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PubMed2005

Gene expression profiling in breast cancer: towards individualising patient management.

Breast cancer is a complex and clinically heterogeneous disease. The increase in knowledge of breast cancer biology has led to a number of clinical advances in the treatment of breast cancer, most notably the implementation of widespread mammography screening and advances in adjuvant treatment of early-stage disease. In the last 20 years, arrays of potential prognostic and/or predictive markers of breast cancer have been analysed. However, relatively few have proven to be clinically useful. To date, the only widely accepted markers for routine use in breast cancer are the oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor, HER-2 (c-erbB2/neu). Expression microarray technology and laser capture microdissection have now been employed to further our understanding of the molecular pathogenesis of breast cancer. Recently reported advances in array technology and RNA amplification methods are having a considerable impact in this field, allowing the analysis of pre-malignant and pre-invasive lesions. A number of studies have identified prognostic and predictive gene 'signatures', whose prediction of disease outcome and response to treatment is superior to conventional prognostic indicators. Despite major technological advances, a number of confounding issues remain concerning the potential clinical utility of gene expression profiling, including differences in study design, patient selection, array technology, chemistry, and methods of analysis. It seems likely, however, that following careful 'hypothesis driven' validation studies and clinical trials, expression profiling will be applied in the future to identify patient-specific disease profiles and provide rationale for individualised treatment. This review focuses on the current use and future potential of microarray profiling in breast cancer.

Breast Neoplasms↗
PubMed2005

Expression profiling suggests a regulatory role of gallbladder in lipid homeostasis.

AIM: To examine expression profile of gallbladder using microarray and to investigate the role of gallbladder in lipid homeostasis. METHODS: 33P-labelled cDNA derived from total RNA of gallbladder tissue was hybridized to a cDNA array representing 17,000 cDNA clusters. Genes with intensities > or =2 and variation <0.33 between two samples were considered as positive signals with subtraction of background chosen from an area where no cDNA was spotted. The average gray level of two gallbladders was adopted to analyze its bioinformatics. Identified target genes were confirmed by touch-down polymerase chain reaction and sequencing. RESULTS: A total of 11 047 genes expressed in normal gallbladder, which was more than that predicted by another author, and the first 10 genes highly expressed (high gray level in hybridization image), e.g. ARPC5 (2 225.88+/-90.46), LOC55972 (2 220.32+/-446.51) and SLC20A2 (1 865.21+/-98.02), were related to the function of smooth muscle contraction and material transport. Meanwhile, 149 lipid-related genes were expressed in the gallbladder, 89 of which were first identified (with gray level in hybridization image), e.g. FASN (11.42+/-2.62), APOD (92.61+/-8.90) and CYP21A2 (246.11+/-42.36), and they were involved in each step of lipid metabolism pathway. In addition, 19 of those 149 genes were gallstone candidate susceptibility genes (with gray level in hybridization image), e.g. HMGCR (10.98+/-0.31), NPC1 (34.88+/-12.12) and NR1H4 (16.8+/-0.65), which were previously thought to be expressed in the liver and/or intestine tissue only. CONCLUSION: Gallbladder expresses 11 047 genes and takes part in lipid homeostasis.

Adult↗
PubMed2006

Exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data.

BACKGROUND: The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other. RESULTS: We validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections. CONCLUSION: The temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.

Animals↗
PubMed2002

Core biopsies can be used to distinguish differences in expression profiling by cDNA microarrays.

The primary focus of this work was to determine the feasibility of obtaining representative expression array profiles from clinical core biopsies. For this purpose we performed six 16-gauge needle core biopsies and an excision biopsy on each of two different human xenografts, one from an Ewing's sarcoma cell line and the second from neuroblastoma cell line grown in Beige-Scid mice. Three of the six core biopsies were processed separately and the remaining three were pooled and processed together. As the initial RNA material isolated from the core biopsies was not sufficient for microarray analysis, an amplification procedure using a modified Eberwine protocol was performed, and the amplified products applied onto a 6000-feature human cDNA microarray. Comparisons of the array results from core biopsies (amplified RNA) and surgical specimens (non-amplified RNA) showed maintenance of the expression profile as assessed by hierarchical clustering. Gene expression profiles obtained from microarray analysis clearly differentiated the Ewing's sarcoma from the neuroblastoma with both core and excisional biopsies as starting material. Pooling the core biopsies did not improve the concordance with excisional biopsies. In conclusion, our results suggest that core biopsies can be used as a suitable and reliable material for the determination of tumor genetic profiles.

