A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.

BACKGROUND: Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development. RESULTS: We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events. Based on a set of scores, an exon score and an array score, a decision regarding alternative splicing can be made. The model enables the ability to distinguish a differential expressed gene from a differential spliced exon. The proposed model was applied to three case studies concerning both exon and HTA arrays. CONCLUSION: The REIDS model provides a work flow for the identification of alternative splicing events relying on the established linear mixed model. The model can be applied to different types of arrays.

24h-gene variation effect of combined bevacizumab/erlotinib in advanced non-squamous non-small cell lung cancer using exon array blood profiling.

BACKGROUND: The SAKK 19/05 trial investigated the safety and efficacy of the combined targeted therapy bevacizumab and erlotinib (BE) in unselected patients with advanced non-squamous non-small cell lung cancer (NSCLC). Although activating EGFR mutations were the strongest predictors of the response to BE, some patients not harboring driver mutations could benefit from the combined therapy. The identification of predictive biomarkers before or short after initiation of therapy is therefore paramount for proper patient selection, especially among EGFR wild-types. The first aim of this study was to investigate the early change in blood gene expression in unselected patients with advanced non-squamous NSCLC treated by BE. The second aim was to assess the predictive value of blood gene expression levels at baseline and 24h after BE therapy. METHODS: Blood samples from 43 advanced non-squamous NSCLC patients taken at baseline and 24h after initiation of therapy were profiled using Affymetrix' exon arrays. The 24h gene dysregulation was investigated in the light of gene functional annotations using gene set enrichment analysis. The predictive value of blood gene expression levels was assessed and validated using an independent dataset. RESULTS: Significant gene dysregulations associated with the 24h-effect of BE were detected from blood-based whole-genome profiling. BE had a direct effect on "Pathways in cancer", by significantly down-regulating genes involved in cytokine-cytokine receptor interaction, MAPK signaling pathway and mTOR signaling pathway. These pathways contribute to phenomena of evasion of apoptosis, proliferation and sustained angiogenesis. Other signaling pathways specifically reflecting the mechanisms of action of erlotinib and the anti-angiogenesis effect of bevacizumab were activated. The magnitude of change of the most dysregulated genes at 24h did not have a predictive value regarding the patients' response to BE. However, predictive markers were identified from the gene expression levels at 24h regarding time to progression under BE. CONCLUSIONS: The 24h-effect of the combined targeted therapy BE could be accurately monitored in advanced non-squamous NSCLC blood samples using whole-genome exon arrays. Putative predictive markers at 24h could reflect patients' response to BE after adjusting for their mutational status. Trial registration ClinicalTrials.gov: NCT00354549.

Whole Transcriptome Analysis of Pre-invasive and Invasive Early Squamous Lung Carcinoma in Archival Laser Microdissected Samples.

BACKGROUND: Preinvasive squamous cell cancer (PSCC) are local transformations of bronchial epithelia that are frequently observed in current or former smokers. Their different grades and sizes suggest a continuum of dysplastic change with increasing severity, which may culminate in invasive squamous cell carcinoma (ISCC). As a consequence of the difficulty in isolating cancerous cells from biopsies, the molecular pathology that underlies their histological variability remains largely unknown. METHOD: To address this issue, we have employed microdissection to isolate normal bronchial epithelia and cancerous cells from low- and high-grade PSCC and ISCC, from paraffin embedded (FFPE) biopsies and determined gene expression using Affymetric Human Exon 1.0 ST arrays. Tests for differential gene expression were performed using the Bioconductor package limma followed by functional analyses of differentially expressed genes in IPA. RESULTS: Examination of differential gene expression showed small differences between low- and high-grade PSCC but substantial changes between PSCC and ISCC samples (184 vs 1200 p-value <0.05, fc ±1.75). However, the majority of the differentially expressed PSCC genes (142 genes: 77%) were shared with those in ISCC samples. Pathway analysis showed that these shared genes are associated with DNA damage response, DNA/RNA metabolism and inflammation as major biological themes. Cluster analysis identified 12 distinct patterns of gene expression including progressive up or down-regulation across PSCC and ISCC. Pathway analysis of incrementally up-regulated genes revealed again significant enrichment of terms related to DNA damage response, DNA/RNA metabolism, inflammation, survival and proliferation. Altered expression of selected genes was confirmed using RT-PCR, as well as immunohistochemistry in an independent set of 45 ISCCs. CONCLUSIONS: Gene expression profiles in PSCC and ISCC differ greatly in terms of numbers of genes with altered transcriptional activity. However, altered gene expression in PSCC affects canonical pathways and cellular and biological processes, such as inflammation and DNA damage response, which are highly consistent with hallmarks of cancer.

