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.

scholarly journals 909 Differentiation subgroups within LKB1-deficient lung cancer influence both the immune exclusion phenotype and cellular composition of the immune microenvironment

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A954-A955
Author(s):  
Jacob Kaufman ◽  
Doug Cress ◽  
Theresa Boyle ◽  
David Carbone ◽  
Neal Ready ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Plasma Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Neuroendocrine Differentiation ◽  
Gene Expression Profiles ◽  
Immune Microenvironment ◽  
Immune Exclusion

BackgroundLKB1 (STK11) is a commonly disrupted tumor suppressor in NSCLC. Its loss promotes an immune exclusion phenotype with evidence of low expression of interferon stimulated genes (ISG) and decreased microenvironment immune infiltration.1 2 Clinically, LKB1 loss induces primary immunotherapy resistance.3 LKB1 is a master regulator of a complex downstream kinase network and has pleiotropic effects on cell biology. Understanding the heterogeneous phenotypes associated with LKB1 loss and their influence on tumor-immune biology will help define and overcome mechanisms of immunotherapy resistance within this subset of lung cancer.MethodsWe applied multi-omic analyses across multiple lung adenocarcinoma datasets2 4–6 (>1000 tumors) to define transcriptional and genetic features enriched in LKB1-deficient lung cancer. Top scoring phenotypes exhibited heterogeneity across LKB1-loss tumors, and were further interrogated to determine association with increased or decreased markers of immune activity. Further, immune cell-types were estimated by Cibersort to identify effects of LKB1 loss on the immune microenvironment. Key conclusions were confirmed by blinded pathology review.ResultsWe show that LKB1 loss significantly affects differentiation patterns, with enrichment of ASCL1-expressing tumors with putative neuroendocrine differentiation. LKB1-deficient neuroendocrine tumors had lower expression of Interferon Stimulated Genes (ISG), MHC1 and MHC2 components, and immune infiltration compared to LKB1-WT and non-neuroendocrine LKB1-deficient tumors (figure 1).The abundances of 22 immune cell types assessed by Cibersort were compared between LKB1-deficient and LKB1-WT tumors. We observe skewing of immune microenvironmental composition by LKB1 loss, with lower abundance of dendritic cells, monocytes, and macrophages, and increased levels of neutrophils and plasma cells (table 1). These trends were most pronounced among tumors with neuroendocrine differentiation, and were concordant across three independent datasets. In a confirmatory subset of 20 tumors, plasma cell abundance was assessed by a blinded pathologist. Pathologist assessment was 100% concordant with Cibersort prediction, and association with LKB1 loss was confirmed (P=0.001).Abstract 909 Figure 1Immune-associated Gene Expression Profiles Affected by Neuroendocrine Differentiation within LKB1-Deficient Lung Adenocarcinomas. Gene expression profiles corresponding to five immune-associated phenotypes are shown with bars indicating average GEP scores for tumors grouped according to LKB1 and neuroendocrine status as indicated. P-values represent results from Student’s T-test between groups as indicated.Abstract 909 Table 1LKB1 Loss Affects Composition of Immune Microenvironment. Values indicate log10 P-values comparing LKB1-loss to LKB1-WT tumors. Positive (red) indicates increased abundance in LKB1 loss. Negative (blue) indicates decreased abundance.ConclusionsWe conclude that tumor differentiation patterns strongly influence the immune microenvironment and immune exclusion characteristics of LKB1-deficient tumors. Neuroendocrine differentiation is associated with the strongest immune exclusion characteristics and should be evaluated clinically for evidence of immunotherapy resistance. A novel observation of increased plasma cell abundance is observed across multiple datasets and confirmed by pathology. Causal mechanisms linking differentiation status to immune activity is not well understood, and the functional role of plasma cells in the immune biology of LKB1-deficient tumors is undefined. These questions warrant further study to inform precision immuno-oncology treatments for these patients.AcknowledgementsThis work was funded by SITC AZ Immunotherapy in Lung Cancer grant (SPS256666) and DOD Lung Cancer Research Program Concept Award (LC180633).ReferencesSkoulidis F, Byers LA, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov 2015;5:860–77.Schabath MB, Welsh EA, Fulp WJ, et al. Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene 2016;35:3209–16.Skoulidis F, Goldberg ME, Greenawalt DM, et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discovery 2018;8:822-835.Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014;511:543–50.Chitale D, Gong Y, Taylor BS, et al. An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 2009;28:2773–83.Shedden K, Taylor JM, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008;14:822–7.

