Inferring context-specific essentiality networks using large-scale CRISPR-KO screens

Large-scale genome-wide CRISPR knockout screens, such as the ones from DepMap and Project Score, revealed that a lot of genes are essential, i.e. required, in only a subset of cell lines. These context-essential genes offer insights into vulnerabilities of different cancer types and provide promising targets for personalized cancer therapies. However, the challenge is to systematically identify and define those context-essential genes and to understand how cellular phenotype and interaction networks are altered in these contexts. In this thesis, I present different approaches to understanding context-specific essentiality networks. I developed a Bayesian linear model called PLMCECS which identifies genes important in the context of cancer driver mutations and tissue of origin by modelling important properties of CRISPR knockout data. I validated the performance using simulated data and performed various sanity checks, in the absence of a gold standard benchmark. When analysing genome- wide CRISPR-Cas9 knockout data, I found that gene essentiality was highly variable across tissues in the context of the same cancer driver mutation. Genes essential in the context of the same cancer driver mutation shared similar functions and formed tightly connected functional networks with clusters representing functions that were required in particular tissues. Understanding cancer dependencies involves not only how context affects single-gene essentiality, but also how gene interactions shape cancer-specific essentiality landscapes. Together with collaborators, we performed a large-scale dual-knockout CRISPR-Cas9 screen to identify genetic interactions in *KRAS*-mutant colorectal cancer and triple-negative breast cancer with the aim to nominate combinations for more effective cancer therapies and to counteract tumour resistance. Using preliminary data, I discussed different mathematical models to score genetic interactions and highlight context-specific synthetic interactions. Lastly, I nominated promising synthetic lethal gene pairs for follow-up validation in *KRAS*-mutant colorectal cancers. Overall, this thesis contributes important knowledge on context-essential genes and genetic interactions in cancer, enhancing our understanding of cancer vulnerabilities and guiding the development of targeted therapies for improved patient outcomes.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Weidemüller, Paula Helena

Advisor dc:contributor.advisor

• Petsalaki, Evangelia

• cancer research
• CRISPR
• gene essentiality
• precision medicine

Licence

• https://www.repository.cam.ac.uk/bitstreams/f777150a-bc2c-4f81-aeb5-7c8da8566ece/download
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.106157

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/364536