Use of gene expression analysis of periampullary carcinomas to identify biliary-like and intestinal-like subgroups of ampullary and duodenal carcinomas.

2011 ◽  
Vol 29 (4_suppl) ◽  
pp. 161-161
Author(s):  
M. J. Overman ◽  
J. Zhang ◽  
G. R. Varadhachary ◽  
R. F. Hwang ◽  
M. Kapoor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Kras Mutations ◽  
Histologic Subtype ◽  
Therapeutic Implications ◽  
Periampullary Carcinomas

161 Background: Though adenocarcinomas of the ampulla of Vater are classified as biliary cancers, the epithelium of origin and treatment approach for these rare tumors remains controversial. We compared the gene expression profiles of ampullary carcinomas with that of known periampullary carcinomas. Methods: We analyzed 32 fresh-frozen resected untreated periampullary carcinomas (8 pancreatic, 2 extrahepatic biliary, 8 non-ampullary duodenal, and 14 ampullary) with verified histology and >70% tumor tissue using the Affymetrix U133 Plus 2.0 genome array. Hierarchical clustering of all samples based upon pancreatic and duodenal differentially expressed genes and unsupervised hierarchical clustering was done. Ampullary and duodenal samples were analyzed for histologic subtype (pancreaticobiliary, intestinal, mixed), MSI by PCR, CDX-2 by IHC and KRAS and PI3K mutations by mass spectroscopy-based sequencing (Sequenom). Results: We identified 3 subgroups: pancreatic (8 pancreatic, 1 duodenal), biliary-like (4 duodenal, 7 ampullary, 2 biliary) and intestinal-like (3 duodenal, 7 ampullary). The intestinal-like subgroup had a significantly improved RFS (p=0.03) and OS (p =0.04) compared to the biliary-like subgroup, after stratification by grade and stage. Unsupervised clustering of only ampullary and duodenal samples identified very similar good prognostic (4 duodenal, 5 ampullary) and bad prognostic groups (3 duodenal, 9 ampullary) with 3-year RFS 75% vs. 31%, p =0.05 and 3-year OS 100% vs. 27%, p =0.01. These 2 groups showed no statistically significant differences in adenoma (56% vs 25%), poor differentiation (11% vs 42%), T4 (33% vs 25%), N1 (67% vs 100%), MSI-high (22% vs 0%), KRAS mutations (33% vs 17%), or PI3K mutations (0% vs 17%). CDX-2 expression (100% vs 50%, p=.04) and intestinal histologic subtype (100% vs 0%, p <0.01) were more common in the good prognostic group. Conclusions: Gene expression analysis classifies ampullary carcinomas with duodenal carcinomas and identifies a good prognosis intestinal-like group and a poor prognosis biliary-like group. These findings have therapeutic implications. [Table: see text]

Comparative gene expression analysis of zebrafish and mammals identifies common regulators in hematopoietic stem cells

Blood ◽  
2010 ◽  
Vol 115 (2) ◽  
pp. e1-e9 ◽  
Author(s):  
Isao Kobayashi ◽  
Hiromasa Ono ◽  
Tadaaki Moritomo ◽  
Koichiro Kano ◽  
Teruyuki Nakanishi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Hematopoietic Stem Cells ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Side Population ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Junction ◽  
Hematopoietic Stem

Abstract Hematopoiesis in teleost fish is maintained in the kidney. We previously reported that Hoechst dye efflux activity of hematopoietic stem cells (HSCs) is highly conserved in vertebrates, and that Hoechst can be used to purify HSCs from teleost kidneys. Regulatory molecules that are strongly associated with HSC activity may also be conserved in vertebrates. In this study, we identified evolutionarily conserved molecular components in HSCs by comparing the gene expression profiles of zebrafish, murine, and human HSCs. Microarray data of zebrafish kidney side population cells (zSPs) showed that genes involved in cell junction and signal transduction tended to be up-regulated in zSPs, whereas genes involved in DNA replication tended to be down-regulated. These properties of zSPs were similar to those of mammalian HSCs. Overlapping gene expression analysis showed that 40 genes were commonly up-regulated in these 3 HSCs. Some of these genes, such as egr1, gata2, and id1, have been previously implicated in the regulation of HSCs. In situ hybridization in zebrafish kidney revealed that expression domains of egr1, gata2, and id1 overlapped with that of abcg2a, a marker for zSPs. These results suggest that the overlapping genes identified in this study are regulated in HSCs and play important roles in their functions.