Improved Feature Selection by Incorporating Gene Similarity into the LASSO

2012 ◽  
Vol 3 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Christopher E. Gillies ◽  
Xiaoli Gao ◽  
Nilesh V. Patel ◽  
Mohammad-Reza Siadat ◽  
George D. Wilson
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Personalized Medicine ◽  
Objective Function ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genetic Profile ◽  
Data Set ◽  
Coordinate Descent Algorithm ◽  
Gene Similarity

Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model’s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors’ modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors’ model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.

scholarly journals PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kota Fujisawa ◽  
Mamoru Shimo ◽  
Y.-H. Taguchi ◽  
Shinya Ikematsu ◽  
Ryota Miyata
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Target Genes ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Principal Component ◽  
Data Set ◽  
Immune Related Genes ◽  
Unsupervised Feature Extraction

AbstractCoronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.

scholarly journals Granulosa-Lutein Cell Sirtuin Gene Expression Profiles Differ between Normal Donors and Infertile Women

2019 ◽  
Vol 21 (1) ◽  
pp. 295
Author(s):  
Rebeca González-Fernández ◽  
Rita Martín-Ramírez ◽  
Deborah Rotoli ◽  
Jairo Hernández ◽  
Frederick Naftolin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Localization ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cellular Protein ◽  
Control Group ◽  
Poor Responders ◽  
Male Factor ◽  
Expression Levels ◽  
Genes Expression

Sirtuins are a family of deacetylases that modify structural proteins, metabolic enzymes, and histones to change cellular protein localization and function. In mammals, there are seven sirtuins involved in processes like oxidative stress or metabolic homeostasis associated with aging, degeneration or cancer. We studied gene expression of sirtuins by qRT-PCR in human mural granulosa-lutein cells (hGL) from IVF patients in different infertility diagnostic groups and in oocyte donors (OD; control group). Study 1: sirtuins genes’ expression levels and correlations with age and IVF parameters in women with no ovarian factor. We found significantly higher expression levels of SIRT1, SIRT2 and SIRT5 in patients ≥40 years old than in OD and in women between 27 and 39 years old with tubal or male factor, and no ovarian factor (NOF). Only SIRT2, SIRT5 and SIRT7 expression correlated with age. Study 2: sirtuin genes’ expression in women poor responders (PR), endometriosis (EM) and polycystic ovarian syndrome. Compared to NOF controls, we found higher SIRT2 gene expression in all diagnostic groups while SIRT3, SIRT5, SIRT6 and SIRT7 expression were higher only in PR. Related to clinical parameters SIRT1, SIRT6 and SIRT7 correlate positively with FSH and LH doses administered in EM patients. The number of mature oocytes retrieved in PR is positively correlated with the expression levels of SIRT3, SIRT4 and SIRT5. These data suggest that cellular physiopathology in PR’s follicle may be associated with cumulative DNA damage, indicating that further studies are necessary.

scholarly journals The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kyu-Sang Lim ◽  
Qian Dong ◽  
Pamela Moll ◽  
Jana Vitkovska ◽  
Gregor Wiktorin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Globin Genes ◽  
Sequencing Data ◽  
Globin Mrna ◽  
Data Set ◽  
Mrna Sequencing ◽  
Porcine Blood

