<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-24T13:30:24Z</responseDate><request verb="GetRecord" identifier="oai:www.repository.cam.ac.uk:1810/297686" metadataPrefix="uketd_dc">https://api.repository.cam.ac.uk/server/oai/request</request><GetRecord><record><header><identifier>oai:www.repository.cam.ac.uk:1810/297686</identifier><datestamp>2021-04-21T20:15:53Z</datestamp><setSpec>com_1810_221783</setSpec><setSpec>com_1810_256067</setSpec><setSpec>col_1810_221784</setSpec></header><metadata><uketd_dc:uketddc xmlns:uketd_dc="http://naca.central.cranfield.ac.uk/ethos-oai/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:uketdterms="http://naca.central.cranfield.ac.uk/ethos-oai/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://naca.central.cranfield.ac.uk/ethos-oai/2.0/ http://naca.central.cranfield.ac.uk/ethos-oai/2.0/uketd_dc.xsd">
   <dc:title>Leveraging genomic and molecular variations to understand the regulatory landscape in human cancers and differentiating stem cells</dc:title>
   <dc:identifier xsi:type="dcterms:DOI">10.17863/CAM.44740</dc:identifier>
   <dc:creator>Urban, Lara Hanne</dc:creator>
   <uketdterms:authoridentifier xsi:type="uketdterms:ORCID">0000000254459314</uketdterms:authoridentifier>
   <uketdterms:advisor>Stegle, Oliver</uketdterms:advisor>
   <uketdterms:authoridentifier xsi:type="uketdterms:ORCID">0000000288187193</uketdterms:authoridentifier>
   <dcterms:abstract>Genetic and molecular variations are closely intertwined; while genetic factors drive phenotypic
differences ranging from gene expression to organismal traits, phenotypic variations are the
target of evolutionary selection, what eventually results in genetic changes. As technological
advances have resulted in high-throughput assays for different molecular dimensions, it has
become challenging to turn these large-scale data into meaningful insights and to delineate
biological cause and consequence. In this thesis, I use computational modelling to detect and
understand biologically meaningful associations between genetic variation and gene expression
alterations.
First, we use data across 27 human cancer types to probe associations between different
genetic factors and gene expression levels. We describe the tumours' regulatory landscape that
is highly heterogeneous across cancer types, and quantify the relationship between gene
expression and various genetic features that characterise local and global mutational burden as
well as distinct mutational processes.
Next, we study the relationship between genetic and epigenetic variation and alternative
splicing. This analysis extends studies of splicing events in bulk data to variability in splicing
between single cells from the same tissue: We analyse DNA methylation and alternative splicing
across single cells derived from one human donor to characterise splicing variation and its
determinants across genes. Thus, we identify relevant genetic determinants of splicing in
induced pluripotent stem cells as well as during their differentiation, and a significant
contribution of DNA methylation to splicing variation across cells.
Finally, we show how gene expression-mutagenesis screens can be applied to understand
complex mutational signatures, using the cancer hallmark of DNA repair deficiency as an
example. The molecular cause and consequence of homologous recombination repair
deficiency are not yet fully understood. We explore genome-wide molecular aberrations caused
by this repair deficiency beyond the few previously known genes. Our preliminary results point
towards a genetically dominant effect of BRCA1 mutagenesis.
Taken together, this thesis highlights novel dimensions of genotype-phenotype associations in
highly heterogeneous molecular datasets. We describe the complex regulatory landscape
across human cancer types, as well as molecular alterations and relevant epigenetic effects in
differentiating pluripotent stem cells.</dcterms:abstract>
   <uketdterms:institution>University of Cambridge</uketdterms:institution>
   <dcterms:issued>2019-10-26</dcterms:issued>
   <dc:type>Thesis</dc:type>
   <uketdterms:qualificationlevel>Doctoral</uketdterms:qualificationlevel>
   <uketdterms:qualificationname>Doctor of Philosophy (PhD)</uketdterms:qualificationname>
   <dc:language>en</dc:language>
   <uketdterms:sponsor>This PhD was funded by an EMBL PhD Fellowship, financed by the European Union’s Horizon2020 research and innovation programme (grant agreement number N635290).</uketdterms:sponsor>
   <dcterms:isReferencedBy xsi:type="dcterms:URI">https://www.repository.cam.ac.uk/handle/1810/297686</dcterms:isReferencedBy>
   <dc:identifier xsi:type="dcterms:URI">https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/e513ce81-5bd1-4b0b-941c-5ed08a2dc254/download</dc:identifier>
   <uketdterms:checksum xsi:type="uketdterms:MD5">b435f1bd2025266fecdb93e9ced2f4c1</uketdterms:checksum>
   <dcterms:license>https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/7df6effe-316e-46b3-b298-0a45f8bb3769/download</dcterms:license>
   <uketdterms:checksum xsi:type="uketdterms:MD5">87eda9de84448d1f82354d60eee3eb5f</uketdterms:checksum>
   <dc:rights>https://www.rioxx.net/licenses/all-rights-reserved/</dc:rights>
   <dc:subject>Statistical Genomics</dc:subject>
   <dc:subject>Computational Genomics</dc:subject>
   <dc:subject>Human Cancer Genomics</dc:subject>
   <dc:subject>Transcriptomics</dc:subject>
   <dc:subject>Mutational Signatures</dc:subject>
   <dc:subject>Single-cell Genomics</dc:subject>
   <dc:subject>DNA methylation</dc:subject>
   <dc:subject>Alternative Splicing</dc:subject>
</uketd_dc:uketddc>
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