Transcriptome-wide targets of alternative splicing by RBM4 and possible role in cancer.

This study determined transcriptome-wide targets of the splicing factor RBM4 using Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays and HeLa cells treated with RBM4-specific siRNA. This revealed 238 transcripts that were targeted for alternative splicing. Cross-linking and immunoprecipitation experiments identified 945 RBM4 targets in mouse HEK293 cells, 39% of which were ascribed to "alternative splicing" by in silico pathway analysis. Mouse embryonic stem cells transfected with Rbm4 siRNA hairpins exhibited reduced colony numbers and size consistent with involvement of RBM4 in cell proliferation. RBM4 cDNA probing of a cancer cDNA array involving 18 different tumor types from 13 different tissues and matching normal tissue found overexpression of RBM4 mRNA (p<0.01) in cervical, breast, lung, colon, ovarian and rectal cancers. Many RBM4 targets we identified have been implicated in these cancers. In conclusion, our findings reveal transcriptome-wide targets of RBM4 and point to potential cancer-related targets and mechanisms that may involve RBM4.

Algorithms for differential splicing detection using exon arrays: a comparative assessment.

BACKGROUND: The analysis of differential splicing (DS) is crucial for understanding physiological processes in cells and organs. In particular, aberrant transcripts are known to be involved in various diseases including cancer. A widely used technique for studying DS are exon arrays. Over the last decade a variety of algorithms for the detection of DS events from exon arrays has been developed. However, no comprehensive, comparative evaluation including sensitivity to the most important data features has been conducted so far. To this end, we created multiple data sets based on simulated data to assess strengths and weaknesses of seven published methods as well as a newly developed method, KLAS. Additionally, we evaluated all methods on two cancer data sets that comprised RT-PCR validated results. RESULTS: Our studies indicated ARH as the most robust methods when integrating the results over all scenarios and data sets. Nevertheless, special cases or requirements favor other methods. While FIRMA was highly sensitive according to experimental data, SplicingCompass, MIDAS and ANOSVA showed high specificity throughout the scenarios. On experimental data ARH, FIRMA, MIDAS, and KLAS performed best. CONCLUSIONS: Each method shows different characteristics regarding sensitivity, specificity, interference to certain data settings and robustness over multiple data sets. While some methods can be considered as generally good choices over all data sets and scenarios, other methods show heterogeneous prediction quality on the different data sets. The adequate method has to be chosen carefully and with a defined study aim in mind.

An integrative framework identifies alternative splicing events in colorectal cancer development.

Alternative splicing (AS) is a common mechanism which creates diverse RNA isoforms from a single gene, potentially increasing protein variety. Growing evidence suggests that this mechanism is closely related to cancer progression. In this study, whole transcriptome analysis was performed with GeneChip Human exon 1.0 ST Array from 80 samples comprising 23 normal colon mucosa, 30 primary colorectal cancer and 27 liver metastatic specimens from 46 patients, to identify AS events in colorectal cancer progression. Differentially expressed genes and exons were estimated and AS events were reconstructed by combining exon-level analyses with AltAnalyze algorithms and transcript-level estimations (MMBGX probabilistic method). The number of AS genes in the transition from normal colon mucosa to primary tumor was the most abundant, but fell considerably in the next transition to liver metastasis. 206 genes with probable AS events in colon cancer development and progression were identified, that are involved in processes and pathways relevant to tumor biology, as cell-cell and cell-matrix interactions. Several AS events in VCL, CALD1, B3GNT6 and CTHRC1 genes, differentially expressed during tumor development were validated, at RNA and at protein level. Taken together, these results demonstrate that cancer-specific AS is common in early phases of colorectal cancer natural history.