Sub-Classification of Hyperdiploid Myeloma Using Global Gene Expression Profiling and SNP-Based Mapping Arrays.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3390-3390
Author(s):  
Brian A. Walker ◽  
Paola E. Leone ◽  
Matthew W. Jenner ◽  
David C. Johnson ◽  
David Gonzalez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Response ◽  
Response To Therapy ◽  
Genomic Change ◽  
Group 5 ◽  
Mapping Arrays ◽  
Group 1

Abstract The translocation/cyclin classification system in myeloma does not neatly define subgroups of hyperdiploidy (HRD) and we sought a more definitive sub-classification. Using 131 pre-treatment samples (49 HRD with no split IgH locus by FISH) we defined subgroups using both supervised and unsupervised hierarchical clustering of gene expression profiles. RNA was purified from CD138+ cells, amplified using a 2-cycle IVT and hybridised onto U133 Plus 2 GeneChips. On 30 of the 49 HRD samples we also performed 500K SNP mapping arrays to define the true extent of the genomic change in HRD. The most common trisomic chromosomes were 15 (97%), 9 (86%), 19 (80%), 5 (77%), 11 (74%), 3 (64%), 21 (54%) and 7 (54%). There was no association between HRD and any of the major genetic abnormalities (1p, 1q, 6q, 8p, 13, 16q and 17p) compared to the non-HRD (NHRD) group. Many interstitial deletions were seen in all HRD samples, on both odd and even numbered chromosomes. However, using gene mapping alone it was not possible to globally sub-classify HRD myeloma. We compared NHRD and HRD sample gene expression profiles, removing differences between t(4;14) and t(11;14) cases in the NHRD group. This analysis showed that HRD samples segregate into 2 groups; one with a pattern distinct to NHRD samples and another containing genes that are up-regulated in both HRD and NHRD samples. In this analysis 176 genes were up-regulated in the HRD samples and were predominantly located on the trisomic chromosomes, especially 19, 11, 9 and 5. These genes showed a predominant upregulation of HGF and TRAIL, and down-regulation of TRAIL-R2 compared to NHRD samples. Unsupervised hierarchical clustering split the HRD samples into 5 distinct groups suggesting that there are distinct pathological entities. Group 1 overexpressed 90 genes including BCL2, CCNL1 (cyclin L1) and CDK6, consistent with a proliferation signature. Group 2 overexpressed interferon inducible genes including IFI6, IFI27, IFIT1 as well as TRAIL. Group 3 upregulated genes included IL8, MMP9 and TIMP2. Group 4 upregulated transcripts include neurexophilin 3. Group 5 was less well defined but contained transcripts for CCND2, WNT5A and CXCR4. To define clinically relevant subgroups the HRD samples were clustered comparing response or no response to induction chemotherapy. Analysis showed that Group 1 cases cluster together and were either non or minimal responders. This is consistent with the Group 1 cases over-expressing cell-cycle and proliferation related genes. Group 5 clustered together and were either complete or partial responders, and had a low expression of the genes over expressed by Group 1. The non-responder group overexpressed 58 genes and include MMSET-like 1 (in a region on 8p paralogous to 4p containing FGFR1), DVL3 (dishevelled homolog 3) and CCNL1. 23 genes were over expressed in the complete response group including caspase 1 and manic fringe homolog. The unsupervised HRD cluster and the supervised response cluster shared 10 genes, including CCNL1 and ASS. We have used both genetic and expression data to further define the HRD sub-group in terms of gene expression signatures and response to therapy and have identified 5 groups, of which Group 1 has a proliferation signature and poor response to induction therapy.

Combining Gene Expression Profiles and Clinical Parameters for Risk Stratification in Medulloblastomas

2004 ◽  
Vol 22 (6) ◽  
pp. 994-998 ◽  
Author(s):  
Ana Fernandez-Teijeiro ◽  
Rebecca A. Betensky ◽  
Lisa M. Sturla ◽  
John Y.H. Kim ◽  
Pablo Tamayo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Gene Expression Profiles ◽  
Clinical Parameters ◽  
Histologic Subtype ◽  
Clinical Variables ◽  
Outcome Predictions

Purpose Stratification of risk in patients with medulloblastoma remains a challenge. As clinical parameters have been proven insufficient for accurately defining disease risk, molecular markers have become the focus of interest. Outcome predictions on the basis of microarray gene expression profiles have been the most accurate to date. We ask in a multivariate model whether clinical parameters enhance survival predictions of gene expression profiles. Patients and Methods In a cohort of 55 young patients (whose medulloblastoma samples have been analyzed previously for gene expression profile), associations between clinical and gene expression variables and survival were assessed using Cox proportional hazards models. Available clinical variables included age, stage (ie, the presence of disseminated disease at diagnosis), sex, histologic subtype, treatment, and status. Results Univariate analysis demonstrated expression profiles to be the only significant clinical prognostic factor (P = .03). In multivariate analysis, gene expression profiles predicted outcome independent of other criteria. Clinical criteria did not significantly contribute additional information for outcome predictions, although an exploratory analysis noted a trend for decreased survival of patients with metastases at diagnosis but favorable gene expression profile. Conclusion Gene expression profiling predicts medulloblastoma outcome independent of clinical variables. These results need to be validated in a larger prospective study.

