Gene Expression Profiles Document That Recently- and Previously-Divided CLL Fractions Represent a Continuum but Suggest Differing Modes of Activation for These Fractions in U-CLL and M-CLL

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 317-317
Author(s):  
Xiao J. Yan ◽  
Wentian Li ◽  
Sophia Yancopoulos ◽  
Igor Dozmorov ◽  
Carlo Calissano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Death ◽  
Cell Signaling ◽  
Lymphoid Tissue ◽  
Research Funding ◽  
Expression Profiles ◽  
Lymphocytic Leukemia ◽  
Gene Set Enrichment Analysis ◽  
P Value ◽  
Gene Sets

Abstract Abstract 317 By using reciprocal densities of surface membrane CXCR4 and CD5, chronic lymphocytic leukemia (CLL) B cells can be divided into 3 fractions indicating time since last division (proliferative, intermediate, and resting). It has been suggested that cells in these fractions represent a continuum from resting to intermediate to proliferative. In this study, we made intraclonal gene expression profile (GEP) comparisons of these fractions from 17 CLL patients to try to confirm this notion and interclonal comparisons between U-CLL and M-CLL patients to determine if pathways involved in the actions of these fractions differed between patient subgroups. PBMCs from 8 U-CLL and 9 M-CLL patients were sorted into 3 fractions (CD19+CD3−CD5hiCXCR4lo, PROLIF), (CD19+CD3−CD5intCXCR4int, INTERM), and (CD19+CD3−CD5loCXCR4hi, REST); RNA was purified from each, and gene expression microarrays using Illumina HumanHT12 beadchips performed. To determine differentially expressed genes in intraclonal comparisons, expression value ratios for fractions from each patient were computed, log-transformed, and Student t-test performed using R (www.r-project.org); for interclonal comparisons, raw GEP data between subpopulations were compared: U-PROLIF and M-PROLIF, and U-REST and M-REST. Sets of significant genes (≥1.5 fold change and P<0.01) were analyzed using Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). Upon plotting intraclonal average log ratios of PROLIF/INTERM vs INTERM/REST, it was clear that gene expression levels changed in the same direction, i.e. PROLIF>INTERM>REST, or PROLIF<INTERM<REST, consistent with a continuum between the 3 fractions. Within this pattern, 36 genes were significant for both plotted ratios. Of these, 29 were overexpressed, along with CD5; CD68, ITGAX, CCND2, CRIP1 and LGALS1 were the highest. Functional analysis using IPA showed these genes to be related to NFkB signaling and cell trafficking. Seven genes (ADARB1, BACH2, CNTNAP2, HRK, RHPN2, PRPML, and RXPA) were significantly downregulated, along with CXCR4. Next we characterized GEP differences between the PROLIF and REST fractions, identifying 390 genes up-regulated in PROLIF and 244 in REST. The top 5 upregulated PROLIF genes were CD68, LY96, ITGAX, CCND2 and CRIP1, and the top 5 REST genes were BACH2, CXCR4, ADARB1, RHPN2 and HRK. Functionally, the upregulated PROLIF genes were related to BCR signaling, cytokines (IFNa, IL12), NFkB, and Akt, whereas the upregulated REST genes related to BCL2, cell death and cell movement. By GSEA, 813/881 gene sets, defined by expression neighborhoods centered on cancer associated genes, were upregulated in the PROLIF with 436 gene sets significant at a false discovery rate (FDR) <10%; 206 sets were significantly enriched with p value <0.01. For the REST, 68/881 gene sets were upregulated, with none significant even at FDR <25%. Finally, we examined PROLIF and REST fractions from U-CLL vs M-CLL patients. In this interclonal analysis, 93 genes were significantly different between U-PROLIF and M-PROLIF. The top 5 in U-PROLIF were MSI2, TGFBR3, TP53I3, RGCC and IGSF3, and the top 5 in M-PROLIF were MTSS1, BACE2, BRI3BP, AP3B1 and UBE2G2. Similarly, there were 125 genes that were significantly different between U-REST and M-REST. The top 5 in U-REST were DUSP26, CLEC2B, MDK, and EGR2 and in M-REST were NAPSA, RAB24, TARDBP, KCNN4 and ADD3. Interestingly, U-PROLIF and M-PROLIF differed in pathway assignments, with upregulated genes in U-PROLIF contributing to cell signaling and activation, particularly implicating Akt, ERK and P38MAPK. The intraclonal gene GEP analysis on these 3 fractions confirms that CLL clones contain a spectrum of cells that transition in a sequential manner from PROLIF to INTERM to REST fractions. Functional analyses show that genes upregulated in PROLIF correlate with cell signaling and proliferation, while genes upregulated in REST relate to cell death. Thus the PROLIF fraction is enriched in recently divided cells that likely exit from lymphoid tissue and the REST in older, less vital cells that either traffic to lymphoid tissue or die. The interclonal analysis implies that the stimuli and/or the responses of cells in the PROLIF and REST fractions differ between U-CLL and M-CLL. This last novel finding suggests either distinct cells of origin or distinct activation pathways for the IGHV-defined CLL subsets. Disclosures: Barrientos: gilead and pharmacyclics research funding: Research Funding.

