on chronic lymphocytic leukemia (CLL) cells from peripheral blood, but becomes down-regulated in the tumor microenvironment by e.g. CD40:CD40L interaction. Since the functional role of FcmR on lymphomagenesis is still not understood, we developed a conditional knockout mouse with B cell-specific FcmR-depletion. These mice were crossbred with the Eµ-TCL1 murine model, which develops a CLL-like phenotype. Results: The depletion of FcmR/TOSO in TCL1 mice (Eµ-Tcl1tg/wt FcmRfl/fl CD19cre/wt; further on called TCT) revealed a significantly shorter overall survival (296 days; n=40) compared to the TOSO expressing control mice (Eµ-Tcl1tg/wt FcmRwt/wt CD19cre/wt; TC; 344 days; n=106; Log-rank p<0.0001). In addition, these mice show a significantly higher blood leukocyte count and lower platelet and erythrocyte count. Leukocytes could be identified as CLL-characteristic leukemic CD19+/CD5+ B cells. Altogether TCT exhibited a faster progress of disease. Spleen immunohistochemistry revealed the transformation of most TCT (14/17 transformed) into an even more aggressive phenotype with increased splenomegaly and change in tissue and cell morphology compared to TC (9/9 not transformed). While characterizing these cells by flow cytometry, we identified a significantly higher expression of IgM on malignant B cells from TCT in comparison to TC mice. This finding indicates that the BCR itself might have a different contribution to lymphomagenesis in FcmR knock-out settings. Therefore, to validate the functional role of FcmR in the process of lymphomagenesis, we performed transcriptome profiling by RNA-Seq using splenic leukemic cells (CD19+ CD5+) from 36-week old TC (n=4) and TCT (n=4) mice. 2089 genes were found to be significantly modulated in the malignant cells of TCT mice, from which 1221 were downregulated and 868 showed an upregulation (significant change in mean expression; p<0.05). To investigate the role of IgM on TCT mice, purified malignant B cells were incubated for two hours with F(ab')2 goat anti-mouse IgM. Strikingly, TCT mice showed 3941 genes (2054 downregulated, 1887 upregulated) with significant difference in expression compared to TC (p<0.05). The gene expression profiles of the anti-IgM treated mice revealed a stronger regulation of BCR signalling in TCT mice, suggesting that FcmR represents an important factor in these processes. We examined the gene expression profiles, using Ingenuity Pathway Analysis Software. Analysis revealed that the most deregulated functions include interferon-signalling, recruitment of leukocytes, infection of cells and cellular movement. Conclusion: Here we present functional evidence that loss of FcmR results in increased IgM/BCR on the surface of non-switched leukemia. Moreover, malignant cells with loss of FcmR are more susceptible to BCR stimulation and show a signature of signalling pathways, which contribute to inflammation in B cell malignancies. Disclosures Fingerle-Rowson: MorphoSys: Employment. Pallasch:Gilead: Research Funding. Wendtner:Abbvie: Consultancy, Honoraria, Other: travel support, Research Funding; Mundipharma: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Genetech: Consultancy, Honoraria, Other: travel support, Research Funding; Janssen: Consultancy, Honoraria, Other: travel support, Research Funding; Pharmacyclics: Consultancy, Honoraria, Other: travel support, Research Funding; MorphoSys: Consultancy, Honoraria, Other: travel support, Research Funding; Roche: Consultancy, Honoraria, Other: travel support, Research Funding.

scholarly journals Age-related differences in monocyte DNA methylation and immune function in healthy Kenyan adults and children

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Katherine R. Dobbs ◽  
Paula Embury ◽  
Emmily Koech ◽  
Sidney Ogolla ◽  
Stephen Munga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Young Adults ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Innate Immune ◽  
Inflammatory Gene Expression ◽  
Cpg Sites ◽  
Age Related ◽  
Adults And Children

Abstract Background Age-related changes in adaptive and innate immune cells have been associated with a decline in effective immunity and chronic, low-grade inflammation. Epigenetic, transcriptional, and functional changes in monocytes occur with aging, though most studies to date have focused on differences between young adults and the elderly in populations with European ancestry; few data exist regarding changes that occur in circulating monocytes during the first few decades of life or in African populations. We analyzed DNA methylation profiles, cytokine production, and inflammatory gene expression profiles in monocytes from young adults and children from western Kenya. Results We identified several hypo- and hyper-methylated CpG sites in monocytes from Kenyan young adults vs. children that replicated findings in the current literature of differential DNA methylation in monocytes from elderly persons vs. young adults across diverse populations. Differentially methylated CpG sites were also noted in gene regions important to inflammation and innate immune responses. Monocytes from Kenyan young adults vs. children displayed increased production of IL-8, IL-10, and IL-12p70 in response to TLR4 and TLR2/1 stimulation as well as distinct inflammatory gene expression profiles. Conclusions These findings complement previous reports of age-related methylation changes in isolated monocytes and provide novel insights into the role of age-associated changes in innate immune functions.