Animals↗
PubMed2004

Gene expression profiling in mouse lung following polymeric hexamethylene diisocyanate exposure.

Isocyanates are a common cause of occupational lung disease. Hexamethylene diisocyanate (HDI), a component of polyurethane spray paints, can induce respiratory symptoms, inflammation, lung function impairment, and isocyanate asthma. The predominant form of HDI in polyurethane paints is a nonvolatile polyisocyanate known as HDI biuret trimer (HDI-BT). Exposure of mice to aerosolized HDI-BT results in pathological effects, including pulmonary edema, lung inflammation, cellular proliferation, and fibrotic lesions, which occur with distinct time courses following exposure. To identify genes that mediate lung pathology in the distinct temporal phases after exposure, gene expression profiles in HDI-BT-exposed C57BL/6J mouse lungs were analyzed. RNase protection assay (RPA) of genes involved in apoptosis, cell survival, and inflammation revealed increased expression of IkappaBalpha, Fas, Bcl-X(L), TNFalpha, KC, MIP-2, IL-6, and GM-CSF following HDI-BT exposure. Microarray analysis of approximately 10000 genes was performed on lung RNA collected from mice 6, 18, and 90 h after HDI-BT exposure and from unexposed mice. Classes of genes whose expression was increased 6 h after exposure included those involved in stress responses (particularly oxidative stress and thiol redox balance), growth arrest, apoptosis, signal transduction, and inflammation. Types of genes whose expression was increased at 18 h included proteinases, anti-proteinases, cytoskeletal molecules, and inflammatory mediators. Transcripts increased at 90 h included extracellular matrix components, transcription factors, inflammatory mediators, and cell cycle regulators. This characterization of the gene expression profile in lungs exposed to HDI-BT will provide a basis for investigating injury and repair pathways that are operative during isocyanate-induced lung disease.

Administration, Inhalation↗
PubMed2001

The coming impact of gene expression profiling on the diagnosis and treatment of HCV-associated liver disease.

Gene expression profiling allows the level of activity of thousands of genes to be monitored simultaneously. Profiling is often carried out on specialized chips or slides, which have microarrays of gene targets at predetermined addresses. In the immediate future, microarrays promise to yield new insights into hepatitis C virus (HCV) pathogenesis and to produce 'signatures' that can be used in molecular diagnostics. In the longer-term, they may aid the development of serological tests by identifying genes encoding secretory proteins produced by HCV-infected livers, and they may suggest new avenues for disease intervention by detecting genes whose products are retained in the infected liver.

Gene Expression Profiling↗
PubMed2004

Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter.

Little is known about global gene expression patterns in the human neurodegenerative disease amyotrophic lateral sclerosis (ALS). To address this, we used high-density oligonucleotide microarray technology to compare expression levels of approximately 6,800 genes in postmortem spinal cord gray matter obtained from individuals with ALS as well as normal individuals. Using Fisher discriminant analysis (FDA) and leave-one-out cross-validation (LOOCV), we discerned an ALS-specific signature. Moreover, it was possible to distinguish familial ALS (FALS) from sporadic ALS (SALS) gene expression profiles. Characterization of the specific genes significantly altered in ALS uncovered a pro-inflammatory terminal state. Moreover, we found alterations in genes involved in mitochondrial function, oxidative stress, excitotoxicity, apoptosis, cytoskeletal architecture, RNA transcription and translation, proteasomal function, and growth and signaling. It is apparent from this study that DNA microarray analysis and appropriate bioinformatics can reveal distinct phenotypic changes that underlie the terminal stages of neurodegeneration in ALS.

Amyotrophic Lateral Sclerosis↗
PubMed2006

Alteration of gene expression profiles during mycoplasma-induced malignant cell transformation.

