Jens Ledet Jensen

Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets

Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.

Identifying distinct classes of bladder carcinoma using microarrays

Bladder cancer is a common malignant disease characterized by frequent recurrences. The stage of disease at diagnosis and the presence of surrounding carcinoma in situ are important in determining the disease course of an affected individual. Despite considerable effort, no accepted immunohistological or molecular markers have been identified to define clinically relevant subsets of bladder cancer. Here we report the identification of clinically relevant subclasses of bladder carcinoma using expression microarray analysis of 40 well characterized bladder tumors. Hierarchical cluster analysis identified three major stages, Ta, T1 and T2-4, with the Ta tumors further classified into subgroups. We built a 32-gene molecular classifier using a cross-validation approach that was able to classify benign and muscle-invasive tumors with close correlation to pathological staging in an independent test set of 68 tumors. The classifier provided new predictive information on disease progression in Ta tumors compared with conventional staging (P < 0.005). To delineate non-recurring Ta tumors from frequently recurring Ta tumors, we analyzed expression patterns in 31 tumors by applying a supervised learning classification methodology, which classified 75% of the samples correctly (P < 0.006). Furthermore, gene expression profiles characterizing each stage and subtype identified their biological properties, producing new potential targets for therapy.

Gene Expression in the Urinary Bladder: A Common Carcinoma in Situ Gene Expression Signature Exists Disregarding Histopathological Classification

The presence of carcinoma in situ (CIS) lesions in the urinary bladder is associated with a high risk of disease progression to a muscle invasive stage. In this study, we used microarray expression profiling to examine the gene expression patterns in superficial transitional cell carcinoma (sTCC) with surrounding CIS (13 patients), without surrounding CIS lesions (15 patients), and in muscle invasive carcinomas (mTCC; 13 patients). Hierarchical cluster analysis separated the sTCC samples according to the presence or absence of CIS in the surrounding urothelium. We identified a few gene clusters that contained genes with similar expression levels in transitional cell carcinoma (TCC) with surrounding CIS and invasive TCC. However, no close relationship between TCC with adjacent CIS and invasive TCC was observed using hierarchical cluster analysis. Expression profiling of a series of biopsies from normal urothelium and urothelium with CIS lesions from the same urinary bladder revealed that the gene expression found in sTCC with surrounding CIS is found also in CIS biopsies as well as in histologically normal samples adjacent to the CIS lesions. Furthermore, we also identified similar gene expression changes in mTCC samples. We used a supervised learning approach to build a 16-gene molecular CIS classifier. The classifier was able to classify sTCC samples according to the presence or absence of surrounding CIS with a high accuracy. This study demonstrates that a CIS gene expression signature is present not only in CIS biopsies but also in sTCC, mTCC, and, remarkably, in histologically normal urothelium from bladders with CIS. Identification of this expression signature could provide guidance for the selection of therapy and follow-up regimen in patients with early stage bladder cancer.

Genomic profiling of microRNAs in bladder cancer: miR-129 is associated with poor outcome and promotes cell death in vitro.

microRNAs (miRNA) are involved in cancer development and progression, acting as tumor suppressors or oncogenes. Here, we profiled the expression of 290 unique human miRNAs in 11 normal and 106 bladder tumor samples using spotted locked nucleic acid-based oligonucleotide microarrays. We identified several differentially expressed miRNAs between normal urothelium and cancer and between the different disease stages. miR-145 was found to be the most down-regulated in cancer compared with normal, and miR-21 was the most up-regulated in cancer. Furthermore, we identified miRNAs that significantly correlated to the presence of concomitant carcinoma in situ. We identified several miRNAs with prognostic potential for predicting disease progression (e.g., miR-129, miR-133b, and miR-518c*). We localized the expression of miR-145, miR-21, and miR-129 to urothelium by in situ hybridization. We then focused on miR-129 that exerted significant growth inhibition and induced cell death upon transfection with a miR-129 precursor in bladder carcinoma cell lines T24 and SW780 cells. Microarray analysis of T24 cells after transfection showed significant miR-129 target down-regulation (P = 0.0002) and pathway analysis indicated that targets were involved in cell death processes. By analyzing gene expression data from clinical tumor samples, we identified significant expression changes of target mRNA molecules related to the miRNA expression. Using luciferase assays, we documented a direct link between miR-129 and the two putative targets GALNT1 and SOX4. The findings reported here indicate that several miRNAs are differentially regulated in bladder cancer and may form a basis for clinical development of new biomarkers for bladder cancer.

