Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for clinical research. However, nucleic acids extracted from FFPE tissues are fragmented and chemically modified making them challenging to use in molecular studies. We analysed 23 fresh-frozen (FF), 35 FFPE and 38 paired FF/FFPE specimens, representing six different human tissue types (bladder, prostate and colon carcinoma; liver and colon normal tissue; reactive tonsil) in order to examine the potential use of FFPE samples in next-generation sequencing (NGS) based retrospective and prospective clinical studies. Two methods for DNA and three methods for RNA extraction from FFPE tissues were compared and were found to affect nucleic acid quantity and quality. DNA and RNA from selected FFPE and paired FF/FFPE specimens were used for exome and transcriptome analysis. Preparations of DNA Exome-Seq libraries was more challenging (29.5% success) than that of RNA-Seq libraries, presumably because of modifications to FFPE tissue-derived DNA. Libraries could still be prepared from RNA isolated from two-decade old FFPE tissues. Data were analysed using the CLC Bio Genomics Workbench and revealed systematic differences between FF and FFPE tissue-derived nucleic acid libraries. In spite of this, pairwise analysis of DNA Exome-Seq data showed concordance for 70–80% of variants in FF and FFPE samples stored for fewer than three years. RNA-Seq data showed high correlation of expression profiles in FF/FFPE pairs (Pearson Correlations of 0.90 +/- 0.05), irrespective of storage time (up to 244 months) and tissue type. A common set of 1,494 genes was identified with expression profiles that were significantly different between paired FF and FFPE samples irrespective of tissue type. Our results are promising and suggest that NGS can be used to study FFPE specimens in both prospective and retrospective archive-based studies in which FF specimens are not available.

Alternative splicing enhances proteome diversity and modulates cancer-associated proteins. To identify tissueand tumor-specific alternative splicing, we used the GeneChip Human Exon 1.0 ST Array to measure wholegenome exon expression in 102 normal and cancer tissue samples of different stages from colon, urinary bladder, and prostate. We identified 2069 candidate alternative splicing events between normal tissue samples from colon, bladder, and prostate and selected 15 splicing events for RT-PCR validation, 10 of which were successfully validated by RT-PCR and sequencing. Furthermore 23, 19, and 18 candidate tumor-specific splicing alterations in colon, bladder, and prostate, respectively, were selected for RT-PCR validation on an independent set of 81 normal and tumor tissue samples. In total, seven genes with tumor-specific splice variants were identified (ACTN1, CALD1, COL6A3, LRRFIP2, PIK4CB, TPM1, and VCL). The validated tumor-specific splicing alterations were highly consistent, enabling clear separation of normal and cancer samples and in some cases even of different tumor stages. A subset of the tumor-specific splicing alterations (ACTN1, CALD1, and VCL) was found in all three organs and may represent general cancer-related splicing events. In silico protein predictions suggest that the identified cancerspecific splice variants encode proteins with potentially altered functions, indicating that they may be involved in pathogenesis and hence represent novel therapeutic targets. In conclusion, we identified and validated alternative splicing between normal tissue samples from colon, bladder, and prostate in addition to cancerspecific splicing events in colon, bladder, and prostate Alternative splicing is a key component in expanding a relatively limited number of genes into very complex proteomes. It has been estimated that about three-quarters of all human genes undergo alternative splicing (1-3), which may affect function, localization, binding properties, and stability of the encoded proteins (4). The recent results from the ENCODE (Encyclopedia of DNA Elements) consortium (5) extend and confirm the ubiquity of alternative splicing (6). Several splice variants with antagonistic functions have been described, e.g. BCL-X has an antiapoptotic long isoform and a proapoptotic short isoform (7,8). Alternative splicing can also lead to degradation of the transcript, thereby abrogating protein expression; examples include certain Serine/Arginine-rich (SR) protein splicing factors for which the inclusion of a particular exon causes mRNA degradation by nonsense-mediated decay (9, 10).Single nucleotide polymorphisms and somatic splice site mutations leading to aberrant splicing patterns have been described for a number of tumor suppressor genes, including APC, TP53, and BRCA1 (11). Deregulation of trans-acting proteins, such as splicing factors and heterogeneous nuclear ribonucleoproteins, may cause a more general change in RNA splicing in cancer cells. The SFRS1 gene, encoding the splicing factor 2/alternate spli...

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 T a or T 1 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.Bladder cancer is a common malignant disease with 357,000 new cases and 145,000 deaths worldwide annually (1). Its prevalence is 3-to 8-fold higher than its incidence, making bladder cancer one of the most prevalent neoplasms, and hence a major burden for health care systems. The overall causespecific 5-year survival rate is about 65%. The disease presents in two different forms: non -muscle-invasive tumors (stages T a and T 1 ), usually treated with a local, organ-sparing approach, and muscle-invasive cancers (stages T 2 -T 4 ), usually requiring cystectomy if cure is intended.The non -muscle-invasive tumors account for f75% of newly diagnosed cases. A low proportion of patients are cured after tumor resection, but the tumors of more than 60% of these patients recur, and the frequency of recurrences has a significant effect on the patients' quality of life. Some of these patients also develop muscle-invasive tumors over time, the proportion ranging from very low for noninvasive papillary low-grade tumors to up to 60% progression for high-grade submucosa-invasive tumors (2, 3). Clinical risk factors for progression include invasion of the lamina propria, high grade, tumor size, occurrence of carcinoma in situ (CIS), and multiplicity or recurrence of ...

We present Bayesian hierarchical models for the analysis of Affymetrix GeneChip data. The approach we take differs from other available approaches in two fundamental aspects. Firstly, we aim to integrate all processing steps of the raw data in a common statistically coherent framework, allowing all components and thus associated errors to be considered simultaneously. Secondly, inference is based on the full posterior distribution of gene expression indices and derived quantities, such as fold changes or ranks, rather than on single point estimates. Measures of uncertainty on these quantities are thus available. The models presented represent the first building block for integrated Bayesian Analysis of Affymetrix GeneChip data: the models take into account additive as well as multiplicative error, gene expression levels are estimated using perfect match and a fraction of mismatch probes and are modeled on the log scale. Background correction is incorporated by modeling true signal and cross-hybridization explicitly, and a need for further normalization is considerably reduced by allowing for array-specific distributions of nonspecific hybridization. When replicate arrays are available for a condition, posterior distributions of condition-specific gene expression indices are estimated directly, by a simultaneous consideration of replicate probe sets, avoiding averaging over estimates obtained from individual replicate arrays. The performance of the Bayesian model is compared to that of standard available point estimate methods on subsets of the well known GeneLogic and Affymetrix spike-in data. The Bayesian model is found to perform well and the integrated procedure presented appears to hold considerable promise for further development.

Background: Affymetrix 3' GeneChip microarrays are widely used to profile the expression of thousands of genes simultaneously. They differ from many other microarray types in that GeneChips are hybridised using a single labelled extract and because they contain multiple 'match' and 'mismatch' sequences for each transcript. Most algorithms extract the signal from GeneChip experiments in a sequence of separate steps, including background correction and normalisation, which inhibits the simultaneous use of all available information. They principally provide a point estimate of gene expression and, in contrast to BGX, do not fully integrate the uncertainty arising from potentially heterogeneous responses of the probes.