Systems biology of deregulated splicing in cancer. A pan-cancer analysis of dysfunctional splicing machinery and alternative splicing events.

The deregulation or disruption of the splicing process has been shown to play a role in the onset, development, and even response to treatment of some malignancies. Partly due to our incomplete understanding of the mechanism and regulation behind splicing and alternative splicing, the importance of aberrant splicing in oncogenesis is not yet understood. In this project, we set out to perform a systematic analysis of aberrant splicing events in cancer cell lines from two perspectives: deregulated splicing because of dysfunctional splicing factors and the appearance of de novo splicing events because of splice-disruption mutations in the DNA sequence. All the analyses have been performed using data publicly available in the DepMap portal. For the analysis of de novo splicing events caused by mutations in the DNA sequence, we used the deep-learning tool SpliceAI. To the extent of our knowledge, SpliceAI has not been previously used to analyze RNA-sequencing from cancerous samples - cell lines nor human tumour samples. In this project, we decided to test the deep learning algorithm in data from the Cancer Cell Line Encyclopedia collected by the DepMap consortium and evaluate its performance to detect splicing alterations that may affect oncogenesis. In order to try to evaluate the clinical relevance of our findings, we used the MSK-IMPACT sequencing panel to narrow down the analysis to actionable genes - genes that can be targeted by drugs.

Department dc:contributor.department

Chalmers tekniska högskola / Institutionen för biologi och bioteknik

Year dc:date.issued

2021

Author dc:creator

• Castillon, Leticia

Advisor dc:contributor.supervisor

• Limeta, Angelo

• multi-omics
• splicing
• alternative splicing
• deep learning
• cancer
• transcriptome

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/20.500.12380/303875

OAI record oai:identifier

https://odr.chalmers.se/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aodr.chalmers.se%3A20.500.12380%2F303875