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.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

Distinct Gene Expression Signatures in Patients with Richter's Syndrome and Chronic Lymphocytic Leukemia with Prior Exposure to Ibrutinib

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 30-31
Author(s):  
Hanyin Wang ◽  
Shulan Tian ◽  
Qing Zhao ◽  
Wendy Blumenschein ◽  
Jennifer H. Yearley ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Cell Activation ◽  
Expression Profiles ◽  
Lymphocytic Leukemia ◽  
B Cell Activation ◽  
Advisory Committees ◽  
Upregulated Genes

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.

Functional Somatic and Germline Variants in the NF-κB Pathway in Chronic Lymphocytic Leukemia

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 560-560 ◽  
Author(s):  
Ma. Reina Improgo ◽  
Adam Kiezun ◽  
Yaoyu Wang ◽  
Lillian Werner ◽  
Petar Stojanov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Death ◽  
Chronic Lymphocytic Leukemia ◽  
Transcriptional Activity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Lymphocytic Leukemia ◽  
Wild Type ◽  
Rt Pcr ◽  
Germline Variants

Abstract Abstract 560 Nuclear factor kappa B (NF-κB) encompasses a family of transcription factors involved in oncogenic processes including cellular proliferation and apoptotic inhibition. Constitutive activation of NF-κB has been observed in hematologic malignancies and is thought to confer resistance to chemotherapeutic agents. Here, we examine the role of the NF-κB pathway in chronic lymphocytic leukemia (CLL). Whole-exome sequencing was performed using tumor and matched germline DNA from 167 CLL patients. We identified 51 patients (30%) carrying 53 non-silent somatic variants in genes of the canonical NF-κB pathway, which consists of 272 genes as defined by the Ingenuity Pathway Analysis tool. Of the 99 patients whose germline sequences have been analyzed to date, 27 patients (27%) carry 34 non-silent germline variants in NF-κB pathway genes. A total of 67 patients (40%) have at least one non-silent somatic or germline variant. Variants in the NFKB1 gene, itself, were also observed: a somatic variant, H66R, found in two patients, and two germline variants, Y89F and R849W, each found in one patient. To evaluate the functional consequences of the NFKB1 variants, we performed site-directed mutagenesis to generate full-length NFKB1 cDNAs encoding these variants. We subsequently measured transcriptional activity of wild-type and mutant NFKB1 via luciferase assays in HEK293T cells using reporter cassettes containing the NFKB1 response element. Transcriptional activity of the three NFKB1 variants was found to be at least 2-fold higher than that of wild-type NFKB1 (p<0.0001). We further hypothesized that this increased transcriptional activity would lead to increased expression of NFKB1 downstream target genes. Analysis of gene expression profiles from Affymetrix HG-U133 Plus 2.0 Arrays of 65 CLL patient samples showed that the NFKB1 downstream targets CCL3, CCL4, and CD69 are upregulated in NFKB1 variants. To validate these results, we performed quantitative RT-PCR in patients with (n=3) or without (n=9) NFKB1 variants and confirmed upregulation of CCL3 (p=0.0286), CCL4 (p=0.0384), and CD69 (p=0.0263). Direct transfection of HEK293T cells with NFKB1 variants also resulted in a 3.3-fold upregulation of CCL3 (p=0.05). To test the hypothesis that deregulation of the NF-κB pathway is a key mechanism in CLL, we compared gene expression profiles of NF-κB pathway genes between CLL patient samples (n=146) and normal B cells (n=16) and found overall upregulation of the NF-κB pathway in CLL (Kolmogorov-Smirnov test, p=2.2e-16). K-means clustering and principal component analysis (PCA) further revealed that CLL patients can be divided into two subgroups exhibiting differential magnitude of NF-κB pathway upregulation. Studies in progress aim to identify the clinical significance of these subgroups. Finally, we assessed the effect of inhibiting the NF-κB pathway using the cell permeant NF-κB inhibitor, SN50. We performed Annexin V/PI staining 24 hours post-treatment in CLL cells with (n=9) or without (n=3) NF-κB pathway variants. SN50 increased cell death 1.8-fold in all cells tested (p<0.0001). Quantitative RT-PCR also showed a 59% decrease in expression of CCL3 one hour post-treatment, confirming inhibition of the NF-κB pathway. In conclusion, our findings demonstrate that a high proportion of CLL patients harbor somatic and germline variants in NF-κB pathway genes, some of which appear to be functional. Furthermore, the NF-κB pathway is upregulated in CLL and pharmacological inhibition of the pathway leads to increased cancer cell death. Functional characterization of NF-κB pathway variants offers mechanistic insight into the disease, providing novel targets for therapy. Disclosures: No relevant conflicts of interest to declare.

A 13-Glycosylation Gene Signature in Multiple Myeloma Can Predicts Survival and Identifies Candidates for Targeted Therapy (GiMM13)

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4423-4423 ◽  
Author(s):  
Caoilfhionn Connolly ◽  
Alokkumar Jha ◽  
Alessandro Natoni ◽  
Michael E O'Dwyer
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Research Funding ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Rank Test ◽  
P Value ◽  
Log Rank Test ◽  
Novel Approach

