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

Blood ◽  
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.

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

Blood ◽  
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.

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.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3006-3006
Author(s):  
Wee-Joo Chng ◽  
Scott Van Wier ◽  
Gregory Ahmann ◽  
Tammy Price-Troska ◽  
Kim Henderson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
Multiple Myeloma ◽  
Protein Synthesis ◽  
Differentially Expressed Genes ◽  
Gene Dosage ◽  
Lymphoblastic Leukemia ◽  
Differentially Expressed ◽  
Expression Data ◽  
Gene Dosage Effect

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.

scholarly journals Comprehensive high-throughput meta-analysis of differentially expressed microRNAs in transcriptomic datasets reveals significant disruption of MAPK/JNK signal transduction pathway in Adult T-cell leukemia/lymphoma

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shahrzad Shadabi ◽  
Nargess Delrish ◽  
Mehdi Norouzi ◽  
Maryam Ehteshami ◽  
Fariba Habibian-Sezavar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
T Cell ◽  
Network Analysis ◽  
High Throughput ◽  
Target Gene ◽  
Differentially Expressed ◽  
Cell Leukemia ◽  
Adult T Cell Leukemia ◽  
Healthy Donors

Abstract Background Human T-lymphotropic virus 1 (HTLV-1) infection may lead to the development of Adult T-cell leukemia/lymphoma (ATLL). To further elucidate the pathophysiology of this aggressive CD4+ T-cell malignancy, we have performed an integrated systems biology approach to analyze previous transcriptome datasets focusing on differentially expressed miRNAs (DEMs) in peripheral blood of ATLL patients. Methods Datasets GSE28626, GSE31629, GSE11577 were used to identify ATLL-specific DEM signatures. The target genes of each identified miRNA were obtained to construct a protein-protein interactions network using STRING database. The target gene hubs were subjected to further analysis to demonstrate significantly enriched gene ontology terms and signaling pathways. Quantitative reverse transcription Polymerase Chain Reaction (RTqPCR) was performed on major genes in certain pathways identified by network analysis to highlight gene expression alterations. Results High-throughput in silico analysis revealed 9 DEMs hsa-let-7a, hsa-let-7g, hsa-mir-181b, hsa-mir-26b, hsa-mir-30c, hsa-mir-186, hsa-mir-10a, hsa-mir-30b, and hsa-let-7f between ATLL patients and healthy donors. Further analysis revealed the first 5 of DEMs were directly associated with previously identified pathways in the pathogenesis of HTLV-1. Network analysis demonstrated the involvement of target gene hubs in several signaling cascades, mainly in the MAPK pathway. RT-qPCR on human ATLL samples showed significant upregulation of EVI1, MKP1, PTPRR, and JNK gene vs healthy donors in MAPK/JNK pathway. Discussion The results highlighted the functional impact of a subset dysregulated microRNAs in ATLL on cellular gene expression and signal transduction pathways. Further studies are needed to identify novel biomarkers to obtain a comprehensive mapping of deregulated biological pathways in ATLL.

Gene Expression Reveals Two Distinct Biological Groups within T-Cell Prolymphocytic Leukaemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 4366-4366
Author(s):  
Nnenna Osuji ◽  
Ilaria Del Giudice ◽  
Tim Dexter ◽  
Estella Matutes ◽  
Vasantha Brito-Babapulle ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Chromosome 8 ◽  
Rt Pcr ◽  
Double Positive ◽  
Reciprocal Translocations ◽  
Prolymphocytic Leukemia ◽  
Group A ◽  
Differential Responses ◽  
Group B