Emmprin and Survivin Predict Response and Survival following Cisplatin-Containing Chemotherapy in Patients with Advanced Bladder Cancer

Purpose: Cisplatin-containing chemotherapy is the standard of care for patients with locally advanced and metastatic transitional cell carcinoma of the urothelium. The response rate is ∼50% and tumor-derived molecular prognostic markers are desirable for improved estimation of response and survival. Experimental Design: Affymetrix GeneChip expression profiling was carried out using tumor material from 30 patients. A set of genes with an expression highly correlated to survival time after chemotherapy was identified. Two genes were selected for validation by immunohistochemistry in an independent material of 124 patients receiving cisplatin-containing therapy. Results: Fifty-five differentially expressed genes correlated significantly to survival time. Two of the protein products (emmprin and survivin) were validated using immunohistochemistry. Multivariate analysis identified emmprin expression (hazard ratio, 2.23; P < 0.0001) and survivin expression (hazard ratio, 2.46; P < 0.0001) as independent prognostic markers for poor outcome, together with the presence of visceral metastases (hazard ratio, 2.62; P < 0.0001). In the clinical good prognostic group of patients without visceral metastases, both markers showed significant discriminating power as supplemental risk factors (P < 0.0001). Within this group of patients, the subgroups of patients with no positive, one positive, or two positive immunohistochemistry scores (emmprin and survivin) had estimated 5-year survival rates of 44.0%, 21.1%, and 0%, respectively. Response to chemotherapy could also be predicted with an odds ratio of 4.41 (95% confidence interval, 1.91-10.1) and 2.48 (95% confidence interval, 1.1-5.5) for emmprin and survivin, respectively. Conclusions: Emmprin and survivin proteins were identified as strong independent prognostic factors for response and survival after cisplatin-containing chemotherapy in patients with advanced bladder cancer.

Gene Expression Signatures Predict Outcome in Non–Muscle-Invasive Bladder Carcinoma: A Multicenter Validation Study

Purpose: Clinically useful molecular markers predicting the clinical course of patients diagnosed with non–muscle-invasive bladder cancer are needed to improve treatment outcome. Here, we validated four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ (CIS) and for predicting disease recurrence and progression. Experimental Design: We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain, and France using custom microarrays. Molecular classifications were compared with pathologic diagnosis and clinical outcome. Results: Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathologic stage (P < 0.001). Furthermore, the classifier added information regarding disease progression of Ta or T1 tumors (P < 0.001). The molecular 88-gene progression classifier was highly significantly correlated with progression-free survival (P < 0.001) and cancer-specific survival (P = 0.001). Multivariate Cox regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade, and treatment (hazard ratio, 2.3; P = 0.007). The diagnosis of CIS using a 68-gene classifier showed a highly significant correlation with histopathologic CIS diagnosis (odds ratio, 5.8; P < 0.001) in multivariate logistic regression analysis. Conclusion: This multicenter validation study confirms in an independent series the clinical utility of molecular classifiers to predict the outcome of patients initially diagnosed with non–muscle-invasive bladder cancer. This information may be useful to better guide patient treatment.

Networks and Chaos - Statistical and Probabilistic Aspects

Mathematical methods of neurocomputing.- Statistical aspects of neural networks.- Statistical aspects of chaos: a review.- Chaotic dynamical systems with a View towards statistics: a review.- A tutorial on queuing networks.- River networks: a brief guide to the literature for statisticians and probabilists.- Random graphical networks.