scholarly journals Identification of Prognostic Markers in Cholangiocarcinoma Using Altered DNA Methylation and Gene Expression Profiles

2020 ◽  
Vol 11 ◽  
Author(s):  
Nitish Kumar Mishra ◽  
Meng Niu ◽  
Siddesh Southekal ◽  
Prachi Bajpai ◽  
Amr Elkholy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Prognostic Markers ◽  
Expression Profiles ◽  
Gene Expression Profiles

Prediction of Cytogenetic Abnormalities in Multiple Myeloma Based on Gene Expression Profiles

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 629-629
Author(s):  
Yiming Zhou ◽  
Qing Zhang ◽  
Christoph Heuck ◽  
Owen Stephens ◽  
Erming Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Research Funding ◽  
Prediction Accuracy ◽  
Plasma Cells ◽  
Chromosome Region ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Set ◽  
Expression Levels ◽  
The Mean

Abstract Abstract 629 Background: Cytogenetic abnormalities (CA) are a hallmark of multiple myeloma (MM) and other cancers and are commonly used as clinical parameters for determining disease stage and guiding therapy decisions. Traditional techniques, including fluorescence in situ hybridization (FISH) and karyotyping, and the recently developed array-based comparative genomic hybridization are expensive and time consuming. As gene expression profiling (GEP) is becoming more integrated in the diagnostic workup of MM and is increasingly being used for risk stratification as well as tailoring therapy, we are presented with vast amounts of data that should reflect disease associated alterations of the genome. We therefore sought to develop a GEP based vitual CA (vCA) model to predict CA in MM. Methods/Results: We determined genome-wide gene expression profiles and DNA copy numbers (CNs) in purified plasma cell samples obtained from 92 newly diagnosed MM patients, using the Affymetrix GeneChip and the Agilent aCGH platforms, respectively. We identified 1,114 CN-sensitive genes by Pearson's correlation coefficient (PCC) of gene expression levels and the copy numbers of the corresponding DNA loci, keeping the false discovery rate to <5%. On the basis of these CN-sensitive genes, we developed a vCA model for predicting CA in MM patients by means of GEP. The model focuses particularly on chromosomes 3, 5, 7, 9, 11, 13, 15, 19, and 21, as well as the 1p, 1q, and 6q segments, which are the most commonly altered chromosome regions in MM plasma cells. The reference CA (rCA) of a given chromosome region were determined by the mean values of signals of aCGH probes located in that region. The values of rCA could be used to distinguish among amplification, deletion, and normal. The predicted CA (pCA) of a given chromosome region were determined by the following procedures. First, we calculated the mean expression levels of CN-sensitive genes within the region. Then, by training the model in a GEP data set with 92 MM samples, we set the cutoff value of the mean expression levels of CN-sensitive genes for each chromosome region in order to obtain pCA that were most consistent with rCA in terms of the Matthews correlation coefficient, a measure of the quality of binary (two-class) classifications. The mean prediction accuracy was 0.88 (0.59–0.99) when the model was applied to the training data set. To check for overfitting in the vCA model, we applied the model to an independent data set of 23 MM samples for which both GEP and aCGH data were available. The mean prediction accuracy was 0.89 (0.74–1.00), which indicated that overfitting was negligible if present at all. We further validated the model with a FISH data set compiled from 262 independent MM samples for which both FISH records and GEP data were available. The mean prediction accuracy was 0.87. The consistency between vCA-predicted chromosomal alterations and findings of karyotyping dropped to 0.65. However, this underperformance could be due to the fact that karyotyping is limited by the low proliferation rate of terminally differentiated plasma cells in vitro. Conclusion: Our results provide a proof of concept that GEP data alone can reveal all the information provided by conventional cytogenetic techniques. We show that re-purposing gene expression data using our model is a fast and economical way to obtain cytogenetic information that is accurate and can be used for diagnosis and observation in MM and potentially other malignancies. GEP can serve as a one-stop genomic data source for information from the level of specific genes to whole chromosomes. Disclosures: Barlogie: Celgene: Consultancy, Honoraria, Research Funding; IMF: Consultancy, Honoraria; MMRF: Consultancy; Millennium: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy; Novartis: Research Funding; NCI: Research Funding; Johnson & Johnson: Research Funding; Centocor: Research Funding; Onyx: Research Funding; Icon: Research Funding. Shaughnessy:Myeloma Health, Celgene, Genzyme, Novartis: Consultancy, Employment, Equity Ownership, Honoraria, Patents & Royalties.

