University of Cambridge
Identification and functional characterisation of gene fusions in human cancer cell lines
Abstract
dc:description.abstractAdvances in next-generation sequencing have accelerated the rate at which novel gene fusions are discovered. The discovery of gene fusions such as EML4-ALK in lung cancer and BCR-ABL1 in chronic myeloid leukaemia have already led to changes in clinical care. However, important questions remain about the role of gene fusions in promoting oncogenic phenotypes and their relevance in drug response. In this study, I combine RNA sequencing, CRISPR/Cas9 screens and high-throughput drug sensitivity data in a panel of 1,011 human cancer cell lines across 42 tissue types to examine the occurrence and functional relevance of gene fusions in cancer. Fusions were called using three algorithms and filtered to reveal 8,354 fusion events with a validation rate of 70%. Cell lines exhibit known fusions in their corresponding tissue types as well as a large number of putative passenger events and fusion recurrence across tissue types correlates with that found in patient samples. The panel of 1,011 cell lines has previously undergone high-throughput drug screening of 409 drug compounds. I implemented a systematic analysis to identify associations between fusion occurrence and drug response. It reliably recapitulates known associations (e.g. BCR-ABL1 and sensitivity to ABL inhibitors). However, the number of novel findings is low, likely due to the low numbers of recurrent fusions, a lack of prior knowledge of novel gene fusions as well as a narrow range of drug targets. Next, I developed a computational approach using whole-genome CRISPR/Cas9 screening data for 339 cell lines. It utilises CRISPR/Cas9 data on a guide-level to systematically evaluate essentiality of novel gene fusions. My analysis predicts essentiality of known gene fusions with high accuracy and provides evidence for the oncogenic relevance of novel gene fusions. A gene fusions in YAP1-MAML2 represents a particularly interesting finding showing functionality across multiple distinct cancer types. Altogether, in my thesis, I demonstrate that innovative computational approaches leveraging new datasets can enable us to elucidate the functionality of rare gene fusions in human cancer. These types of discoveries may aid in the development of targeted therapies and supports the use of clinical basket trials to capture cancer events across multiple tissue types.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2018
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Chen, Elisabeth Daisy
- Advisor dc:contributor.advisor
-
- Garnett, Mathew
Subjects
dc:subject × 5Rights
dc:rightsIdentifiers
dc:identifier.*- Author Identifier
- 0000-0003-2129-7985
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/293136