Abstract
BackgroundTreatment of non-small cell lung cancer with novel targeted therapies is a major unmet clinical need. Alternative splicing is a mechanism which generates diverse protein products and is of functional relevance in cancer.ResultsIn this study, a genome-wide analysis of the alteration of splicing patterns between lung cancer and normal lung tissue was performed. We generated an exon array data set derived from matched pairs of lung cancer and normal lung tissue including both the adenocarcinoma and the squamous cell carcinoma subtypes. An enhanced workflow was developed to reliably detect differential splicing in an exon array data set. In total, 330 genes were found to be differentially spliced in non-small cell lung cancer compared to normal lung tissue. Microarray findings were validated with independent laboratory methods for CLSTN1, FN1, KIAA1217, MYO18A, NCOR2, NUMB, SLK, SYNE2, TPM1, (in total, 10 events) and ADD3, which was analysed in depth. We achieved a high validation rate of 69%. Evidence was found that the activity of FOX2, the splicing factor shown to cause cancer-specific splicing patterns in breast and ovarian cancer, is not altered at the transcript level in several cancer types including lung cancer.ConclusionsThis study demonstrates how alternatively spliced genes can reliably be identified in a cancer data set. Our findings underline that key processes of cancer progression in NSCLC are affected by alternative splicing, which can be exploited in the search for novel targeted therapies.
Highlights
Treatment of non-small cell lung cancer with novel targeted therapies is a major unmet clinical need
In order to identify events of differential splicing we developed a workflow that essentially consists of three components (Figure 1): (1) filtering of probe sets whose signals are not significantly above background signal, (2) re-definition of probe sets according to most up-to-date transcript annotations from public databases, and (3) statistical evaluation using a mixed linear model (MLM) analysis of variance (ANOVA) and splicing index (SI)
Background filtering reduces the number of false positive results We utilised the generally accepted analysis of variance (ANOVA) method in order to identify gene loci affected by differential splicing
Summary
Treatment of non-small cell lung cancer with novel targeted therapies is a major unmet clinical need. Non-small-cell lung cancer (NSCLC) is a histologically defined sub-group that represents 75 to 80% of all lung cancer cases. Probe sets with a signal near the background noise are termed absent. The software was modified in such a way that detection above background p values are generated per probe set instead of per gene as in the original implementation. A probe set is treated as present if p ≤ 0.01 in at least 75% of the samples of the respective pathology group (tumour or NAT). A probe set is treated as absent in the respective sample group. Probe sets that are absent in both sample groups, i.e. absent both in tumour as well as in NAT, are filtered out. Genes containing fewer than five present probe sets are removed
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