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.

scholarly journals Global Modulation in DNA Epigenetics during Pro-Inflammatory Macrophage Activation

2019 ◽  
Author(s):  
Nikhil Jain ◽  
Tamar Shahal ◽  
Tslil Gabrieli ◽  
Noa Gilat ◽  
Dmitry Torchinsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Single Molecule ◽  
Transcriptional Control ◽  
Excision Repair ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Inflammatory Activation ◽  
Methylation Patterns

AbstractDNA methylation patterns create distinct gene expression profiles. These patterns are maintained after cell division, thus enabling the differentiation and maintenance of multiple cell types from the same genome sequence. The advantage of this mechanism for transcriptional control is that chemical-encoding allows to rapidly establish new epigenetic patterns “on-demand” through enzymatic methylation and de-methylation of DNA. Here we show that this feature is associated with the fast response of macrophages during their pro-inflammatory activation. By using a combination of mass spectroscopy and single-molecule imaging to quantify global epigenetic changes in the genomes of primary macrophages, we followed three distinct DNA marks (methylated, hydroxymethylated and unmethylated), involved in establishing new DNA methylation patterns during pro-inflammatory activation. The observed epigenetic modulation together with gene expression data generated for the involved enzymatic machinery, may suggest that de-methylation upon LPS-activation starts with oxidation of methylated CpGs, followed by excision-repair of these oxidized bases and their replacement with unmodified cytosine.

scholarly journals Comparison of visceral adipose tissue DNA methylation and gene expression profiles in female adolescents with obesity

2019 ◽  
Author(s):  
Matthew D. Barberio ◽  
Evan P. Nadler ◽  
Samantha Sevilla ◽  
Rosemary Lu ◽  
Brennan Harmon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Adipose Tissue ◽  
Visceral Adipose Tissue ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Akt Signaling ◽  
Adolescent Females ◽  
Akt Signaling Pathway ◽  
Visceral Adipose

AbstractBackgroundEpigenetic changes in visceral adipose tissue (VAT) with obesity and their effects on gene expression are poorly understood, especially during emergent obesity in youth. The current study tested the hypothesis that methylation and gene expression profiles of key growth factor and inflammatory pathways such as PI3K/AKT signaling are altered in VAT from obese compared to non-obese youth.MethodsVAT samples from adolescent females grouped as Lean (L; n=15; age=15±3 yrs, BMI=21.9±3.0 kg/m2) or Obese (Ob; n=15, age=16±2 yrs, BMI=45.8±9.8 kg/m2) were collected. Global methylation (n=20) and gene expression (N=30) patterns were profiled via microarray and interrogated for differences between groups by ANCOVA (p<0.05), followed by biological pathway analysis.ResultsOverlapping differences in methylation and gene expression in 317 genes were found in VAT from obese compared to lean groups. PI3K/AKT Signaling (p=1.83×10−6; 10/121 molecules in dataset/pathway) was significantly overrepresented in Ob VAT according to pathway analysis. mRNA upregulations in the PI3K/AKT Signaling Pathway genes TFAM (p=0.03; Fold change=1.8) and PPP2R5C (p=0.03, FC=2.6) were confirmed via qRT-PCR.ConclusionOur analyses show obesity-related differences in DNA methylation and gene expression in visceral adipose tissue of adolescent females. Specifically, we identified methylation site/gene expression pairs differentially regulated and mapped these differences to PI3K/AKT signaling, suggesting that PI3K/AKT signaling pathway dysfunction in obesity may be driven in part by obesity-related changes in DNA methylation.

scholarly journals Biological interpretations of transcriptomic profiles in mammalian oocytes and embryos

Reproduction ◽  
2008 ◽  
Vol 135 (2) ◽  
pp. 129-139 ◽  
Author(s):  
S L Rodriguez-Zas ◽  
K Schellander ◽  
H A Lewin
Keyword(s):  
Gene Expression ◽  
Systems Approach ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Developmental Competence ◽  
Complex Processes ◽  
Genetic And Environmental Factors ◽  
Pathway Analyses ◽  
Microarray Studies

The characterization of gene-expression profiles in oocytes and embryos is critical to understand the influence of genetic and environmental factors on preimplantation and fetal development. Numerous gene-expression microarray studies using different platforms and species are offering insights into the biological processes extensively represented among the genes exhibiting differential expression. Major advances on understanding the direct relationship between gene expression and developmental competence are being reported. Integration of information across studies using meta-analysis techniques can increase the precision and accuracy to identify expression profiles associated with embryo development. Gene network and pathway analyses are offering insights into gene interactions and expression profiles of embryos. All these advances are cementing the way toward a comparative and systems approach to understanding the complex processes underlying vertebrate development.

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