Abstract Introduction Advances in genomics have highlighted the potential for individualized prognostication and therapy in multiple myeloma (MM). Previously developed gene expression signatures have identified patients with high risk (Kuiper et al, Blood 2016) however, they provide few insights into underlying disease biology thereby limiting their use in informing treatment decisions. Glycosylation is deregulated in MM (Glavey et al), and potential consequences include altered cell adhesion, signaling, immune evasion and drug resistance. In this study we have utilized RNA sequencing data from the IA7 CoMMpass cohort to characterize the expression profile of genes involved in glycosylation. This represents a novel approach to identify a distinct molecular pathway related to outcome, which is potentially actionable. Methods A pathway based approach was adopted to evaluate genes implicated in glycosylation, including the generation of selectin ligands. A literature review and KEGG pathway analysis of pathways relating to O-glycans, N-glycans, sialic acid metabolism, glycolipid synthesis and metabolism was completed. RNA Cufflinks-gene level FPKM expression of 458 patients enrolled in the IA7 cohort of the Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) were analysed as derivation cohort. We developed expression cut-offs using a novel approach of adjusted existing linear regression model to define the gene expression cut-off by applying 3rd Quartile data (q1+q2/2-qmin). The analysis of overall survival (OS) was completed using adjusted 'kpas' R-package according to our cut-off model. Association between individual transcripts and OS was analyzed with log-rank test. Genes with p-value <0.2 were used in subsequent prioritization analysis. This cut-off methodology was employed to define the nearest neighbor for a gene for Gene Set Enrichment Analysis (GSEA). As far as 4th neighbor above and below the cut off was used to have centrally driven gene selection method for prioritization. The gene signature was validated in GSE2658 (Shaughnessy et al) dataset. Results Initial analysis yielded 184 prospective genes. 147 were significant on univariate analysis. Following further prioritization of these genes, we identified thirteen genes that had significant impact upon outcomes (GiMM13). Figure 1 reveals that GiMM13 signature has a significant correlation with inferior OS (HR 4.66 p-value 0.022). The prognostic impact of stratifying GiMM13 positive (High risk) or GiMM13 negative (Low risk) by ISS stage was evaluated. In Table 1. Kaplan Meier estimates generated for GiMM13 (High) or GiMM13 (Low) stratified by ISS are compared statistically using the log rank test. The prognostic ability of GiMM13 to synthesize distinct subgroups relative to each ISS stage is shown in Figure 2. ISS1-Low is the the lowest risk group with best prognosis. Hazard ratios relative to the ISS1-Low group were 1.8, p-value 0.029 (ISS2-Low), 2.1, p-value 0.031 (ISS3-Low), 4.3, p-value 0.04 (ISS1-HR), 5.9, p-value 0.039 (ISS2-HR) and 3.1, p-value 0.001 (ISS3-HR). The GiMM13 signature enhances the prognostic ability of ISS to identify patients with inferior or superior outcomes respectively. Conclusion While the therapeutic armamentarium for MM has expanded considerably, the significant molecular heterogeneity in the disease still poses a significant challenge. Our data suggests aberrant transcription of glycosylation genes, involved predominantly in selectin ligand