Open Access Methodology article

Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer

Ruben Pio12*, David Blanco13, Maria Jose Pajares13, Elena Aibar4, Olga Durany4, Teresa Ezponda1, Jackeline Agorreta13, Javier Gomez-Roman5, Miguel Angel Anton6, Angel Rubio6, Maria D Lozano7, Jose M López-Picazo8, Francesc Subirada4, Tamara Maes4 and Luis M Montuenga13

Author Affiliations

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

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BMC Genomics 2010, 11:352  doi:10.1186/1471-2164-11-352

Published: 3 June 2010

Abstract

Background

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.

Results

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.

Conclusions

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.