Genomic Analysis of High Hyperdiploid Acute Lymphoblastic Leukemia and Hyperdiploid Multiple Myeloma Suggests Differential Gene Dosage Effect on Expression and Provide Clues to Preferential Selection of Recurrently Trisomic Chromosomes.

Abstract Hyperdiploid (>48 chromosomes) multiple myeloma (H-MM) and high hyperdiploid (>50 chromosomes) acute lymphoblastic leukemia (H-ALL) are characterized by aneuploidy and multiple recurrent trisomies (chromosome 3,5,7,9,11,15,19 for H-MM and chromosomes X,4,6,10,14,17,18,21 for H-ALL). Little is known about the oncogenic events, consequences of the trisomies and reasons for the different recurrent trisomies. In an attempt to answer these questions, we undertook a combined gene expression and network/pathway analysis approach. Gene expression data was generated using the Affymetrix U133A chip (Affymetrix, Santa Clara, Ca) for 53 H-MM and 37 non-hyperdiploid MM (NH-MM) cases using CD138-enriched plasma-cell RNA. Gene expression data using the same chip for ALL was obtained from previous published data (Ross ME et al Blood2004; 104: 3679–3687). Analysis was performed using Genespring 7 (Agilent Technologies, Palo Alto, CA). By comparing the median expression of all genes on each chromosome between H-MM/H-ALL and their non-hyperdiploid counterparts (NH-MM and NH-ALL) for the 23 chromosomes (excluding Y), one can clearly identify the commonly trisomic chromosomes in H-ALL and H-MM. However, the relationship of gene expression was highly variable for H-MM and NH-MM as compared to H-ALL and NH-ALL which tended to have expression ratios close to 1 for the non-trisomic chromosomes. Sixty-nine percent of the differentially expressed genes generated by ANOVA analysis (p<0.001) in H-ALL were on the commonly trisomic chromosomes and were upregulated whereas the corresponding figure in H-MM is 40%. These similarities and differences probably reflect both an overall gene dosage effect and the different complexities of the karyotypes of H-MM and H-ALL compared to NH-MM and NH-ALL respectively (MM karyotypes are more complex, hence difference between H and NH-MM is greater and less confined to the trisomic chromosomes). We next performed network analysis using a curated web-based software (MetaCore, GeneGo Inc, St Joseph, MI) using the 2 sets of differentially expressed genes. Majority of genes differentially expressed in H-MM are involved in mRNA translation/protein synthesis whereas the genes differentially expressed in H-ALL were mainly involved in signal transduction. Therefore the transcriptional program that characterize the difference between H and NH-MM/ALL seem to recapitulate normal cellular function: protein synthesis in the mature secretory plasma cells and signal transduction in response to cytokines in a differentiating early-B cell. However, due to the concurrent deregulation of many genes on these trisomic chromosomes, these and other cellular programs are deregulated resulting in malignant transformation. We also intersected the 2 lists of differentially expressed genes to find genes that are up- or downregulated in both H-MM and H-ALL relative to the NH tumors. Thirteen genes including interferon response genes (TNFSF10, MX1, ZNF185) and transcription factors like RUNX1 were upregulated, whereas 13 genes including a cancer testis antigen gene (MAGED4) were downregulated in both H-MM and H-ALL. These genes may point to common oncogenic mechanisms.

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Gene Expression Profiling of Hyperdiploid Multiple Reveal Complex Gene Dosage Effects and an mRNA Translation/Protein Synthesis Signature.

Blood ◽

10.1182/blood.v106.11.1538.1538 ◽

2005 ◽

Vol 106 (11) ◽

pp. 1538-1538

Author(s):

Wee-Joo Chng ◽

Scott Van Wier ◽

Gregory Ahmann ◽

Tammy Price-Troska ◽

Kim Henderson ◽

...