BACKGROUND: Mycoplasmas are the smallest microorganisms capable of self-replication. Our previous studies show that some mycoplasmas are able to induce malignant transformation of host mammalian cells. This malignant transformation is a multistage process with the early infection, reversible and irreversible stages, and similar to human tumor development in nature. The purpose of this study is to explore mechanisms for this malignant transformation. METHODS: To better understand mechanisms for this unique process, we examined gene expression profiles of C3H cells at different stages of the mycoplasma-induced transformation using cDNA microarray technology. A total of 1185 genes involved in oncogenesis, apoptosis, cell growth, cell-cycle regulation, DNA repair, etc. were examined. Differences in the expression of these genes were compared and analyzed using the computer software AtlasImage. RESULTS: Among 1185 genes screened, 135 had aberrant expression at the early infection stage, 252 at the reversible stage and 184 at the irreversible stage. At the early infection stage, genes with increased expression (92 genes) were twice more than those with decreased expression (42 genes). The global gene expression at the reversible stage appeared to be more volatile than that at any other stages but still resembled the profile at the early infection stage. The expression profile at the irreversible stage shows a unique pattern of a wide range of expression levels and an increased number of expressing genes, especially the cancer-related genes. Oncogenes and tumor suppressors are a group of molecules that showed significant changes in expression during the transformation. The majority of these changes occurred in the reversible and irreversible stages. A prolonged infection by mycoplasmas lead to the expression of more cancer related genes at the irreversible stage. CONCLUSION: The results indicate that the expression profiles correspond with the phenotypic features of the cells in the mycoplasma induced transformation process. The early mycoplasma infection stage shares a common phenomenon with many other acute infections, genes with increased expression significantly outnumbering those with decreased expression. The reversible stage is a transition stage between benignancy and malignancy at the molecular level. Aberrant expression of oncogenes and tumor repressors plays a key role in mycoplasma-induced malignant transformation.

Animals↗
PubMed2002

Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the largest category of aggressive lymphomas. Less than 50% of patients can be cured by combination chemotherapy. Microarray technologies have recently shown that the response to chemotherapy reflects the molecular heterogeneity in DLBCL. On the basis of published microarray data, we attempted to develop a long-overdue method for the precise and simple prediction of survival of DLBCL patients. We developed a fuzzy neural network (FNN) model to analyze gene expression profiling data for DLBCL. From data on 5857 genes, this model identified four genes (CD10, AA807551, AA805611 and IRF-4) that could be used to predict prognosis with 93% accuracy. FNNs are powerful tools for extracting significant biological markers affecting prognosis, and are applicable to various kinds of expression profiling data for any malignancy.

DNA-Binding Proteins↗
PubMed2006

Gene expression profiling changes induced by a novel Gemini Vitamin D derivative during the progression of breast cancer.

We investigated gene expression changes induced by a novel Gemini Vitamin D(3) analog, RO-438-3582 (1alpha,25-dihydroxy-20S-21(3-hydroxy-3-methyl-butyl)-23-yne-26,27-hexafluoro-cholecalciferol, Ro3582), in a unique human breast MCF10 model. We used two breast epithelial cell lines from this model, namely MCF10AT1 (Ha-ras oncogene transfected MCF10A, early premalignant) and MCF10CA1a (fully malignant and metastatic derived from the MCF10AT1 line). We analyzed gene expression changes induced by Ro3582 using GeneChip technology, quantitative RT-PCR, Western blot analysis, or a gene transcription assay. Interestingly, we found distinct gene expression profile differences between Ro3582-induced response of the early premalignant MCF10AT1 and the malignant and metastatic MCF10CA1a cell lines. Moreover, while the Gemini Vitamin D(3) analog Ro3582 modulated the expression of several Vitamin D target genes such as the 24-hydroxylase, CD14, osteocalcin, and osteopontin in both cell lines, Ro3582 regulated many genes involved in cell proliferation and apoptosis, cell adhesion, invasion, angiogenesis as well as cell signaling pathways, such as the BMP and TGF-beta systems, differently in the two cell lines. The Gemini Vitamin D(3) analog Ro3582 induced more significant gene changes in the early premalignant MCF10AT1 cells than in the malignant metastatic MCF10CA1a cells, suggesting that Gemini Vitamin D(3) analogs may be more effective in preventing the progression of an early stage of breast carcinogenesis than in treating late stage breast cancer.

Apoptosis↗
PubMed2004

Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction.

