Strategies to identify novel therapeutic targets for oesophageal adenocarcinoma

Abstract

Oesophageal adenocarcinoma (OAC) is a leading cause of cancer death in the UK and current systemic therapies are ineffective for the majority of patients. The central aim of this work was to explore strategies to identify novel therapeutic targets. Research has failed, thus far, to identify a dominant oncogene in OAC, although the tumour suppressor p53 is frequently mutated. Inhibiting the mitotic kinase, polo-like kinase 1 (PLK-1), was proposed as a synthetic lethal strategy. PLK-1 was demonstrated to be over-expressed in both verified OAC cell lines and human OAC tissue compared to non-transformed cells and epithelium. Mutation of p53 was associated with over-expression of PLK-1 in both OAC and ovarian cancer tissue. Using a carefully validated viability assay, both an established and novel PLK-1 inhibitor were demonstrated to induce a G2/M arrest and reduce OAC cell proliferation. Relative selectivity was demonstrated for OAC compared to non-transformed cells. This therapeutic window could be enhanced with the induction of cancer cell cytotoxicity by pulsed administration of a short half-life inhibitor. Immunotherapeutics offer potential tumour-selectivity but no OAC-specific proteins have been defined. A comparative proteomic approach was employed to identify OAC-specific proteins as potential therapeutic targets. A tissue resource was established and methods to lyse fresh frozen biopsies optimised. An isobaric quantitative proteomic workflow was applied to OAC and matched normal biopsies and quantitative accuracy confirmed for 6 candidate proteins by immunohistochemistry. Proteome coverage and quantitative dynamic range were compared between isobaric and label-free systematic sequencing proteomic strategies applied to further patients’ tissues. The challenges of combining incomplete datasets were approached with a Bayesian framework to estimate the probability that a protein was missed during an experiment compared to not being present in the sample. This method was applied to generate a complete set of protein identifications and relative tissue expression. To gain insight into the dysregulated cellular processes in human OAC tissue, a network analysis was applied to the quantitative proteomic data. Enriched functional clusters were identified suggesting deranged glucose metabolism, potentially due to the Warburg effect. These findings were duplicated and candidate tumour-specific proteins identified in a further set of biopsies using the optimised quantitative proteomic method. The combined quantitative oesophageal proteomic dataset represents the largest in OAC to date. This thesis demonstrates a hypothesis-driven, synthetic lethal approach can yield cancer-selective therapeutic effects. Novel candidate therapeutic targets are also revealed through the development of quantitative proteomic methods and the application of network analysis.