Keyword(s):

Gene Expression ◽

Protein Synthesis ◽

Differentially Expressed Genes ◽

Gene Dosage ◽

Gene Copy Number ◽

Mrna Translation ◽

Differentially Expressed ◽

Gene Copy ◽

Gene Dosage Effect ◽

Expression Of Genes

Abstract Hyperdiploid MM (H-MM), characterized by recurrent trisomies constitute about 50% of MM, yet very little is known about its pathogenesis and oncogenic mechanisms. Studies in leukemia and solid tumors have shown gene dosage effect of aneuploidy on gene expression. To determine the possible gene dosage effect and deregulated cellular program in H-MM we undertook a gene expression study of CD138-enriched plasma-cell RNA from 53 hyperdiploid and 37 non-hyperdiploid MM (NH-MM) patients using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). Gene expression data was analyzed using GeneSpring 7 (Agilent Technologies, Palo Alto, CA). Genes differentially expressed between H-MM and NH-MM were obtained by t-test (p<0.01). The majority of the differentially expressed genes (57%) were under-expressed in H-MM. Genes located on the commonly trisomic chromosomes were mostly (but not always) over-expressed in H-MM and constitute 76% of over-expressed genes. Chromosome 1 contained the most differentially expressed genes (17%) followed by chromosome 12 (9%), and 19 (8%). To examine the relationship of gene copy number to gene expression, we examined the expression of genes on chromosomes 9 and 15 in subjects with 2 copies (15 normal control and 20 NH-MM) and 3 copies (12 H-MM) of each chromosome as detected by interphase FISH. We then derived a ratio of the mean expression of each gene on these chromosomes between patients with 3 copies and 2 copies of the chromosome. If a simple relationship exists between gene expression and gene copy number, one would expect the ratio of expression of most genes on these two chromosomes to be about 1.5 in H-MM compared to NH-MM. However, many genes have ratios either higher than 2 or lower than 0.5. Furthermore, when the heterogeneity of cells with underlying trisomies is taken into consideration by correcting the ratio for the number of cells with trisomies, the actual ratio is always lower than the expected ratio. When the expression of genes on a chromosome was compressed to a median value, this value was always higher in the trisomic chromosomes for H-MM compared to NH-MM. The data suggests that although gene dosage influence gene expression, the relationship is complex and some genes are more gene dosage dependent than others. Amongst the differentially expressed genes with known function, 33% are involved in mRNA translation/protein synthesis. Of note, 37 of the top 100 differentially expressed genes are involved in these processes. In particular, 60 ribosomal protein (RP) genes are significantly (p<0.05) upregulated in H-MM. This signature in H-MM is not associated with increase proliferation as measured by PCLI. This predominant signature suggests that deregulated protein synthesis may be important for the biology of H-MM. Many of these RPs are involved in the synthesis of product of oncogenic pathways (e.g. MYC, NF-KB pathways) and may mediate the growth and survival of tumor cells. It is therefore possible that these tumor cells may be sensitive to the disruption of mRNA translation/protein synthesis. Targeting the mTOR pathway with rapamycin may therefore be useful for treatment of H-MM.

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Generation of patterns from gene expression data by assigning confidence to differentially expressed genes

Bioinformatics ◽

10.1093/bioinformatics/16.8.685 ◽

2000 ◽

Vol 16 (8) ◽

pp. 685-698 ◽

Cited By ~ 60

Author(s):

E. Manduchi ◽

G. R. Grant ◽

S. E. McKenzie ◽

G. C. Overton ◽

S. Surrey ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Gene Expression Data ◽

Differentially Expressed ◽

Expression Data

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Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽

10.3390/biom10060850 ◽

2020 ◽

Vol 10 (6) ◽

pp. 850 ◽

Cited By ~ 2

Author(s):

Mehran Piran ◽

Reza Karbalaei ◽

Mehrdad Piran ◽

Jehad Aldahdooh ◽

Mehdi Mirzaie ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Expression Profile ◽

Signaling Network ◽

Differentially Expressed ◽

Network Activity ◽

Gene Products ◽

Expression Data ◽

Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

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Association Between Gene Expression Profiles and Commonly Mutated Genes In The Hematopoietic Stem Cells Of Patients With Myelodysplastic Syndromes

