Olga Modlich

Identifying superficial, muscle-invasive, and metastasizing transitional cell carcinoma of the bladder: Use of cDNA array analysis of gene expression profiles

Purpose: Expression profiling by DNA microarray technology permits the identification of genes underlying clinical heterogeneity of bladder cancer and which might contribute to disease progression, thereby improving assessment of treatment and prediction of patient outcome. Experimental Design: Invasive (20) and superficial (22) human bladder tumors from 34 patients with known outcome regarding disease recurrence and progression were analyzed by filter-based cDNA arrays (Atlas Human Cancer 1.2; BD Biosciences Clontech) containing 1185 genes. For 9 genes, array data were confirmed using real-time reverse transcription-PCR. Additionally, Atlas array data were validated using Affymetrix GeneChip oligonucleotide arrays with 22,283 human gene fragments and expressed sequence tags sequences in a subset of three superficial and six invasive bladder tumors. Results: A two-way clustering algorithm using different subsets of gene expression data, including a subset of 41 genes validated by the oligonucleotide array (Affymetrix), classified tumor samples according to clinical outcome as superficial, invasive, or metastasizing. Furthermore, (a) a clonal origin of superficial tumors, (b) highly similar gene expression patterns in different areas of invasive tumors, and (c) an invasive-like pattern was observed in bladder mucosas derived from patients with locally advanced disease. Several gene clusters that characterized invasive or superficial tumors were identified. In superficial bladder tumors, increased mRNA levels of genes encoding transcription factors, molecules involved in protein synthesis and metabolism, and some proteins involved into cell cycle progression and differentiation were observed, whereas transcripts for immune, extracellular matrix, adhesion, peritumoral stroma and muscle tissue components, proliferation, and cell cycle controllers were up-regulated in invasive tumors. Conclusions: Gene expression profiling of human bladder cancers provides insight into the biology of bladder cancer progression and identifies patients with distinct clinical phenotypes.

Immediate Gene Expression Changes After the First Course of Neoadjuvant Chemotherapy in Patients with Primary Breast Cancer Disease

Purpose: Our goal was to identify genes undergoing expressional changes shortly after the beginning of neoadjuvant chemotherapy for primary breast cancer. Experimental Design: The biopsies were taken from patients with primary breast cancer prior to any treatment and 24 hours after the beginning of the neoadjuvant chemotherapy. Expression analyses from matched pair samples representing 25 patients were carried out with Clontech filter arrays. A subcohort of those 25 paired samples were additionally analyzed with the Affymetrix GeneChip platform. All of the transcripts from both platforms were queried for expressional changes. Results: Performing hierarchical cluster analysis, we clustered pre- and posttreatment samples from individual patients more closely to each other than the samples taken from different patients. This reflects the rather low number of transcripts responding directly to the drugs used. Although transcriptional drug response occurring during therapy differed between individual patients, two genes (p21WAF1/CIP1 and MIC-1) were up-regulated in posttreatment samples. This could be validated by semiquantitative and real-time reverse transcription-PCR. Partial least- discriminant analysis based on approximately 25 genes independently identified by either Clontech or Affymetrix platforms could clearly discriminate pre- and posttreatment samples. However, correlation of certain gene expression levels as well as of differential patterns and clusters as determined by a different platform was not always satisfying. Conclusions: This study has demonstrated the potential of monitoring posttreatment changes in gene expression as a measure of the pharmacodynamics of drugs. As a clinical laboratory model, it can be useful to identify patients with sensitive and reactive tumors and to help for optimized choice for sequential therapy and obviously improve relapse- free and overall survival.