A molecular signature in superficial bladder carcinoma predicts clinical outcome.

Purpose: Cancer of the urinary bladder is a common malignant disease in the western countries. The majority of patients presents with superficial tumors with a high recurrence frequency, a minor fraction of these patients experience disease progression to a muscle invasive stage. No clinical useful molecular markers exist to identify patients showing later disease progression. The purpose of this study was to identify markers of disease progression using full-genome expression analysis. Experimental Design: We did a full-genome expression analysis (59,619 genes and expressed sequence tags) of superficial bladder tumors from 29 bladder cancer patients (13 without later disease progression and 16 with later disease progression) using high-density oligonucleotide microarrays. We used supervised learning for identification of the optimal genes for predicting disease progression. The identified genes were validated on an independent test set (74 superficial tumor samples) using in house-fabricated 60-mer oligonucleotide microarrays. Results: We identified a 45-gene signature of disease progression. By monitoring this progression signature in an independent test set, we found a significant correlation between our classifications and the clinical outcome (P < 0.03). The genes identified as differentially expressed were involved in regulating apoptosis, cell differentiation, and cell cycle and hence may represent potential therapeutic targets. Conclusions: Our results indicate that it may be possible to identify patients with a high risk of disease progression at an early stage using a molecular signature present already in the superficial tumors. In this way, better treatment and follow-up regimens could be assigned to patients suffering from superficial bladder cancer.

Spatial mixture modeling of fMRI data.

Recently, Everitt and Bullmore [ 1999 ] proposed a mixture model for a test statistic for activation in fMRI data. The distribution of the statistic was divided into two components; one for nonactivated voxels and one for activated voxels. In this framework one can calculate a posterior probability for a voxel being activated, which provides a more natural basis for thresholding the statistic image, than that based on P‐values. In this article, we extend the method of Everitt and Bullmore to account for spatial coherency of activated regions. We achieve this by formulating a model for the activation in a small region of voxels and using this spatial structure when calculating the posterior probability of a voxel being activated. We have investigated several choices of spatial models but find that they all work equally well for brain imaging data. We applied the model to synthetic data from statistical image analysis, a synthetic fMRI data set and to visual stimulation data. Our conclusion is that the method improves the estimation of the activation pattern significantly, compared to the nonspatial model and to smoothing the data with a kernel of FWHM 3 voxels. The difference between FWHM 2 smoothing and our method were more modest. Hum. Brain Mapping 11:233–248, 2000.

Role of activating Fibroblast growth factor receptor 3 mutations in the development of bladder tumors

Purpose: Bladder tumors develop through different molecular pathways. Recent reports suggest activating mutations of the fibroblast growth factor receptor 3 (FGFR3) gene as marker for the “papillary” pathway with good prognosis, in contrast to the more malignant “carcinoma in situ” (CIS) pathway. The aim of this clinical follow-up study was to investigate the role of FGFR3 mutations in bladder cancer development in a longitudinal study. Experimental Design: We selected 85 patients with superficial bladder tumors, stratified into early (stage Ta/grade 1-2, n = 35) and more advanced (either stage T1 or grade 3, n = 50) developmental stages. The patients were followed prospectively, and metachronous tumors were included. We did screening for FGFR3 and TP53 mutations by direct bidirectional sequencing and for genome-wide molecular changes with microarray technology. Results: A total of 43 of 85 cases (51%) showed activating mutations of FGFR3. The mutations were associated with papillary tumors of early developmental stage. However, after stratifying for developmental stage, FGFR3-mutated tumors showed the same malignant potential as wild-type tumors. Tumors with concomitant CIS were generally FGFR3 wild type. They were characterized by different patterns of chromosomal changes and gene expression signatures compared with FGFR3-mutated tumors, indicating different molecular pathways. Conclusions:FGFR3 mutations seem to have a central role in the early development of papillary bladder tumors. These tumors follow a common molecular pathway, which is different from tumors with concomitant CIS. FGFR3 mutations do not seem to play a role in bladder cancer progression.