scholarly journals Global Gene Expression Analysis of Yeast Cells during Sake Brewing

2006 ◽  
Vol 72 (11) ◽  
pp. 7353-7358 ◽  
Author(s):  
Hong Wu ◽  
Xiaohong Zheng ◽  
Yoshio Araki ◽  
Hiroshi Sahara ◽  
Hiroshi Takagi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Yeast Cells ◽  
Yeast Gene ◽  
High Concentration ◽  
Brewing Process ◽  
Global Gene Expression Analysis

ABSTRACT During the brewing of Japanese sake, Saccharomyces cerevisiae cells produce a high concentration of ethanol compared with other ethanol fermentation methods. We analyzed the gene expression profiles of yeast cells during sake brewing using DNA microarray analysis. This analysis revealed some characteristics of yeast gene expression during sake brewing and provided a scaffold for a molecular level understanding of the sake brewing process.

scholarly journals NormExpression: an R package to normalize gene expression data using evaluated methods

2018 ◽  
Author(s):  
Zhenfeng Wu ◽  
Weixiang Liu ◽  
Xiufeng Jin ◽  
Deshui Yu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
R Package ◽  
Data Normalization ◽  
Expression Data ◽  
Normalization Methods ◽  
Normalize Gene Expression

AbstractData normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

A genomic strategy to refine prognosis in early stage non-small cell lung carcinoma (NSCLC)

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 7026-7026
Author(s):  
D. H. Harpole ◽  
R. Petersen ◽  
S. Mukherjee ◽  
A. Bild ◽  
H. Dressman ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Smoking History ◽  
Histologic Subtype ◽  
Multiple Gene ◽  
Risk Of Recurrence

7026 Background. Although stage-specific classification identifies appropriate populations for adjuvant chemotherapy, this is likely an imprecise predictor for the individual patient with early stage NSCLC. Methods. Using previously-described methodologies that employ DNA microarray data, multiple gene expression profiles (‘metagenes’) that predict risk of recurrence in patients with stage I disease were identified. This analysis used an initial ‘test’ cohort of patients with NSCLC (n = 89) that represented an equal mix of squamous cell and adenocarcinoma. Also, each histologic subset had equal number of patients who survived more than 5 years and those who died within 2.5 years of initial diagnosis. The performance of the metagene-based model generated on the training cohort was then evaluated in independent ‘validation’ sets, including two multi-center cooperative group studies (ACOSOG Z0030 and CALGB 9761). Importantly, the CALGB validation was performed in a blinded fashion. Results. Classification tree analyses that sample multiple gene expression profiles were used to develop a model of recurrence, termed the Lung Metagene Model, that accurately assesses prognosis (risk of recurrence and survival), performing significantly (p<0.001, odds ratio: 16.1, multivariate analysis) better than pathologic stage, T-size, nodal status, age, gender, histologic subtype and smoking history. The accuracy of prognosis using the Lung Metagene Model exceeded 90% (leave-one-out cross validation) in the initial training set (n = 89), 72% in the ACOSOG (n = 25), and 81% in the CALGB (n = 84) datasets. The prognostic accuracy was consistent across histologic subtypes and stages of NSCLC. Importantly, this provides an opportunity to re-classify stage IA patients to identify a subset of ‘high risk’ patients that may benefit from adjuvant chemotherapy. Further, stage IB and II patients identified as ‘low risk’ for recurrence, and who present co-morbidities, could potentially be candidates for observation, and those patients predicted to be at ‘high risk’ may benefit from novel therapeutic trials. Conclusions. The Lung Metagene Model provides a mechanism to refine the estimation of an individual patient’s risk for disease recurrence and thus guide the use of adjuvant chemotherapy in NSCLC. No significant financial relationships to disclose.

Validating Molecular Targets of Thalidomide in CLL: Net Effect of Increased Apoptosis through the Intrinsic Pathway and down Regulation of NF-kB Signaling- Validation Using Gene Expression Profile from the Phase I/II Clinical Trial of Thalidomide and Fludarabine.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 5043-5043
Author(s):  
Asher Alban Chanan-Khan ◽  
Swaminathan Padmanabhan ◽  
Leighton Stein ◽  
Jennifer Panzarella ◽  
Kena C. Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Expression Analysis ◽  
Clinical Success ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Post Treatment ◽  
Clinical Activity ◽  
Base Line

