Prostate cancer evolution employing artificial intelligence to enhance biomarker discovery
type: CreativeWork
description: Prostate cancer is highly heterogeneous, with the potential for multifocality with phenotypically and genetically distinct tumour foci. Additionally, cellular heterogeneity has the ability to harbour phenotypically diverse cancer cell types. Prostate cancer needs to be explored across different levels to enhance our understanding of disease progression. This can be achieved through investigating progression at a cellular level, before exploring whole patient genomics, and then across larger patient cohorts. Technological advancements within genomics are changing how we understand prostate tumours and their heterogenous nature, which in turn have the potential to better deliver personalized medicine. This thesis questions whether bioinformatics tools can assess intra-tumour heterogeneity via alignment-free methods and identify novel prostate cancer biomarkers associated with advanced disease across these different levels. Exploring isolated prostate cellular sub-populations allows for novel biomarkers and targetable pathways to be identified towards cancer stem-cell (CSC) biology. Towards the patient level, intra-patient heterogeneity can be assessed through next generation sequencing by adopting multi-regional sampling via whole exome sequencing. Expanding to the cohort level, AI learning algorithms in particle swarm optimization (PSO) can explore and visualize, in real time, the most informative and succinct gene signatures from transcriptomic datasets. Differentially expressed genes (DEGs) from the isolated prostate side-populations (SPs) have shown these prostate SPs to be genetically distinct from terminally differentiated cells. DEGs were also used as input gene signatures for identifying FDA-approved drug targets for potential repurposing towards hormone manipulated distal SP (DSP) samples when compared against non- androgen deprivation therapy DSP samples. Using novel alignment-free phylogenetic analysis, we assessed intra-patient heterogeneity across differe…
abstract: Prostate cancer is highly heterogeneous, with the potential for multifocality with phenotypically and genetically distinct tumour foci. Additionally, cellular heterogeneity has the ability to harbour phenotypically diverse cancer cell types. Prostate cancer needs to be explored across different levels to enhance our understanding of disease progression. This can be achieved through investigating progression at a cellular level, before exploring whole patient genomics, and then across larger patient cohorts. Technological advancements within genomics are changing how we understand prostate tumours and their heterogenous nature, which in turn have the potential to better deliver personalized medicine. This thesis questions whether bioinformatics tools can assess intra-tumour heterogeneity via alignment-free methods and identify novel prostate cancer biomarkers associated with advanced disease across these different levels. Exploring isolated prostate cellular sub-populations allows for novel biomarkers and targetable pathways to be identified towards cancer stem-cell (CSC) biology. Towards the patient level, intra-patient heterogeneity can be assessed through next generation sequencing by adopting multi-regional sampling via whole exome sequencing. Expanding to the cohort level, AI learning algorithms in particle swarm optimization (PSO) can explore and visualize, in real time, the most informative and succinct gene signatures from transcriptomic datasets. Differentially expressed genes (DEGs) from the isolated prostate side-populations (SPs) have shown these prostate SPs to be genetically distinct from terminally differentiated cells. DEGs were also used as input gene signatures for identifying FDA-approved drug targets for potential repurposing towards hormone manipulated distal SP (DSP) samples when compared against non- androgen deprivation therapy DSP samples. Using novel alignment-free phylogenetic analysis, we assessed intra-patient heterogeneity across differe…
author: Murphy, Ross
language: eng
keywords: Bioinformatics, prostate cancer, gene expression signature, big data, multi-omics, prognostic factors, prognostic signature, machine learning, particle swarm optimization, alignment-free, genomics, tumour hetereogeneity, biomarker
date published: 2022-7
date modified: 2026-07-24T03:56:04Z
provider: Queen's University Belfast
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Queen's University Belfast