Blood ◽

10.1182/blood.v122.21.2779.2779 ◽

2013 ◽

Vol 122 (21) ◽

pp. 2779-2779 ◽

Cited By ~ 1

Author(s):

Andrea Pellagatti ◽

Moritz Gerstung ◽

Elli Papaemmanuil ◽

Luca Malcovati ◽

Aristoteles Giagounidis ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Gene Expression Data ◽

Expression Profiles ◽

Gene Mutations ◽

Gene Expression Profiles ◽

Differentially Expressed ◽

Expression Data ◽

Common Gene ◽

Mutational Status

Abstract A particular profile of gene expression can reflect an underlying molecular abnormality in malignancy. Distinct gene expression profiles and deregulated gene pathways can be driven by specific gene mutations and may shed light on the biology of the disease and lead to the identification of new therapeutic targets. We selected 143 cases from our large-scale gene expression profiling (GEP) dataset on bone marrow CD34+ cells from patients with myelodysplastic syndromes (MDS), for which matching genotyping data were obtained using next-generation sequencing of a comprehensive list of 111 genes involved in myeloid malignancies (including the spliceosomal genes SF3B1, SRSF2, U2AF1 and ZRSR2, as well as TET2, ASXL1and many other). The GEP data were then correlated with the mutational status to identify significantly differentially expressed genes associated with each of the most common gene mutations found in MDS. The expression levels of the mutated genes analyzed were generally lower in patients carrying a mutation than in patients wild-type for that gene (e.g. SF3B1, ASXL1 and TP53), with the exception of RUNX1 for which patients carrying a mutation showed higher expression levels than patients without mutation. Principal components analysis showed that the main directions of gene expression changes (principal components) tend to coincide with some of the common gene mutations, including SF3B1, SRSF2 and TP53. SF3B1 and STAG2 were the mutated genes showing the highest number of associated significantly differentially expressed genes, including ABCB7 as differentially expressed in association with SF3B1 mutation and SULT2A1 in association with STAG2 mutation. We found distinct differentially expressed genes associated with the four most common splicing gene mutations (SF3B1, SRSF2, U2AF1 and ZRSR2) in MDS, suggesting that different phenotypes associated with these mutations may be driven by different effects on gene expression and that the target gene may be different. We have also evaluated the prognostic impact of the GEP data in comparison with that of the genotype data and importantly we have found a larger contribution of gene expression data in predicting progression free survival compared to mutation-based multivariate survival models. In summary, this analysis correlating gene expression data with genotype data has revealed that the mutational status shapes the gene expression landscape. We have identified deregulated genes associated with the most common gene mutations in MDS and found that the prognostic power of gene expression data is greater than the prognostic power provided by mutation data. AP and MG contributed equally to this work. JB and PJC are co-senior authors. Disclosures: No relevant conflicts of interest to declare.

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Deciphering the Infectious Process of Colletotrichum lupini in Lupin through Transcriptomic and Proteomic Analysis

Microorganisms ◽

10.3390/microorganisms8101621 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1621

Author(s):

Guillaume Dubrulle ◽

Adeline Picot ◽

Stéphanie Madec ◽

Erwan Corre ◽

Audrey Pawtowski ◽

...

Keyword(s):