Abstract Background Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. Results In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads compared to the NGB (from 56.4 to 10.1%). The second highest concentration C2, which showed very similar globin depletion rates (12%) as C1 but a better correlation of the expression of non-globin genes between NGB and GB (r = 0.98), allowed the expression of an additional 1295 non-globin genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of globin reads for NGB (n = 184) and GB (n = 189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB, similar to results from data set 1. Data set 3 (n = 84) revealed that the proportion of globin reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). Conclusions The effect of the GB on reducing the proportion of globin reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-globin mRNA, the GB for QuantSeq has an advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

scholarly journals Comparison of Stemness and Gene Expression between Gingiva and Dental Follicles in Children

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chung-Min Kang ◽  
Seong-Oh Kim ◽  
Mijeong Jeon ◽  
Hyung-Jun Choi ◽  
Han-Sung Jung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pcr Analysis ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Stem Cell Source ◽  
Regenerative Dentistry ◽  
Induced Pluripotent

The aim of this study was to compare the differential gene expression and stemness in the human gingiva and dental follicles (DFs) according to their biological characteristics. Gingiva (n=9) and DFs (n=9) were collected from 18 children. Comparative gene expression profiles were collected using cDNA microarray. The expression of development, chemotaxis, mesenchymal stem cells (MSCs), and induced pluripotent stem cells (iPSs) related genes was assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Histological analysis was performed using hematoxylin-eosin and immunohistochemical staining. Gingiva had greater expression of genes related to keratinization, ectodermal development, and chemotaxis whereas DFs exhibited higher expression levels of genes related to tooth and embryo development. qRT-PCR analysis showed that the expression levels of iPSc factors includingSOX2,KLF4, andC-MYCwere58.5±26.3,12.4±3.5, and12.2±1.9times higher in gingiva andVCAM1(CD146) andALCAM(CD166) were33.5±6.9and4.3±0.8times higher in DFs. Genes related to MSCs markers includingCD13,CD34,CD73,CD90, andCD105were expressed at higher levels in DFs. The results of qRT-PCR and IHC staining supported the microarray analysis results. Interestingly, this study demonstrated transcription factors of iPS cells were expressed at higher levels in the gingiva. Given the minimal surgical discomfort and simple accessibility, gingiva is a good candidate stem cell source in regenerative dentistry.

Identification of Aberrantly Regulated Genes through Comparison of Genome-Wide Expression Profiles and Karyotype in Hyperdiploid >50 Chromosomes Pediatric Acute Lymphoblastic Leukemia.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 420-420
Author(s):  
Christian Flotho ◽  
Susana C. Raimondi ◽  
James R. Downing
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Expression Profiles ◽  
Chromosome 21 ◽  
Small Subset ◽  
Specific Gene ◽  
Expression Levels ◽  
Number Of Genes ◽  
The Mean ◽  
Gene Expression Levels