Abstract T-cell prolymphocytic leukemia (T-PLL) is rare and presents with widespread disease. Indolent presentations are seen but eventually progress. The disease shows marked chemoresistance and is best treated with the monoclonal anti-CD52 antibody (CAMPATH). Prolymphocytes show a post-thymic phenotype and are CD4+CD8− (65%), CD4−CD8+ (10%) or CD4+CD8+ (25%). This double positive phenotype, raises questions about the putative ontology of T-PLL. Morphological heterogeneity, with typical (75%), small cell (20%) and cerebriform/sezary-like variants (5%) is described. Inversions or reciprocal translocations of chromosome 14 involving breakpoints at q11 (TCR a/d) and q32.1 (TCL1 and TCL1b) are seen (~ 80%). Other common abnormalities involve chromosome 8, translocation (X;14)(q28;q11) and, ATM (11q23). We investigated the clinico-pathological heterogeneity in T-PLL, at the level of the transcriptome and evaluated the ability of gene expression profiling to sub-classify T-PLL. Total RNA was extracted from blood prolymphocytes (>92% purity) of 22 patients. cDNA synthesis followed by biotin-labelled cRNA synthesis was carried out as per Affymetrix protocols. Fragmented cRNA was hybridized to the Human U133 PLUS2 GeneChip array (54K probes). Microarray services were provided by MRC geneservice (UK HGMP Resource Centre). Hierarchical clustering of samples was performed using a filtered gene set (12,456) and >4 different algorithims. Prediction analysis for micoarray (PAM) and significance analysis of microarray (SAM) were used to evaluate class performance, and partition genes using pre-defined labels of immunophenotype, karyotype, response and morphology. Validation was performed by RT-PCR in a subset of genes.Unsupervised analysis robustly and reproducibly partitioned samples into 2 groups; A (n=8) and B (n=14). SAM analysis identified 4487 differentially expressed transcripts (false discovery rates <1%), >40% of which showed >2-fold difference in expression between the groups. There was no statistical difference in age, immunophenotype or karyotype betweeen groups, however, differential response to CAMPATH was seen. PAM analysis refined a sub-group of ~123 genes which most efficiently differentiated these groups. Group A showed significantly higher rates of non-response and progressive disease as compared to group B (n=14, p=0.036). Key differences related to apoptosis and cell-cycle associated gene expression. Down regulation of caspases (CASP1, CASP2,CASP4, CARD8 and CASP8AP2), cyclins (CCNC, CCND2, CCND3, CCNG1, CCNI, CCNT2), bcl-2, HDAC1, HIPK2, IL6R and ATM were frequent in group A with upregulation of genes implicated in NF-kB (TRAF4, SQSTM1) and TNF pathways (LMNA, ARTS-1), as well as transcription factors such as ATF-3. CD52 expression was ~2-fold higher in group B and may explain in part, differential responses to CAMPATH. RT-PCR validated gene expression data for LMNA and ATF-3. Despite the small numbers, algorithim-independent segregation into 2 consistent groups, in conjunction with the magnitude of gene differences, presence of many mutually exclusive divisions, and low prediciton errors, imply that the 2 identified profiles arise from fundamental differences at a regulatory level and thus likely represent a generalisable classification for T-PLL. Differential responses to CAMPATH may be a sub-feature of this grouping.

Biomarkers of Vorinostat Resistance in Cutaneous T-Cell Lymphoma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1381-1381
Author(s):  
Chunlei Zhang ◽  
Baoqiang Li ◽  
Rakhshandra Talpur ◽  
C. Cameron Yin ◽  
Madeleine Duvic
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Mononuclear Cells ◽  
Cell Lymphoma ◽  
T Cell Lymphoma ◽  
Differentially Expressed ◽  
Cytokine Signaling ◽  
Cutaneous T Cell Lymphoma ◽  
Phase Ii Studies

