Context-dependent gene essentiality in glioblastoma
dc.contributor.advisor
Carragher, Neil
dc.contributor.advisor
Brennan, Paul
dc.contributor.advisor
Semple, Colin
dc.contributor.advisor
Frame, Margaret
dc.contributor.author
Foster, Mitchell Thomas
dc.contributor.sponsor
Cancer Research UK
en
dc.date.accessioned
2024-07-29T13:15:45Z
dc.date.available
2024-07-29T13:15:45Z
dc.date.issued
2024-07-29
dc.description.abstract
Glioblastoma (GBM) is a heterogeneous and aggressive brain tumour that is invariably fatal
despite maximal treatment. Genetic or transcriptomic ‘biomarkers’ could be used to stratify
patients for treatments, however, pairing biomarkers with appropriate therapeutic ‘targets’ is
challenging. Consequently, therapeutics have not yet been optimised for specific GBM
patient subsets. Here, I integrate genome-wide CRISPR/Cas9 knockout screening and
genetic-annotation data for 60 distinct patient-derived, IDH-wildtype, adult GBM cell lines,
quantifying the essentiality of 15,145 genes. I describe a novel method that uses Targeted
Learning to estimate the effect size of GBM-relevant biomarkers on context-dependent gene
essentiality (GBM-CoDE) and use it to derive multiple target-biomarker pair hypotheses.
Two of the target-biomarker pairs that I have identified (EGFR mutation/amplification as a
biomarker of WWTR1 essentiality, and low VRK2 expression as a biomarker of VRK1
essentiality) have been validated in GBM, implying that my additional novel findings may be
valid. I have identified several other novel target-biomarker pairs that require further
experimental validation. I have developed a web application that I will release in an open,
accessible format for other researchers to explore the results from GBM-CoDE. I hope
dissemination of these results will accelerate translation to biomarker-stratified clinical trials.
To my knowledge, framing the pursuit of CoDE as an estimation problem solved with
targeted machine learning has not been described before in GBM or any other type of cancer.
My method is readily translatable to other cancers of unmet need.
en
dc.identifier.uri
https://hdl.handle.net/1842/42035
dc.identifier.uri
http://dx.doi.org/10.7488/era/4757
dc.language.iso
en
en
dc.publisher
The University of Edinburgh
en
dc.subject
Glioblastoma
en
dc.subject
GBM
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dc.subject
CRISPR
en
dc.subject
heterogeneity in GBM
en
dc.subject
biomarkers
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dc.subject
Targeted Machine Learning
en
dc.subject
target-biomarker pairs
en
dc.title
Context-dependent gene essentiality in glioblastoma
en
dc.type
Thesis or Dissertation
en
dc.type.qualificationlevel
Doctoral
en
dc.type.qualificationname
PhD Doctor of Philosophy
en
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