synthesis, is associated with inferior survival outcomes and may help identify patients likely to benefit from treatment with agents targeting aberrant glycosylation, e.g. E-selectin inhibitor. Consistent with recent findings in chemoresistant minimal residual disease (MRD) (Paiva et al, Blood 2016), it would appear that O-glycosylation, rather than N-glycosylation is most significantly implicated in this biological processes conferring inferior outcomes. In conclusion, using a novel pathway-based approach to identify a 13-gene signature (GiMM13), we have developed a robust tool that can refine patient prognosis and inform clinical decision-making. Acknowledgment These data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiatives (https://research.themmrf.org and www.themmrf.org). Disclosures O'Dwyer: Glycomimetics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding.

Association between cetuximab response and differential immunologic gene expression signatures in colorectal cancer metastases.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 558-558 ◽  
Author(s):  
Michael Sangmin Lee ◽  
Benjamin Garrett Vincent ◽  
Autumn Jackson McRee ◽  
Hanna Kelly Sanoff
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
T Cells ◽  
T Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Activated T Cells ◽  
Gene Sets

558 Background: Different immune cell infiltrates into colorectal cancer (CRC) tumors are associated with different prognoses. Tumor-associated macrophages contribute to immune evasion and accelerated tumor progression. Conversely, tumor infiltrating lymphocytes at the invasive margin of CRC liver metastases are associated with improved outcomes with chemotherapy. Cetuximab is an IgG1 monoclonal antibody against epidermal growth factor receptor (EGFR) and stimulates antibody-dependent cellular cytotoxicity (ADCC) in vitro. However, it is unclear in humans if response to cetuximab is modulated by the immune response. We hypothesized that different immune patterns detected in gene expression profiles of CRC metastases are associated with different responses to cetuximab. Methods: We retrieved gene expression data from biopsies of metastases from 80 refractory CRC patients treated with cetuximab monotherapy (GEO GSE5851). Samples were dichotomized by cetuximab response as having either disease control (DC) or progressive disease (PD). We performed gene set enrichment analysis (GSEA) with GenePattern 3.9.4 using gene sets of immunologic signatures obtained from the Molecular Signatures Database v5.0. Results: Among the 68 patients with response annotated, 25 had DC and 43 had PD. In the PD cohort, 59/1910 immunologic gene sets had false discovery rate (FDR) < 0.1. Notably, multiple gene sets upregulated in monocyte signatures were associated with PD. Also, gene sets consistent with PD1-ligated T cells compared to control activated T cells (FDR = 0.052) or IL4-treated CD4 T cells compared to controls (FDR = 0.087) were associated with PD. Conclusions: Cetuximab-resistant patients tended to have baseline increased expression of gene signatures reflective of monocytic infiltrates, consistent with also having increased expression of the IL4-treated T-cell signature. Cetuximab resistance was also associated with increased expression of the PD1-ligated T cell signature. These preliminary findings support further evaluation of the effect of differential immune infiltrates in prognosis of metastatic CRC treated with cetuximab.