Gene Expression ◽

Mass Spectrometry ◽

Protein Synthesis ◽

Differentially Expressed Genes ◽

Differentially Expressed ◽

White Lupin ◽

Post Inoculation ◽

Infectious Process ◽

Plant Infection ◽

Colletotrichum Lupini

The fungal phytopathogen Colletotrichum lupini is responsible for lupin anthracnose, resulting in significant yield losses worldwide. The molecular mechanisms underlying this infectious process are yet to be elucidated. This study proposes to evaluate C. lupini gene expression and protein synthesis during lupin infection, using, respectively, an RNAseq-based transcriptomic approach and a mass spectrometry-based proteomic approach. Patterns of differentially-expressed genes in planta were evaluated from 24 to 84 hours post-inoculation, and compared to in vitro cultures. A total of 897 differentially-expressed genes were identified from C. lupini during interaction with white lupin, of which 520 genes were predicted to have a putative function, including carbohydrate active enzyme, effector, protease or transporter-encoding genes, commonly described as pathogenicity factors for other Colletotrichum species during plant infection, and 377 hypothetical proteins. Simultaneously, a total of 304 proteins produced during the interaction were identified and quantified by mass spectrometry. Taken together, the results highlight that the dynamics of symptoms, gene expression and protein synthesis shared similarities to those of hemibiotrophic pathogens. In addition, a few genes with unknown or poorly-described functions were found to be specifically associated with the early or late stages of infection, suggesting that they may be of importance for pathogenicity. This study, conducted for the first time on a species belonging to the Colletotrichum acutatum species complex, presents an opportunity to deepen functional analyses of the genes involved in the pathogenicity of Colletotrichum spp. during the onset of plant infection.

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Application of a Deep Matrix Factorization Model on Integrated Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893614666191017094331 ◽

2020 ◽

Vol 15 (4) ◽

pp. 359-367

Author(s):

Yong-Jing Hao ◽

Mi-Xiao Hou ◽

Ying-Lian Gao ◽

Jin-Xing Liu ◽

Xiang-Zhen Kong

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Gene Expression Data ◽

Matrix Factorization ◽

Coefficient Matrix ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Complex Data ◽

Expression Data ◽

Factorization Model

Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features. Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer. Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data. Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples. Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.

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The Transcriptome of Immunomodulator-Resistant Multiple Myeloma

Blood ◽

10.1182/blood-2019-123407 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1772-1772

Author(s):

Moritz Binder ◽

S. Vincent Rajkumar ◽

Martha Q. Lacy ◽

Jessica L. Haug ◽

Angela Dispenzieri ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Overall Survival ◽

Differentially Expressed Genes ◽

Research Funding ◽

Differentially Expressed ◽

Resistant Disease ◽

Kaplan Meier ◽

Gene Coding ◽

Cellular Pathways

Introduction: While the molecular target of immunomodulators such as pomalidomide (POM) and lenalidomide (LEN) has been identified, the mechanisms underlying therapeutic resistance remain incompletely understood. The uniformly emerging resistance to therapy over time in the absence of identifiable cereblon pathway mutations in the majority of patients raises questions about alternative mechanisms including aberrant gene expression. Methods: We performed gene expression profiling using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from patients with relapsed / refractory (RRMM) and newly diagnosed (NDMM) multiple myeloma prior to initiating treatment. Patients were treated on two phase II clinical trial protocols (MC0789: POM ± dexamethasone in RRMM; MC0884: LEN ± dexamethasone in NDMM) between 2007 and 2012. We categorized patients based on their IMWG response as non-responders (SD) and responders (VGPR+). We selected 15 responders and 15 non-responders from MC0789 (n = 30) and compared overall survival, gene expression patterns, and involved cellular pathways between the two groups. We selected 5 responders and 5 non-responders from MC0884 (n = 10) for targeted validation of differentially expressed candidate genes. After data quality control and normalization of gene expression values, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple testing in the discovery set using a false discovery rate-based approach for genome-wide experiments (q-value). We used Gene Ontology and PANTHER pathway analysis for functional annotation of differentially expressed genes. Overall survival estimates were calculated using the Kaplan-Meier method. Computation and visualization were performed in R. Results: Median age at treatment initiation on MC0789 was 65 years (40 - 82), 65% of the patients were male. Pomalidomide resistance was associated with an increase in mortality (median overall survival 1.6 versus 6.4 years, p = 0.009, Kaplan-Meier plot). There were 1076 differentially regulated genes between responders and non-responders (521 up- and 555 down-regulated, q < 0.050 for all genes, volcano plot). Expression of CRBN was 1.5-fold down-regulated in non-responders (q = 0.005). Supervised hierarchical clustering of the top 500 differentially expressed genes demonstrated distinct patterns in pomalidomide-resistant disease (heatmap). Gene ontology analysis revealed protein synthesis as one of the most enriched biological processes (bar graph). Pathway analysis showed a 6-fold enrichment (FDR = 0.007) of the ubiquitin proteasome pathway in pomalidomide-resistant disease. Differentially expressed genes involved key protein degradation pathways, epigenetic modifiers, and transcription factors. Targeted validation in MC0884 revealed 13 common genes with at least 1.5-fold differential expression (5 up- and 8 down-regulated), 12 of which have previously been implicated in the regulation of apoptosis, tumor glucose metabolism, Rho and Wnt signaling, miRNA-driven resistance to chemotherapy, and ubiquitin-dependent protein degradation (Table and Sankey diagram). The most up-regulated gene in non-responders was MYRIP, a gene coding for a vesicle trafficking protein associated with platinum resistance and suppression of pro-apoptotic BCL-2 family members in solid malignancies. The most down-regulated gene was FRZB, a gene coding for a negative regulator of Wnt signaling, previously implicated in the progression of monoclonal gammopathy of undetermined significance to multiple myeloma. Conclusions: Overall survival of patients with pomalidomide-resistant RRMM remains poor. Pomalidomide resistance was associated with differential gene expression in several potentially targetable cellular pathways beyond the known drug target cereblon. Targeted validation of candidate genes revealed common cellular pathways in immunomodulator-resistant disease. Elucidating the exact molecular mechanisms underlying immunomodulator resistance is of considerable interest for biomarker development and the identification of novel therapeutic targets and warrants further exploration. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Alnylam: Research Funding; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Akcea: Consultancy; Takeda: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

