Anne Bramard

Breast cancer is the most common cause of cancer death among women under age 50 years, so it is imperative to identify molecular markers to improve diagnosis and prognosis of this disease. Here, we present a new approach for the identification of breast cancer markers that does not measure gene expression but instead uses the ratio of alternatively spliced mRNAs as its indicator. Using a high-throughput reverse transcription-PCR-based system for splicing annotation, we monitored the alternative splicing profiles of 600 cancer-associated genes in a panel of 21 normal and 26 cancerous breast tissues. We validated 41 alternative splicing events that significantly differed in breast tumors relative to normal breast tissues. Most cancer-specific changes in splicing that disrupt known protein domains support an increase in cell proliferation or survival consistent with a functional role for alternative splicing in cancer. In a blind screen, a classifier based on the 12 best cancer-associated splicing events correctly identified cancer tissues with 96% accuracy. Moreover, a subset of these alternative splicing events could order tissues according to histopathologic grade, and 5 markers were validated in a further blind set of 19 grade 1 and 19 grade 3 tumor samples. These results provide a simple alternative for the classification of normal and cancerous breast tumor tissues and underscore the putative role of alternative splicing in the biology of cancer.

Alternative splicing is a key mechanism regulating gene expression, and it is often used to produce antagonistic activities particularly in apoptotic genes. Heterogeneous nuclear ribonucleoparticle (hnRNP) proteins form a family of RNA-binding proteins that coat nascent pre-mRNAs. Many but not all major hnRNP proteins have been shown to participate in splicing control. The range and specificity of hnRNP protein action remain poorly documented, even for those affecting splice site selection. We used RNA interference and a reverse transcription-PCR screening platform to examine the implications of 14 of the major hnRNP proteins in the splicing of 56 alternative splicing events in apoptotic genes. Out of this total of 784 alternative splicing reactions tested in three human cell lines, 31 responded similarly to a knockdown in at least two different cell lines. On the other hand, the impact of other hnRNP knockdowns was cell line specific. The broadest effects were obtained with hnRNP K and C, two proteins whose role in alternative splicing had not previously been firmly established. Different hnRNP proteins affected distinct sets of targets with little overlap even between closely related hnRNP proteins. Overall, our study highlights the potential contribution of all of these major hnRNP proteins in alternative splicing control and shows that the targets for individual hnRNP proteins can vary in different cellular contexts.

Intense efforts are currently being directed toward profiling gene expression in the hope of developing better cancer markers and identifying potential drug targets. Here, we present a sensitive new approach for the identification of cancer signatures based on direct high-throughput reverse transcription-PCR validation of alternative splicing events. This layered and integrated system for splicing annotation (LISA) fills a gap between high-throughput microarray studies and high-sensitivity individual gene investigations, and was created to monitor the splicing of 600 cancer-associated genes in 25 normal and 21 serous ovarian cancer tissues. Out of >4,700 alternative splicing events screened, the LISA identified 48 events that were significantly associated with serous ovarian tumor tissues. In a further screen directed at 39 ovarian tissues containing cancer pathologies of various origins, our ovarian cancer splicing signature successfully distinguished all normal tissues from cancer. High-volume identification of cancer-associated splice forms by the LISA paves the way for the use of alternative splicing profiling to diagnose subtypes of cancer.

Supplementary Table S1 from Multiple Alternative Splicing Markers for Ovarian Cancer