PURPOSE: Recently, several expression-profiling experiments have shown that adenocarcinoma can be classified into subgroups that also reflect patient survival. In this study, we examined the expression patterns of 44 genes selected by these studies to test whether their expression patterns were relevant to prognosis in our cohort as well, and to create a prognostic model applicable to clinical practice. PATIENTS AND METHODS: Expression levels were determined in 85 adenocarcinoma patients by quantitative reverse transcriptase polymerase chain reaction. Cluster analysis was performed, and a prognostic model was created by the proportional hazards model using a stepwise method. RESULTS: Hierarchical clustering divided the cases into three major groups, and group B, comprising 21 cases, had significantly poor survival (P =.0297). Next, we tried to identify a smaller number of genes of particular predictive value, and eight genes (PTK7, CIT, SCNN1A, PGES, ERO1L, ZWINT, and two ESTs) were selected. We then calculated a risk index that was defined as a linear combination of gene expression values weighted by their estimated regression coefficients. The risk index was a significant independent prognostic factor (P =.0021) by multivariate analysis. Furthermore, the robustness of this model was confirmed using an independent set of 21 patients (P =.0085). CONCLUSION: By analyzing a reasonably small number of genes, patients with adenocarcinoma could be stratified according to their prognosis. The prognostic model could be applicable to future decisions concerning treatment.

Adenocarcinoma↗
PubMed2001

Gene expression profiling of the nervous system in murine experimental autoimmune encephalomyelitis.

Multiple sclerosis is thought to be a polygenic disease driven by dysregulation of the immune system leading to an autoimmune response against one or several antigens of cerebral white matter tissue. Experimental autoimmune encephalomyelitis (EAE) is a mouse model that is used to study the aetiology and pathogenesis of multiple sclerosis and new therapeutic approaches. We used oligonucleotide microarrays to determine gene expression profiles of the inflamed spinal cords of EAE mice at the onset and at the peak of the disease. Of the approximately 11 000 genes studied, 213 were regulated differentially and 100 showed consistent differential regulation throughout the disease. Inflammation resulted in a profile of increased gene expression of immune-related molecules, extracellular matrix and cell adhesion molecules and molecules involved in cell division and transcription, and differential regulation of molecules involved in signal transduction, protein synthesis and metabolism. Of the 104 genes with defined chromosomal locations, 51 mapped to known EAE-linked quantitative trait loci and as such are putative candidate genes for susceptibility to EAE.

Animals↗
PubMed2001

Gene expression profiling of B cell chronic lymphocytic leukemia reveals a homogeneous phenotype related to memory B cells.

B cell-derived chronic lymphocytic leukemia (B-CLL) represents a common malignancy whose cell derivation and pathogenesis are unknown. Recent studies have shown that >50% of CLLs display hypermutated immunoglobulin variable region (IgV) sequences and a more favorable prognosis, suggesting that they may represent a distinct subset of CLLs which have transited through germinal centers (GCs), the physiologic site of IgV hypermutation. To further investigate the phenotype of CLLs, their cellular derivation and their relationship to normal B cells, we have analyzed their gene expression profiles using oligonucleotide-based DNA chip microarrays representative of approximately 12,000 genes. The results show that CLLs display a common and characteristic gene expression profile that is largely independent of their IgV genotype. Nevertheless, a restricted number of genes (<30) have been identified whose differential expression can distinguish IgV mutated versus unmutated cases and identify them in independent panels of cases. Comparison of CLL profiles with those of purified normal B cell subpopulations indicates that the common CLL profile is more related to memory B cells than to those derived from naive B cells, CD5(+) B cells, and GC centroblasts and centrocytes. Finally, this analysis has identified a subset of genes specifically expressed by CLL cells of potential pathogenetic and clinical relevance.

B-Lymphocytes↗
PubMed2005

Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy.

BACKGROUND: Gene expression profiling of prostate carcinoma offers an alternative means to distinguish aggressive tumor biology and may improve the accuracy of outcome prediction for patients with prostate carcinoma treated by radical prostatectomy. METHODS: Gene expression differences between 37 recurrent and 42 nonrecurrent primary prostate tumor specimens were analyzed by oligonucleotide microarrays. Two logistic regression modeling approaches were used to predict prostate carcinoma recurrence after radical prostatectomy. One approach was based exclusively on gene expression differences between the two classes. The second approach integrated prognostic gene variables with a validated postoperative predictive model based on standard variables (nomogram). The predictive accuracy of these modeling approaches was evaluated by leave-one-out cross-validation (LOOCV) and compared with the nomogram. RESULTS: The modeling approach using gene variables alone accurately classified 59 (75%) tissue samples in LOOCV, a classification rate substantially higher than expected by chance. However, this predictive accuracy was inferior to the nomogram (concordance index, 0.75 vs. 0.84, P = 0.01). Models combining clinical and gene variables accurately classified 70 (89%) tissue samples and the predictive accuracy using this approach (concordance index, 0.89) was superior to the nomogram (P = 0.009) and models based on gene variables alone (P < 0.001). Importantly, the combined approach provided a marked improvement for patients whose nomogram-predicted likelihood of disease recurrence was in the indeterminate range (7-year disease progression-free probability, 30-70%; concordance index, 0.83 vs. 0.59, P = 0.01). CONCLUSIONS: Integration of gene expression signatures and clinical variables produced predictive models for prostate carcinoma recurrence that perform significantly better than those based on either clinical variables or gene expression information alone.