Abstract Thalidomide is novel agent with demonstrated antitumor activity in various tumor types. The exact mechanism of the antineoplastic effects of thalidomide remains unknown despite its clinical success. We recently reported on the clinical activity of T in combination with F in patients with treatment naive CLL. In this clinical study pts were treated with T alone for 7 days prior to initiating F. Anti-leukemic effects of T were noted as early as day 7. To investigate the molecular targets of T in malignant CLL cells we analyzed changes in gene expression profiles at base line and at day 7-post treatment with T. Here we report on oligonucleotide microarray expression analysis of these patients, the clinical data is presented separately. Materials and methods: Peripheral blood samples from 5 patients for gene expression profiling were collected on day 0(prior to thalidomide) and on day 7 after completing thalidomide but prior to fludarabine infusion. DNA was extracted from apoptotic cells. Purified B cells extracted by Ficoll-hypaque were homogenized in Trizol and total RNA was extracted from samples prepared for GeneChip analysis as described in the Affymetrix GeneChip Expression Analysis Manual and biotinylated using Bioarray. After QC the samples were hybridized to U95A chips, representing over 12,000 annotated genes from the Unigene database. QPCR analysis was performed using ABI7900 sequence detector system. The data obtained were analyzed using Rapid Multi-Array Analysis (RMA) with all gene ontology (GO) functional annotations and chromosomal locations determined using NetAffx and Locus Link. Pathway Analysis: Two approaches were used-one using Pathway Assist Software, genes altered by Thalidomide were compared to those implicated in established pathways and second by constructing Biological Association Networks (BANS) which identifies associations to over 140,000 biological facts extracted from PubMed. Results: In each of the 5 paired samples 51 genes consistently displayed increased expression- mainly genes whose primary function were related to the immune response and apoptosis while 53 genes- mainly the signal transduction were lower. In particular the apoptotic response include mainly the intrinsic pathways and the activation of the granule mediated pathways. In the surviving B cells many of the genes were directly or indirectly related to the upregulation of nuclear factor kappa B (NF-kB) suggesting possible mechanism of resistance to thalidomide. QPCR validation using five different genes CFLAR, HBD, ILIB, TNF-a and PTPNS1 were done in additional 12 paired CLL samples. The PTPNS1 gene involved in the NF-kB signaling pathway was elevated in 8 of the post treatment samples. Conclusions: Any treatment of CLL, a malignancy with failure of apoptotic cellular machinery must be able to overcome pro-survival mechanisms and induce apoptosis. Our results show that Thalidomide induces a net effect of increasing apoptosis-related responses in malignant B cells through several distinct mechanisms and decreasing those involving the NF-kB pathway. We will present the complete gene expression data at the Annual meeting.

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

2010 ◽  
Vol 49 (03) ◽  
pp. 254-268 ◽  
Author(s):  
C.-S. Yang ◽  
K.-C. Wu ◽  
C.-H. Yang ◽  
L.-Y. Chuang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Microarray Data ◽  
Error Rate ◽  
Gene Expression Analysis ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Error ◽  
Classification Error Rate

Summary Background: Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. Objective: The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Method: In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Results: Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. Conclusion: The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

scholarly journals Biomarkers in Parkinson Disease: global gene expression analysis in peripheral blood from patients with and without mutations in PARK2 and PARK8

2010 ◽  
Vol 8 (3) ◽  
pp. 291-297 ◽  
Author(s):  
Patricia Maria de Carvalho Aguiar ◽  
Patricia Severino
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Expression Analysis ◽  
Peripheral Blood ◽  
Gene Expression Analysis ◽  
Small Sample Size ◽  
Expression Profiles ◽  
Global Gene Expression ◽  
Small Sample ◽  
Global Gene Expression Analysis

ABSTRACT Objective: To evaluate the performance of gene expression analysis in the peripheral blood of Parkinson disease patients with different genetic profiles using microarray as a tool to identify possible diseases related biomarkers which could contribute to the elucidation of the pathological process, as well as be useful in diagnosis. Methods: Global gene expression analysis by means of DNA microarrays was performed in peripheral blood of Parkinson disease patients with previously identified mutations in PARK2 or PARK8 genes, Parkinson disease patients without known mutations in these genes and normal controls. Each group consisted of five individuals. Results: Global gene expression profiles were heterogeneous among patients and controls, and it was not possible to detect a consistent pattern between groups. However, analyzing genes with differential expression of p < 0.005 and fold change ≥ 1.2, we were able to identify a small group of well-annotated genes. Conclusions: Despite the small sample size, the identification of differentially expressed genes suggests that the microarray technique may be useful in identifying potential biomarkers in the peripheral blood of Parkinson disease patients or in people at risk of developing the disease. This will be important once neuroprotective therapies become available, and may contribute to the identification of new pathways involved in the disease physiopathology. Results presented here should be further validated in larger groups of patients.

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