Abstract We have demonstrated that expression profiling of leukemic blasts can accurately identify the known prognostic subtypes of ALL, including T-ALL, E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid &gt;50 chromosomes (HD&gt;50). Interestingly, almost 70% of the genes that defined HD&gt;50 ALL localized to chromosome 21 or X. To further explore the relationship between gene expression and chromosome dosage, we compared the expression profiles obtained using the Affymetrix U133A&B microarrays of 17 HD&gt;50 ALLs to 78 diploid or pseudodiploid ALLs. Our analysis demonstrated that the average expression level for all genes on a chromosome could be used to predict chromosome copy numbers. Specifically, the copy number for each chromosome calculated by gene expression profiling predicted the numerical chromosomal abnormalities detected by standard cytogenetics. For chromosomes that were trisomic in HD&gt;50 ALL, the mean chromosome-specific gene expression level was increased approximately 1.5-fold compared to that observed in diploid or pseudodiploid ALL cases. Similarly, for chromosome 21 and X, the mean chromosome-specific gene expression levels were increased approximately 2-fold, consistent with a duplication of the active X chromosome and tetrasomy of chromosome 21, a finding verified by standard cytogenetics in &gt;90% of the HD&gt;50 cases. These finding indicate that the aberrant gene expression levels seen in HD&gt;50 ALL primarily reflect gene dosages. Importantly, we did not observe any clustering of aberrantly expressed genes across the duplicated chromosomes, making regional gain or loss of genomic material unlikely. Paradoxically, however, a more detailed analysis revealed a small but statistically significant number of genes on the trisomic/tetrasomic chromosomes whose expression levels were markedly reduced when compared to that seen in diploid or pseudodiploid leukemic samples. Using the Statistical Analysis of Microarrays (SAM) algorithm we identified 20 genes whose expression was reduced &gt;2-fold despite having an increase in copy number. Interestingly, included within this group are several known tumor suppressors, including AKAP12, which is specifically silenced by methylation in fos-transformed cells, and IGF2R and IGFBP7, negative regulators of insulin-like growth factor signaling. In addition to the silencing of a small subset of genes, we also identified 21 genes on these chromosomes whose expression levels were markedly higher (&gt;3-fold) than would be predicted solely based on copy number. Although the mechanism responsible for their increased expression remains unknown, included in this group are four genes involved in signal transduction (IL3RA, IL13RA1, SNX9, and GASP) and a novel cytokine, C17, whose expression is normally limited to CD34+ hematopoietic progenitors. Taken together, these data suggest that aberrant growth in HD&gt;50 ALL is in part driven by increased expression of a large number of genes secondary to chromosome duplications, coupled with a further enhanced expression of a limited number of growth promoting genes, and the specific silencing of a small subset of negative growth regulatory genes. Understanding the mechanisms responsible for the non-dosage related changes in gene expression should provide important insights into the pathology of HD&gt;50 ALL.

Overexpression of BRE in Pediatric MLL-Rearranged Acute Myeloid Leukemia Associated with t(9;11)(p22;q23).

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3471-3471
Author(s):  
Brian Balgobind ◽  
C. Michel Zwaan ◽  
Susan T.C.J.M. Arentsen-Peters ◽  
Dirk Reinhardt ◽  
Ursula Creutzig ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Apoptotic Effect ◽  
The Mean ◽  
Pediatric Aml ◽  
Acute Myeloid

Abstract Abstract 3471 Poster Board III-359 One important cytogenetic subgroup of pediatric acute myeloid leukemia (AML) is characterized by translocations of chromosome 11q23, which accounts for 15 to 20% of all cases with an evaluable chromosome analysis. In most of these cases, the mixed lineage leukemia (MLL) gene is involved. More than 50 fusion translocation partners of the MLL gene have been identified and outcome differs by translocation partner, suggesting differences in the biological background. So far these biological differences have not been unravelled. Therefore, we investigated the gene expression profiles of MLL-rearranged subgroups in pediatric AML in order to discover and identify the role of differentially expressed genes. Affymetrix Human Genome U133 plus 2.0 microarrays were used to generate gene expression profiles of 257 pediatric AML cases, which included 21 pediatric AML cases with t(9;11)(p22;q23) and 33 with other MLL-rearrangements. With these profiles, we were able to identify a specific gene expression signature for t(9;11)(p22;q23) using an empirical Bayes linear regression model (Bioconductor package: Limma). This signature was mainly determined by overexpression of the BRE (brain and reproductive organ-expressed) gene. The mean average VSN normalized expression for BRE in the t(9;11)(p22;q23) subgroup was 3.7-fold higher compared with that in other MLL-rearranged cases (p<0.001). Validation by RQ-PCR confirmed this higher expression in t(9;11)(p22;q23) cases (p<0.001). In addition, we confirmed that overexpression of BRE was predominantly found in t(9;11)(p22;q23) in an independent gene expression profile cohort (Ross et al, Blood 2002). Remarkably, MLL-rearranged cases with a BRE expression higher than the mean expression showed a significant better 3 year disease free survival than MLL-rearranged cases with a lower expression (80±13% vs. 30±10%, p=0.02). Previously, overexpression of BRE has been described in hepatocellular carcinomas (HCC) (Chang et al., Oncogene 2008) and an anti-apoptotic effect was described. We transfected BRE in the monomac-1 cell line, which harbors a t(9;11)(p22;q23). We did not find a proliferative advantage for BRE overexpression using a BrDU-assay nor changes in drug sensitivity, indicating that the anti-apoptotic effect as described for HCC in vivo could not be confirmed in vitro in AML. In conclusion, overexpression of the BRE gene is predominantly involved in pediatric MLL-rearranged AML with t(9;11)(p22;q23). Moreover, high expression of BRE showed a favorable prognosis. We did not find any influence of BRE expression on cell proliferation or apoptosis in vitro. This indicates that further studies involving the role of the MLL-fusion protein on BRE transcription are necessary to unravel the leukemogenic role in pediatric AML. Disclosures No relevant conflicts of interest to declare.

