Mining and integration of oral cancer genomics for predictive therapeutics / Bernard Lee Kok Bang - Bernard Lee, Kok Bang (2019)
Global oral cancer incidence and mortality rates are increasing rapidly, with more than 350 000 new cases and 170 000 deaths recorded in 2018. Depressingly, standard treatments for oral cancer such as surgery, chemotherapy, and radiotherapy are associated with significant morbidity and a relatively static 5-year survival rate of around 50 – 60%. To date, three drugs - cetuximab, pembrolizumab, and nivolumab, are available for treating oral cancer. However, only a small fraction of oral cancer patients respond to these drugs. Discovery of further efficacious drugs in a cost-effective way through drug repurposing can potentially uncover the best combinatorial drug therapy against oral cancer. In this thesis, I aimed to create, using computational and statistical approaches, an integrative digital resource that can be mined to identify drug candidates that could be repurposed for oral cancer treatment. To this end, two bioinformatics tools were developed. The first tool – GENIPAC (Genomic Information Portal on Cancer Cell Lines), is a web resource for exploring, visualising, and analysing genomics information from 44 head and neck cancer cell lines. The second tool – DeSigN (Differentially Expressed Gene Signatures - Inhibitors), linksthe gene expression of oral cancer cell lines to the publicly available gene expression databases that have drug sensitivity data. To validate the efficacy of drug candidate shortlisted by DeSigN on a panel of oral cancer cell lines, several in vitro experiments were performed. Using gene expression signatures retrieved from the ORL Series in GENIPAC, DeSigN predicted bosutinib, an Src/Abl kinase inhibitor used for treating leukemia, to have inhibitory effect on oral cancer cell lines. Subsequent in vitro drug sensitivity validation showed that these oral cancer cell lines were susceptible to bosutinib treatment at IC50 of 0.8 – 1.2 µM. Later, antiproliferative experiments confirmed the efficacy of bosutinib in controlling tumour iv growth in oral cancer cell lines. Technical evaluation of performance reliability of six gene signature similarity scoring algorithms showed that the Weighted Connectivity Score or the statistically significant Connectivity Map, are prime candidates for upgrading the current core algorithm of DeSigN, which is based on the Kolmogorov-Smirnov statistic. In conclusion, the present work has demonstrated that cancer genomics data mining and integration through GENIPAC and DeSigN is a viable approach to accelerating the drug development process for oral cancer. Importantly, application of these two tools led to the discovery of bosutinib as a new, promising drug candidate to be repurposed for treating oral cancer in the future.