scholarly journals Gene Expression and Epigenetic Analysis in Relapsed/Refractory Diffuse Large B Cell Lymphoma Provides Insights into Evolution of Treatment Resistance to R-CHOP

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 26-26
Author(s):  
Manishkumar S. Patel ◽  
Ellen K. Kendall ◽  
Sarah Ondrejka ◽  
Agrima Mian ◽  
Yazeed Sawalha ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Differentially Expressed Genes ◽  
Research Funding ◽  
B Cell Lymphoma ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
P Value

Background Diffuse large B cell lymphoma (DLBCL) is curable in ~60-70% of patients using standard chemoimmunotherapy, but the prognosis is poor for relapsed/refractory (R/R) DLBCL. Therefore, understanding the underlying molecular mechanisms will facilitate early prediction and effective management of resistance to therapy. Recent studies of paired diagnostic-relapse biopsies from patients have relied on a single "omics" approach, examining either gene expression or epigenetic evolution. Here we present a combined analysis of gene expression and DNA methylation profiles of paired diagnostic-relapse DLBCL biopsies to identify changes responsible for relapse after R-CHOP. Methods Biopsies from 23 DLBCL patients were obtained at the time of diagnosis and relapse following frontline R-CHOP chemoimmunotherapy. The cohort had 18 (78.3%) male patients with median age of 62 (range, 35-86) years and median IPI of 2.5 (range, 1-5). The median time from diagnosis to relapse was 7 (range, 0-57) months. DNA and RNA were extracted simultaneously from formalin-fixed paraffin embedded (FFPE) biopsy samples. DNA methylation levels were measured through Illumina 850k Methylation Array for 22 pairs of diagnostic-relapse biopsies. RNA from diagnostic-relapse paired biopsies from 6 patients was sequenced 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. Pearson's correlation with a Bonferroni correction to the p-value was used to calculate the correlation between regularized log transformed gene expression counts and methylation beta values. Results In a pairwise comparison of gene expression between diagnostic and R/R biopsy pairs, we found 14 differentially expressed genes (FDR&lt;0.1 & Log2FC&gt;|1|) consistent across all pairs. Compared to gene expression at diagnosis, five genes (CYP1B1, LGR4, ATXN1, CTSC, ZMAT3) were downregulated, and eight genes (ERBB3, CD19, CARD11, MT-RNR2, IGHG3, CCDC88C, ATP2A3, CENPE, and PCNT) were up-regulated in the R/R samples. Many of these genes have been previously implicated in oncogenesis, such as ERBB3, a member of the epidermal growth receptor family. Importantly, some of these genes have known roles in DLBCL biology, such as CD19, a member of the B-cell receptor complex, and CARD11, a gene in which several oncogenic mutations have been identified in DLBCL as a mediator of NF-KB activation. Gene set enrichment analysis revealed overexpression of immune signatures such as cytokine-cytokine receptor interaction, chemokine receptor-chemokine binding, and the IL-12-STAT4 pathway at diagnosis. At relapse, cell cycle, B-cell receptor, and NOTCH signaling pathways were overexpressed. Interestingly, in a pairwise comparison of methylation between diagnostic and R/R biopsy pairs, there were no differentially methylated probes (FDR&lt;0.05), suggesting no coordinated epigenetic evolution between diagnostic and R/R pairs. For biopsy pairs that had both gene expression and methylation data (5 pairs), we correlated gene expression and methylation values. We found that none of the differentially expressed genes between the diagnostic and R/R biopsies were significantly correlated with methylation status (adjusted p-value&lt;0.05). Conclusions By analyzing paired diagnostic and relapse DLBCL biopsies, we found that at the time of relapse, there are significant transcriptomic changes but no significant epigenetic changes when compared to diagnostic biopsies. Activation of B-cell receptor and NOTCH signaling, as well as the loss of immune signaling at relapse, cannot be attributed to coordinated epigenetic changes in methylation. As the epigenetic profile of the biopsies did not consistently evolve, these data emphasize the need for better understanding of the baseline methylation profiles at the time of diagnosis, as well as acquired somatic mutations that may contribute to the emergence of therapeutic resistance. Future studies are needed to focus on how activation of signaling pathways triggered by genomic alterations can be targeted in relapsed/refractory DLBCL. Disclosures Hsi: Seattle Genetics: Consultancy, Honoraria; Miltenyi: Consultancy, Honoraria; Abbvie: Research Funding; Eli Lilly: Research Funding; CytomX: Consultancy, Honoraria. Hill:Takeda: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding.