Molecular Determinants of Lymph Node Microenvironment Induced Host Immune Tolerance In CLL: Role for CAV1, PTPN6, and PKCβ In the Process

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1367-1367
Author(s):  
Christine Gilling ◽  
Amit Mittal ◽  
Vincent Nganga ◽  
Vicky Palmer ◽  
Dennis D. Weisenburger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Model ◽  
Immune Tolerance ◽  
Conflicts Of Interest ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Receptor ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Aggressive Disease

Abstract Abstract 1367 Previously, we have shown that gene expression profiles (GEP) of CLL cells from lymph nodes (LN), bone marrow (BM), and peripheral blood (PB) are significantly different from each other. Among the major pathways associated with differential gene expression, a “tolerogenic signature” involved in host immune tolerance is significant in regulating CLL progression. The genes associated with the tolerogenic signature are significantly differentially expressed in patient LN-CLL compared to BM-CLL and PB-CLL, suggesting that LN-CLL cells induce this immune tolerance. From 83 differentially expressed genes identified by GEP that are associated with immune dysregulation, we selected eleven genes (CAV1, PTPN6, PKCb, ZWINT, IL2Ra, CBLC, CDC42, ZNF175, ZNF264, IL10, and HLA-G) for validation studies to determine whether these genes are also dysregulated in the Emu-TCL1 mouse model of CLL. The results demonstrate a trend of upregulation of these genes as determined by qRT-PCR in the LN-tumor microenvironment. To further evaluate the kinetics of selected gene expression during tumor progression, we determined the expression levels of Cav1, Ptpn6, and Pkcb at 12, 24, and 36 weeks of CLL development in the Em-TCL1 mouse model. We found that the expression of all three genes increased as a function of age, indicating a correlation of gene expression with disease progression. In addition, as CLL progressed in these mice there was a marked decrease in CD4+ and CD8+ T cells. The murine data were further validated using CLL cells from the same patients with indolent versus aggressive disease indicating a similar trend in expression as CLL progressed (n=4). Furthermore, patient data analyzed by Kaplan Meier analyses of the expression levels of the selected genes indicated a significant association between down-regulation of PTPN6 (p=0.031) and up-regulation of ZWINT (p<0.001) with clinical outcome as determined by a shorter time to treatment (p<0.05). Functional analysis by knockdown of CAV1 and PKCb in primary patient CLL cells determined by MTT assay showed a decrease in proliferation following knockdown of these genes (p<0.005). Protein-interaction modeling revealed regulation of CAV1 and PTPN6 by one another. Additionally, the PTPN6 protein regulates B cell receptor (BCR) signaling and subsequently the BCR regulates PKCb. Therefore, these data from both mice and humans with CLL, argue that an aggressive disease phenotype is paralleled by expression of genes associated with immune suppression. In particular, evidence presented here suggests, dysregulation of CAV1, PTPN6, ZWINT, and PKCb expression promotes CLL progression. Disclosures: No relevant conflicts of interest to declare.

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.

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