Aged↗
PubMed2003

Beta defensin-1, parvalbumin, and vimentin: a panel of diagnostic immunohistochemical markers for renal tumors derived from gene expression profiling studies using cDNA microarrays.

The common histopathologic subtypes of renal epithelial neoplasms include conventional, or clear cell, renal cell carcinoma (RCC), papillary RCC, chromophobe RCC, and renal oncocytoma. These subtypes differ clinically and pathologically, making accurate classification important. However, this differential diagnosis can be challenging because of overlapping morphology, suggesting a potential utility for ancillary immunohistochemical markers. We used cDNA microarrays to identify candidate markers for distinguishing renal tumor subtypes. In this report we validated differential expression of three candidate markers, beta defensin-1, parvalbumin, and vimentin, and evaluated the use of this immunohistochemical panel as a potential diagnostic tool. Consistent with our cDNA microarray data, chromophobe RCCs and oncocytomas exhibited similar expression profiles: 8 of 8 examples of each subtype were immunohistochemically positive for beta defensin-1 and parvalbumin and negative for vimentin (sensitivity 100%, specificity 100%); 4 of 7 papillary RCCs were positive for beta defensin-1, parvalbumin, and vimentin (sensitivity 57%, specificity 97%); and 22 of 23 conventional RCCs were negative for beta defensin-1, parvalbumin, or both markers (sensitivity 96%, specificity 96%) as well as positive for vimentin (sensitivity 83%). The immunohistochemical panel distinguished renal tumor subtypes with greater specificity than any marker used alone. This work demonstrates that a useful panel of immunohistochemical markers can be derived from differential gene expression profiles determined using cDNA microarrays.

Adenoma, Oxyphilic↗
PubMed2005

Gene expression profiling to characterize anticancer drug sensitivity.

This chapter presents a protocol for using cDNA microarrays to acquire gene expression profiles that characterize anticancer drug sensitivity. The protocol includes steps for drug exposure, RNA isolation, preparation of fluorescently labeled samples, microarray hybridization, data processing, and data analysis. In addition to the detailed protocol, important experimental design issues are discussed, and some preliminary experiments are recommended.

Antineoplastic Agents↗
PubMed2003

Analysis of gene expression profile of pancreatic carcinoma using cDNA microarray.

AIM: To identify new diagnostic markers and drug targets, the gene expression profiles of pancreatic cancer were compared with that of adjacent normal tissues utilizing cDNA microarray analysis. METHODS: cDNA probes were prepared by labeling mRNA from samples of six pancreatic carcinoma tissues with Cy5-dUTP and mRNA from adjacent normal tissues with Cy3-dUTP respectively through reverse transcription. The mixed probes of each sample were then hybridized with 12 800 cDNA arrays (12 648 unique human cDNA sequences), and the fluorescent signals were scanned by ScanArray 3 000 scanner (General Scanning, Inc.). The values of Cy5-dUTP and Cy3-dUTP on each spot were analyzed and calculated by ImaGene 3.0 software (BioDiscovery, Inc.). Differentially expressed genes were screened according to the criterion that the absolute value of natural logarithm of the ratio of Cy5-dUTP to Cy3-dUTP was greater-than 0.69. RESULTS: Among 6 samples investigated, 301 genes, which accounted for 2.38 % of genes on the microarry slides, exhibited differentially expression at least in 5. There were 166 over-expressed genes including 136 having been registered in Genebank, and 135 under-expressed genes including 79 in Genebank in cancerous tissues. CONCLUSION: Microarray analysis may provide invaluable information on disease pathology, progression, resistance to treatment, and response to cellular microenvironments of pancreatic carcinoma and ultimately may lead to improving early diagnosis and discovering innovative therapeutic approaches for cancer.

Biomarkers, Tumor↗