Abstract Profiling gene expression with DNA microarray technology has elucidated novel therapeutic targets and led the approval of a number of targeted therapeutic agents for the treatment of cancer. Vorinostat (suberoylanilide hydroxamic acid, SAHA) is a pan-histone deacetylase (HDAC) inhibitor that has demonstrated an overall response rate of approximately 24–30% in two phase II studies of cutaneous T cell lymphoma (CTCL) patients. There are currently no known specific biomarkers to indicate resistance to vorinostat. To identify genes resistant to vorinostat we compared profiles using the Aligent whole human genome oligo microarrays containing ∼41,000 genes/transcripts in vitro in vorinostat-resistant MJ and -sensitive HH CTCL cell lines treated with 1 μM of vorinostat for 24 hours and compared them to patients’ peripheral blood mononuclear cells (PBMCs) before and during oral therapy. There were 3151 (7.7%) genes/transcripts differentially expressed in vitro in treated resistant MJ cells compared to untreated vehicle control (p < 0.001). We also studied differential gene expression in two clinically resistant Sézary patients’ PBMCs taken at baseline and four weeks after oral vorinostat (400 mg daily or 300 mg bid 3 days/wk). In patients’ PBMCs, 585 (1.4%) and 2744 (6.7%) differentially expressed genes/transcripts (p < 0.001) were identified, respectively. Genes that were up-regulated both in vitro and in vivo included a tumor necrosis factor receptor super-family member 11a (TNFRSF11a or RANK), matrix metallopeptidase 9 (MMP9), suppressor of cytokine signaling 3 (SOCS3), vinculin (VCL) and KIAA1840. Genes that were down-regulated in both included adenylate kinase 3-like 1 (AK3L1), leucine rich repeat and fibronectin type III domain containing 4 (LRFN4), and AL359650. Increased RANK, MMP9 and SOCS3 mRNA expression in MJ compared to HH cells and in three resistant versus three vorinostat responding Sézary patients’ PBMCs was confirmed using quantitative real-time PCR. In conclusion, our results suggest that oligonucleotide microarray analysis may identify biomarkers of resistance to vorinostat which would be helpful to select patients who may not benefit from treatment. These findings provide the rationale for future functional studies and development of more effective use of HDAC inhibitors for CTCL patients.

scholarly journals Transcriptional Profiling Reveals Strong Impact of Major Molecular Disease Subgroups and Mixed Epistasis in Chronic Lymphocytic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1742-1742
Author(s):  
Thorsten Zenz ◽  
Almut Luetge ◽  
Junyan Lu ◽  
Huellein Jennifer ◽  
Sascha Dietrich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Copy Number ◽  
Epistatic Interaction ◽  
Gene Dosage ◽  
Copy Number Variants ◽  
Gene Mutations ◽  
Gene Dosage Effect ◽  
Mutation Status ◽  
Trisomy 12

While recurrent mutations in CLL have been extensively catalogued, how driver mutations affect disease phenotypes remains incompletely understood. To address this, we performed RNA sequencing on 184 CLL patient samples and linked gene expression changes to molecular subgroups, gene mutations and copy number variants. Library preparation was performed according to the Illumina TruSeq RNA sample preparation v2 protocol. Samples were paired-end sequenced and two to three samples were multiplexed per lane on Illumina HiSeq 2000, Illumina HiSeq3000/4000 or Illumina HiSeqX machines. Raw RNA-seq reads were demultiplexed and quality control was performed using FastQC version 0.11.5. Internal trimming with STAR version 2.5.2a was used to remove adapters before mapping. Mapping was performed using STAR version 2.5.2a against the Ensembl human reference genome release 75 (Homo sapiens GRCh37.75). STAR was run in default mode with internal adapter trimming using the clip3pAdapterSeq option. Mapped reads were summarized into counts using htseq-count version 0.9.0 with default parameters and union mode. Thus, only fragments unambiguously overlapping with one gene were counted. The count data were then imported into R (version 3.4) for subsequent analysis. We identified robust and previously unknown gene expression signatures associated with recurrent copy number variants (including trisomy 12, del11q22.3, del17p13, del18p12 and gain8q24), gene mutations (TP53, BRAF and SF3B1) and the mutation status of the immunoglobulin heavy-chain variable region (IGHV). The most profound gene expression changes were associated with IGHV, methylation groups and trisomy 12. We found evidence for a significant influence of CNVs beyond the gene dosage effect. In line with these observations, unsupervised clustering showed that these major biological subgroups form distinct clusters and are discernible by unsupervised clustering (IGHV, methylation groups and trisomy 12). We found 3275 genes significantly differentially expressed between M-CLL and U-CLL after adjustment for multiple testing using the method of Benjamini and Hochberg for FDR = 1% . In total 9.5 % of variance within gene expression was associated with the IGHV status. These data suggest a much larger impact on transcriptional changes than previously detected (Ferreira et al. 2014), a finding much more in line with the key impact of IGHV on clinical course and biology of disease. We found distinct expression pattern of up- and downregulated genes for trisomy 12 samples. Even though many upregulated genes are located on chromosome 12, the majority of differentially expressed genes are indeed distributed among the other chromosomes and cannot be therefore not be ascribed to a simple gene dosage effect. To investigate the role of genetic interactions, we tested the collaborative effect on gene expression phenotypes. We investigated epistatic gene expression changes for IGHV status and trisomy 12. Epistasis was defined as a non-linear effect on gene expression between sample with both variants co-occuring and the single variants alone. In total 893 genes showed specific expression pattern in a combined genotype (padj<0.1). These expression changes differed from the expected change by simple combination of the single variant's effects. We observed different ways of epistatic interaction and clustered genes by them. In total, we identified five cluster of genes representing different ways of mixed epistasis as inversion down, suppression, different degrees of buffering and inversion up. To further investigate this interaction we used enrichment tests for genes in the different mixed epistasis cluster. We found genes upregulated in trisomy12 U-CLL sample, but suppressed in M-CLL trisomy12 samples were enriched in Wnt beta catenin and Notch signaling. In summary, our study provides a comprehensive reference data set for gene expression in CLL. We show that IGHV mutation status, recurrent gene mutations and CNVs drive gene expression in a previously underappreciated fashion. This includes epistatic interaction between trisomy 12 and IGHV. Using a novel way to describe coordinated changes we can group genes into sets related to buffering, inversion and suppression. Disclosures Sellner: Takeda: Employment.