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The Gene Expression Profile of T-Cell Prolymphocytic Leukemia with inv(14)(q11q32) Reflects the Pattern of Chromosomal Imbalances.

Blood ◽

10.1182/blood.v108.11.2290.2290 ◽

2006 ◽

Vol 108 (11) ◽

pp. 2290-2290

Author(s):

Jan Dürig ◽

Stefanie Bug ◽

Ludger Klein-Hitpass ◽

Tanja Boes ◽

Thomas Jöns ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Gene Dosage ◽

Differentially Expressed ◽

Aggressive Lymphoma ◽

Normal Donor ◽

Gene Dosage Effect ◽

Prolymphocytic Leukemia ◽

Chromosomal Imbalances ◽

Chip Technology

Abstract T-cell prolymphocytic leukemia (T-PLL) is an aggressive lymphoma derived from mature T-cells, which is in most cases characterized by the presence of an inv(14)(q11q32) and a characteristic pattern of secondary chromosomal aberrations. DNA microarray technology was employed to compare the transcriptomes of eight immunomagnetically purified CD3+ normal donor derived peripheral blood cells with five highly purified inv(14)-positive T-PLL blood samples. In comparison between the two experimental groups 740 genes were identified as differentially expressed including functionally important genes involved in lymphomagenesis, cell cycle regulation, apoptosis and DNA repair. Notably, the differentially expressed genes were found to be significantly enriched in genomic regions affected by recurrent chromosomal imbalances. Up-regulated genes clustered significantly on chromosome arms 6p and 8q and down-regulated genes on 6q, 8p, 10p, 11q and 18p. High-resolution copy-number determination using SNP-chip technology in twelve inv(14)/t(14;14)-positive T-PLL including those analyzed for gene expression refined chromosomal breakpoints as well as regions of imbalances. In conclusion, combined transcriptional and molecular cytogenetic profiling identified novel specific chromosomal loci and genes which are likely to be involved in disease progression and suggests a gene dosage effect as a pathogenic mechanism in T-PLL.

