The ultimate goal in cancer treatment is to identify the therapeutic vulnerabilities of a patient’s tumour and use this to design a personalised medicine regime. The cost reduction of genomic technologies in the last few years, has allowed extensive genomic analysis of clinical samples but for most tumour types, we currently lack the ability to translate these data into a successful therapeutic strategy.
My group have developed a suite of artificial intelligence algorithms that use cancer genomic and other ‘big’ data sets to predict cancer driver genes and druggable vulnerabilities in cancer cells.
In the first part of the presentation, I will focus on methods we have developed to identify cancer driver genes and the use of mutational signatures to explore tissue specificity in driver genes.
The second part of presentation will focus on the algorithms that we have developed to identify therapeutic vulnerabilities in cancer. The SLant and MexDrugs algorithms predict tumour-specific therapeutic strategies by identifying targetable synthetically lethal gene pairs of tumour suppressors through protein-protein interaction (PPI) data (SLant) or methylation data (MexDrugs). The DependANT algorithm uses mutation and expression data from cancer cell lines to modulate PPI networks to identify the genes that the cell has become dependent on.
Finally, I will showcase some of the experimental work we have undertaken, developing an inhibitor for one of the identified targets; BLM helicase.