Jens Ledet Jensen

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.

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.

Abstract Although introduced more than 60 years ago it is only during the last 15 years that there has been a systematic development of saddlepoint approximations. These approximations give a highly accurate expression for the tail of a distribution, not only in the centre of the distribution but also for very small tail probabilities. The price for this is a more cumbersome formula, the evaluation of which sometimes requires the use of a small personal computer. This book explains the ideas behind the saddlepoint approximations as well as giving a detailed mathematical description of the subject. The emphasis is two- fold. One is on popularizing the formulae through many worked out and ready to use examples. The second is on giving a comprehensive mathematical background for further research in the field. Some of the major subjects treated are uniformity of the approximations, tests in exponential families, and compound sums with applications in insurance mathematics.

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.