Molecular approaches in pre-clinical models to identify and validate biomarker signatures that predict treatment response in prostate cancer patients

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Background: Alternative splicing (AS) of precursor-mRNAs is an important gene regulatory mechanism that massively expands proteome complexity. Prostate cancer (PC) accounts for >11,000 UK male deaths annually. Dysregulated AS is critically important in PC progression, notably evident by the generation of constitutively-active androgen receptor (AR) variants in response to hormone ablation therapy. Large-scale RNA-sequencing projects (including TCGA) provide an ever-expanding wealth of data linking gene expression to clinical information. AS events are poorly investigated in these databases. Determining aberrant AS events in 100s-1000s PC patients will reveal critical AS gene alterations that predict treatment response and development of therapy-resistance. These novel biomarker signatures will facilitate optimal personalized treatment and disease monitoring.Objectives: Initially we will apply molecular approaches using pre-clinical models to identify candidate AS events associated with PC progression to therapy-resistance. In the second Objective, machine learning tools will interrogate publically-available RNA-sequenced PC patient cohorts to identify bio-signatures of AS events that predict response to therapy and treatment-resistance. These bio-signatures will be validated using several highly relevant pre-clinical PC models. Experimental approach: Incorporating established molecular biology techniques, the student will apply siRNA, CRISPR technology and lentiviral infections to modify gene expression in PC cell lines/organoids. Cells will be exposed to AR antagonists and subjected to RNA-sequencing. The student will be supported in bioinformatics analysis to identify multiple AS events associated with cellular sensitivity/resistance to drug treatments and trained in the application of machine learning tools on large RNA-sequenced PC patient cohorts to identify potential signatures of AS events linked to patient treatment response (biochemical recurrence, survival). Validation of AS event signatures will be examined using multiplex, PCR-based assays in our ex vivo tissue slice model incorporating patient biopsies from treatment-naïve and therapy-resistant tissue exposed to AR antagonists. This interdisciplinary translational research project will provide training in cancer cell biology, molecular biology techniques, advanced imaging approaches, bioinformatics and machine learning.Novelty: provided by accessing a unique suite of pre-clinical tools developed by Supervisor-1 and collaborators (tissue slice, organoid, iPSCs, CRISPR-modified cells) enabling a thorough interrogation of AS events under cellular conditions that closely mimic treatment and emergence of therapy-resistance for PC patients.Timeliness: derived from access to publically accessible and in-house (through European collaboratives), well-annotated, RNA sequenced clinical cohorts of PC patients, including pre- and post-treatment matched biopsies and recently available bioinformatics packages that enable robust evaluation of AS events, alongside the machine learning tools and AI expertise of Supervisor-2.