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The NOD/SCID Xenograft Model Provides Clinically-Relevant Insights Into Glucocorticoid-Induced Gene Expression In Childhood B-Cell Precursor Acute Lymphoblastic Leukemia (ALL)

Blood ◽

10.1182/blood.v116.21.2493.2493 ◽

2010 ◽

Vol 116 (21) ◽

pp. 2493-2493

Author(s):

Vivek A Bhadri ◽

Mark J Cowley ◽

Warren Kaplan ◽

Richard B Lock

Keyword(s):

Gene Expression ◽

Acute Lymphoblastic Leukemia ◽

Mouse Model ◽

B Cell ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Lymphoblastic Leukemia ◽

Xenograft Model ◽

Differentially Expressed ◽

Cell Precursor

Abstract Abstract 2493 Introduction. Glucocorticoids (GC) such as prednisolone (Pred) and dexamethasone (Dex) are critical drugs in multi-agent chemotherapy protocols used to treat acute lymphoblastic leukemia (ALL). The NOD/SCID ALL xenograft mouse model is a clinically relevant model in which the mice develop a systemic leukemia which retains the fundamental biological characteristics of the original disease. Here we report the results of a study evaluating the NOD/SCID xenograft model to investigate GC-induced gene expression. Methods. Cells from a GC-sensitive xenograft derived from a child with B-cell precursor ALL were inoculated into NOD/SCID mice. Engraftment, defined as the proportion of human vs mouse CD45+ cells in the peripheral blood, was monitored by serial weekly tail-vein sampling. When engraftment levels reached >50%, the mice were randomised and treated with either dexamethasone 15 mg/kg or vehicle control by intraperitoneal injection, and harvested at 0, 8, 24 or 48 h thereafter. The 48 hour groups received a second dose of vehicle or Dex at 24 hours. At the defined timepoints, the mice were euthanized and lymphoblasts harvested from the spleen. RNA was extracted, amplified and hybridised onto Illumina WG-6 V3 chips. The data was pre-processed using variance-stabilisation transformation, and quantile normalisation. Differential expression was determined using limma by comparing all treated groups to time 0, with the positive False Discovery Rate correction for multiple testing. Hierarchical clustering was used to compare groups to each other. The stability of results when reducing the number of replicates was assessed using the Recovery Score method. Functional analysis was performed using gene set enrichment analysis (GSEA) and comparison to publicly available microarray data using parametric GSEA. Results. The 8 hour Dex-treated timepoint had the most number of significantly differentially expressed genes (see Table), with fewer observed at the 24 and 48 hour Dex-treated timepoints. There was minimal significant differential gene expression across the time-matched controls. At the 8 hour timepoint, ZBTB16, a known GC-induced gene, was the most significantly upregulated gene. Other significantly differentially expressed genes included TSC22D3 and SOCS1, both downstream targets of the glucocorticoid receptor (upregulated), and BCL-2 and C-MYC (downregulated). GSEA at 8 hours revealed a significant upregulation of catabolic pathways and downregulation of pathways associated with cell proliferation, particularly C-MYC. GSEA at 24 hours revealed enrichment of pathways associated with NF-kB. Replicate analysis revealed that at the 8 hour Dex treated timepoint, a dataset with high signal and differential expression, using data from 3 replicates instead of 4 resulted in excellent recovery scores of >0.9. However at other timepoints with less signal very poor recovery scores were obtained using 3 replicates. We compared our data to publicly available datasets of GC-induced genes in ALL (Schmidt et al, Blood 2006; Rainer et al, Leukemia 2009) using parametric GSEA, which revealed that the 8 hour gene expression data obtained from the NOD/SCID xenograft model clustered with data from primary patient samples (Schmidt) rather than the cell line data (Rainer). The 24 and 48 hour datasets clustered separately from all other datasets by this method, reflecting fewer and predominantly downregulated gene expression at these timepoints. Conclusions: The NOD/SCID xenograft mouse model provides a reproducible experimental model system in which to investigate clinically-relevant mechanisms of GC-induced gene regulation in ALL; the 8 hour timepoint provides the highest number of significantly differentially expressed genes; time-matched controls are redundant and excellent recovery scores can be obtained with 3 replicates. Disclosures: No relevant conflicts of interest to declare.

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