Cancer is one of the leading causes of death worldwide, and various chemotherapeutic agents are in use for its treatment. However, response to these therapies is generally low and resistance to these drugs is a daunting problem for cancer intervention. Prediction of response to drug treatment and the pharmacodynamics of response following treatment are important avenues for research into the clinical efficacy of chemical interventions in cancer, and in the goal of personalisation of medicine through patient stratification.
This research project investigated potential pharmacodynamic biomarkers of response to anticancer drugs through metabonomics, assessing molecular changes in metabolic profile following specific drug treatments. Ultra-performance liquid chromatography-mass spectrometry was used in an untargeted approach on in vitro and in vivo sample sets. Multivariate modelling and statistics were employed alongside database interrogation to identify potential metabonomic biomarkers of response to treatment in three studies: in vitro treatment of breast cancer cells with histone methyltransferase inhibitors; in vitro and in vivo investigation of epidermal growth factor receptor inhibition in colorectal cancer; and in vivo assessment of platinum resensitisation using AKT inhibition in ovarian cancer.
Across the three studies compositional changes in the abundance of several lipids based on treatment was seen, with several phosphatidylcholines, sphingomyelins and ceramides consistently found to increase in concentration in cells or patients that responded to therapy. Polar metabolites that were found to change in level as a result of treatment were more specific to each study. Each tentative identification represents a potential biomarker of treatment response, and requires ID confirmation with chemical standards and fragmentation before further investigating further. The trajectory of the results points to the feasibility of metabonomic biomarkers in early clinical trials as pharmacodynamic and response biomarkers that have the potential to optimise therapy for each cancer patient.