scholarly journals Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

2015 ◽  
Vol 6 ◽  
pp. 2438-2448 ◽  
Author(s):  
Andrew Williams ◽  
Sabina Halappanavar
Keyword(s):  
Gene Expression ◽  
Pulmonary Fibrosis ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Driven ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Analysis Methods ◽  
Gene Sets

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

Use of gene set analysis (GSA) for molecular classification of responders and nonresponders to FOLFOX therapy in colorectal cancer (CRC).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3628-3628
Author(s):  
Stephen Yip ◽  
Maggie Chon U. Cheang ◽  
Hagen F. Kennecke
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Metal Ion ◽  
Expression Profiles ◽  
Single Gene ◽  
Chromosome 17 ◽  
Aminopeptidase Activity ◽  
P Value ◽  
Hematopoietic Stem ◽  
Gene Sets

3628 Background: Folinic acid (FOL) fluorouracil (F) and oxaliplatin (OX) chemotherapy is a commonly used therapy for CRC. Lacking in current literature are clinically relevant classifiers for potential responders. GSA technique is statistical method that detects significance of sets of genes, instead of examining a gene-by-gene basis. The objective of this study was to identify differential functionally annotated gene expression profiles associated with response to FOLFOX therapy in CRC tumors using gene-by-gene and GSA approaches. Methods: Genome wide expression profile data were collected on pre-treatment tumor tissues from patients with unresectable CRC receiving FOLFOX therapy (n = 83, Affymetrix HG U133A, GSE28702, Tsuji et al. BJC 2012). Gene expression was compared between responders (n = 42) and Non-responders (n = 41). GSA was conducted on 3272 curated gene sets from the Molecular Signatures Database (Subramanian, Tamayo et al. 2005, PNAS 102, 15545-15550) annotated by biological pathway, biochemical function and clinical behavior. Significant analysis of Microarray (SAM) and GSA (Tibshirani et al.) was done to identify gene sets associated with FOLFOX response. Results: Differential expressions of 23 genes were significantly associated with response, based on a single gene approach (p-value < 0.05). 13 of these were located on Chromosome 17 (p < 0.001). Among these, the top 5 ranked genes included NPEPPS, MBTD1, CEP44, LTA4H and CPNE4 which are involved in metal ion binding and aminopeptidase activity. GSA revealed only 44 out of 3272 gene sets were significantly associated with response, with a false discovery rate less than 25%. Increased expression of B-lymphocyte differentiation and Ras-signalling-related gene sets was associated with responders while mTOR signaling and hematopoietic stem cell-related genes set were associated with non-responders. Conclusions: Our data showed that differential biological pathways could be identified to predict response to FOLFOX therapy for CRC patients. Analysis may be useful to help define clinically relevant biologic subtypes among patients with metastatic colorectal cancer.

scholarly journals Nonconventional Gene Expression within the NF-κb Signaling Pathway Induced By Poly(propylene)Imine Glycodendrimers in Chronic Lymphocytic Leukemia Cells