scholarly journals Comparative gene expression profiling of T-cell acute lymphoblastic leukemias (T-ALL) expressing the T-cell receptor 𝛂β or γδ (T-ALL𝛂β / T-ALLγδ)

2019 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
T Cell ◽  
T Cell Receptor ◽  
Expression Profiling ◽  
Lymphoblastic Leukemia ◽  
Cell Receptor ◽  
Differentially Expressed ◽  
Aggressive Form ◽  
Cell Acute Lymphoblastic Leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive form of leukemia with inferior treatment outcomes. The T-cell receptor (TCR) exists in two major forms: the 𝛂βTCR or the γδTCR, and 20-35% of T-ALL cases express either the 𝛂βTCR or the γδTCR (T-ALL𝛂β or T-ALLγδ). Using a published dataset from a cohort of 14 TCR+ T-ALL patients, I found a series of genes that are differentially expressed among patients T-ALL𝛂β or T-ALLγδ. Any number of these differentially expressed genes may be a scientifically and/or clinically actionable target in TCR+ T-ALL.

DNA mismatch repair deficiency and benefit from adjuvant bevacizumab in NSABP C-08: Molecular profiling results.

2012 ◽  
Vol 30 (30_suppl) ◽  
pp. 55-55
Author(s):  
Katherine L. Pogue-Geile ◽  
Noriko Tanaka ◽  
Patrick Gavin ◽  
Greg Yothers ◽  
Linda H. Colangelo ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Mismatch Repair ◽  
T Cell Activation ◽  
Cell Activation ◽  
Statistical Tests ◽  
Differentially Expressed ◽  
Braf Mutations ◽  
Study Gene Expression

55 Background: The purpose of this study was to identify biomarkers that define a subset of patients who received benefit from bevacizumab (bev) in NSABP trial C-08, even though bev did not improve outcomes over standard adjuvant chemotherapy (CT) in the treatment of stage II and III colon cancer. Methods: A randomly selected cohort of C-08 cases (N=500) were profiled for whole genome expression (N=445) and for mutations (N=463) in KRAS, NRAS, PIK3CA, and MET. BRAF mutations and mismatch repair (MMR) status were profiled on the available cases (N=1,764 and 1,993, respectively). Cox proportional hazard models were used to assess prognosis and prediction for the value of bev using overall survival (0S) and time to recurrence (TTR) as end points. Results: The effect of bev was different for MMR deficient (MMR-d) and proficient tumors for OS (interaction p=.035) but not TTR (interaction p=.08). Patients with MMR-d (N=252) tumors showed a significant benefit from the addition of bev to CT for OS (hazard ratio =0.52 (95% CI: 0.29-0.94, p=0.028). KRAS, NRAS, PIK3CA, and MET were not significant for interaction with bev in the discovery cohort. BRAF mutations were associated with MMR status (p<.0001) and the prognostic value of MMR depended on BRAF for TTR (interaction p=.027) but not OS (interaction p=.31). The effect of bev was independent of BRAF (interaction p=.28 TTR and .37 OS). Three-factor interaction tests for bev, MMR, and BRAF were not significant for either endpoint. Gene expression analysis with BRB array tools identified 5 BioCarta pathways (p<0.05) which differentially expressed in 4 statistical tests; 4 of these pathways were directly or indirectly involved in T cell activation and one was involved in the activation of VEGF. Conclusions: Patients in C-08 with MMR-d tumors received benefit from bev treatment but these results need to be validated in a separate study. Gene expression data suggest that T-cells may be differentially expressed based on MMR status. Activation of VEGF has been shown to suppress T-cell development (Ohm et al. Blood. 2003:10;4878). A speculative possibility for the benefit of bev in MMR-d tumors may be due to blocking of VEGF, releasing T cells from VEGF suppression.

