Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer
1 Division of Oncology, Center for Applied Medical Research, Pamplona, Spain
2 Department of Biochemistry, School of Medicine, University of Navarra, Pamplona, Spain
3 Department of Histology and Pathology, School of Medicine, University of Navarra, Pamplona, Spain
4 Oryzon Genomics, Scientific Parc University of Barcelona, Barcelona, Spain
5 Department of Pathology, Marques de Valdecilla University Hospital, School of Medicine, University of Cantabria, Santander, Spain
6 CEIT and TECNUN, University of Navarra, San Sebastian, Spain
7 Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
8 Department of Oncology, Clínica Universidad de Navarra, Pamplona, Spain
BMC Genomics 2010, 11:352 doi:10.1186/1471-2164-11-352Published: 3 June 2010
Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events.
The array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer.
This methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.