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5595-5595
Author(s):  
Ida Franiak-Pietryga ◽  
Kinga Ostrowska ◽  
Dietmar Appelhans ◽  
Henryk Maciejewski ◽  
Maria Bryszewska ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Chronic Lymphocytic Leukemia ◽  
Signaling Pathway ◽  
Research Funding ◽  
Target Genes ◽  
Early Stage ◽  
Lymphocytic Leukemia ◽  
P Value

Abstract Introduction The nuclear factor kappa-light-chain enhancer of activated B-cells (NF-κB) signaling pathway is constitutively active in a variety of cancers, including chronic lymphocytic leukemia (CLL). The importance of this signaling pathway identifies it as a prime therapeutic target, however the complexity and potential side effects of inhibiting NF-κB have thus far made the clinical use of NF-κB inhibitors a relatively unexplored resource in this disease. There are a few combined therapies available for the treatment of CLL includes chemotherapy with agents such as chlorambucil, cyclophosphamide, fludarabine and bendamustine, along with immunotherapy including rituximab and alemtuzumab. None available therapy for CLL is curative. Nanotechnology, a new and promising field of scientific research, may be of use in medicine and the pharmaceutical industry. Dendrimers, nanoparticles of dendritic architecture, can interact effectively and specifically with cell components. We have already proved the influence of PPI-G4-OS-M3 dendrimers in cultures in vitro on CLL cells apoptosis.Herein, the objective was to evaluate how MEC-1 cells survival in vitro is affected by influence on NF-κB pathway by PPI-G4-OS-M3 dendrimer comparing to FA. Material and methods Dendrimer, in which approximately 35% of peripheral amino groups, was coated with maltotriose have been defined as PPI-G4-OS-M3 and was used in concentration of 8 mg/ml (the IC50 value for this dendrimer). 'OS' abbreviation stands for the open shell structure of carbohydrate-modified dendrimers. The molar mass of this PPI dendrimer was 31000 g/mol. Fludarabine (FA, Genzyme) in concentration of 1.6 µM, based on previous studies, was used. MEC-1 (DSMZ no. ACC 497) was used as a homogenous cell line with del(17p)(11q). In cultures the percentage of apoptotic cells was verified using AnnV and PI by means of flow cytometer. Cells predominated in the early stage of apoptosis.A microarray gene expression (Agilent SurePrint Technologies) was performed. Samples were hybridized to a whole human genome microarray 8x60K. Arrays were scanned on Agilent DNA Microarray Scanner. Data were deposited at Gene Expression Omnibus (GEO) (accession number GSE68094).Analysis of differential expression of genes was done with the limma method (Smyth, G. K., 2004) as implemented in R/Bioconductor software. We used the FDR multiple testing adjustment. We declared as differentially expressed the genes with FDR-adjusted p-value <0.1, which means that 10% of genes declared as DE are expected to be false positives. Results Dendrimer induced expression of REL, RELB and NFKBIB genes. In contrast, FA monotherapy resulted in significant differences in gene expression of cellular pathway-dependent transcription factor NF-kB. The most significant differences in the function of the FA and dendrimer are reflected in different levels of expression of three genes: NFKBIA, BCL3 and CHUK. Conclusion Constitutive NF-κB signaling contributes to cell growth, proliferation and survival. CLL cells have high basal levels of NF-κB compared with normal B cells. The activity is variable in CLL patients, correlates with in vitro cell survival, and importantly, increased levels of NF-κB activity enhanced resistance to the purine analogues FA (del17p). Therefore, disruption of NF-κB signaling and downstream target genes either promoted or repressed is an important strategy to pursue to disrupt drug resistance in CLL. The study indicates that the use of PPI dendrimers modified maltotriose may be the key to developing therapies deliberates CLL. The study was partially supported by Grant No. DEC-2011/01/B/NZ5/01371from the National Science Centre, Poland. Disclosures Robak: Janssen: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding.

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