Analysis of Gene Expression in Patients with Acute Graft Versus Host Disease (GVHD) Following Hematopoietic Stem Cell Transplantation (HSCT).

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 729-729 ◽  
Author(s):  
Laura Tabellini ◽  
Ming-Tseh Lin ◽  
Wenhong Fan ◽  
Era Pogosova-Agadjanyan ◽  
Bart Stephens ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Blood Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Cellular Stress Response ◽  
Statistical Criterion ◽  
Gene Profiles ◽  
Clinically Significant

Abstract To better understand the cellular events that precede onset of clinically significant acute GVHD, a complication of allogeneic HSCT, we compared global gene expression profiles in patients 3 (days 18–22) and 4 (days 28–32) weeks after transplant. Patients in this study underwent myeloablative-conditioning regimen prior to receiving a T cell replete PBSCT from a related (n=9) or unrelated donor (5 HLA matched and 4 mismatched). Blood was obtained prospectively at scheduled times (prior to administration of glucocorticosteroids). RNA was isolated from nucleated blood cells and biotin-labeled cRNA hybridized on Affymetrix HG-U133A chips. MAS 5.0 software was used to extract gene expression values. We initially compared gene expression profiles between 15 patients 3 weeks post-HSCT and 10 normal controls. A total of 1176 genes were differentially expressed with statistical criterion of NFD (number of false discovery) equal to 10. Gene profiles for these 1176 genes were compared between 8 patients who subsequently developed GVHD within 1–5 days and 7 patients who remained GVHD free for 90 days. A limited number of genes were differentially expressed with NFD=1: 3 genes in GVHD patients showed increased expression and 6 showed decreased expression. A second set of experiments was performed to compare changes occurring within individual patients over an interval of 7 days (between weeks 3 and 4) prior to diagnosis of clinically significant GVHD (onset between days 27–32). We used a pair-wise comparison with selection criterion NFD=1. Increased expression prior to GVHD was observed in 55 genes and decreased expression in 88 genes. Approximately 50 of these genes were associated with inflammation and cellular stress response. Using the same statistical criterion we compared gene profiles between weeks 3 and 4 for 3 patients who remained GVHD-free for at least 90 days. Fewer changes were observed with increased expression occurring in 6 genes and decreased expression in 14 genes. These differentially expressed genes did not overlap with the candidate genes associated with the development of GVHD. Genes showing expression changes in GVHD included: Increased Decreased Inflamamtory Response IFN-α10, IL8, IL17 Transcription Factors NFATC1 GATA3 Cell Surface/Signal Transduction CD6, CD7, CD8, TCR-interacting molecule, MAP4K1, TNFRSF25, Effectors Molecules GRMM AICD/Apoptosis TOSO, BAX Cellular Stress Response DDAH1 DLAT, PKC1, COX5B These results suggest that extensive complex gene expression changes occur among nucleated blood cells during the early post-transplant period presumably due to extensive alterations in cellular activation occurring during reconstitution. The preliminary results of the longitudinal analysis of changes occurring within individual patients indicate that early post-transplant studies are feasible and that they may be informative for yielding insight into the molecular events associated with development of clinically significant GVHD. These data also indicate a paradoxical decrease in certain T cell associated genes in GVHD. However alloimmune induced T cell activation may lead to AICD and previous studies have demonstrated increased apoptosis among peripheral blood T cells in GVHD patients. Further studies including gene expression profiling of isolated T cells will be necessary to determine if this approach can be useful in identifying a molecular “signature” for GVHD that may be useful for diagnosis and monitoring.

Sign in / Sign up

Export Citation Format

Share Document