Gene expression in acute Stanford type A dissection: a comparative microarray study

BackgroundWe compared gene expression profiles in acutely dissected aorta with those in normal control aorta.Materials and methodsAscending aorta specimen from patients with an acute Stanford A-dissection were taken during surgery and compared with those from normal ascending aorta from multiorgan donors using the BD Atlas™ Human1.2 Array I, BD Atlas™ Human Cardiovascular Array and the Affymetrix HG-U133A GeneChip®. For analysis only genes with strong signals of more than 70 percent of the mean signal of all spots on the array were accepted as being expressed. Quantitative real-time polymerase chain reaction (RT-PCR) was used to confirm regulation of expression of a subset of 24 genes known to be involved in aortic structure and function.ResultsAccording to our definition expression profiling of aorta tissue specimens revealed an expression of 19.1% to 23.5% of the genes listed on the arrays. Of those 15.7% to 28.9% were differently expressed in dissected and control aorta specimens. Several genes that encode for extracellular matrix components such as collagen IV α2 and -α5, collagen VI α3, collagen XIV α1, collagen XVIII α1 and elastin were down-regulated in aortic dissection, whereas levels of matrix metalloproteinases-11, -14 and -19 were increased. Some genes coding for cell to cell adhesion, cell to matrix signaling (e.g., polycystin1 and -2), cytoskeleton, as well as several myofibrillar genes (e.g., α-actinin, tropomyosin, gelsolin) were found to be down-regulated. Not surprisingly, some genes associated with chronic inflammation such as interleukin -2, -6 and -8, were up-regulated in dissection.ConclusionOur results demonstrate the complexity of the dissecting process on a molecular level. Genes coding for the integrity and strength of the aortic wall were down-regulated whereas components of inflammatory response were up-regulated. Altered patterns of gene expression indicate a pre-existing structural failure, which is probably a consequence of insufficient remodeling of the aortic wall resulting in further aortic dissection.

Breast cancer expression profiling: the impact of microarray testing on clinical decision making

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

Gene expression profiling of advanced head and neck squamous cell carcinomas and two squamous cell carcinoma cell lines under radio/chemotherapy using cDNA arrays

BACKGROUND AND PURPOSE The response of squamous cell carcinomas of the head and neck (HNSCC) to radio/chemotherapy is accompanied by complex changes in patterns of gene expression. It is highly probable that a better understanding of molecular and genetic changes can help to optimize the treatment of HNSCC. cDNA arrays provide a powerful tool for high-throughput monitoring of gene expression in small clinical specimens. MATERIALS AND METHODS We used tumour biopsies from four patients with HNSCC which have been taken prior to and during radio/chemotherapy. The patterns of gene expression obtained from clinical samples were compared with gene expression profiles of two squamous cell carcinoma cell lines (FaDU and UD-7A). RESULTS The experimental data analysis revealed changes in expression levels of several genes during radio/chemotherapy. Despite treatment, independent samples taken from the same cell line or tumour in situ were more similar to each other than either was to other specimens. The data indicate a high gene heterogeneity of HNSCC that is preserved during treatment. CONCLUSIONS From our preliminary results we conclude that the cDNA array experimental approach can detect differences in gene expression between treated and untreated small tumour biopsies, as well as inter-individual differences in expression profiles between HNSCC tumours. The examination of a greater sample size will be needed to make this preliminary evaluation useful to elucidate the functional significance of individual genes which exhibit altered levels of expression under radiation therapy.

Can a 70-gene signature provide useful prognostic information in patients with node-negative breast cancer?

Can a 70-gene signature provide useful prognostic information in patients with node-negative breast cancer?

Statistical Framework for Gene Expression Data Analysis

DNA (mRNA) microarray, a highly promising technique with a variety of applications, can yield a wealth of data about each sample, well beyond the reach of every individuals comprehension. A need exists for statistical approaches that reliably eliminate insufficient and uninformative genes (probe sets) from further analysis while keeping all essentially important genes. This procedure does call for in-depth knowledge of the biological system to analyze. We conduct a comparative study of several statistical approaches on our own breast cancer Affymetrix microarray datasets. The strategy is designed primarily as a filter to select subsets of genes relevant for classification. We outline a general framework based on different statistical algorithms for determining a high-performing multigene predictor of response to the preoperative treatment of patients. We hope that our approach will provide straightforward and useful practical guidance for identification of genes, which can discriminate between biologically relevant classes in microarray datasets.

In situ monitoring of early effects of epirubicin-based neoadjuvant chemotherapy in breast cancer by cDNA array technology

In situ monitoring of early effects of epirubicin-based neoadjuvant chemotherapy in breast cancer by cDNA array technology