Optimizing drug combinations for cancer treatment through integrative network modelling
The advent of targeted drugs, e.g. kinase inhibitors, has revolutionized cancer treatment; but resistance to these drugs as monotherapies remains a major problem preventing their full clinical impact. Resistance is caused in part by the ability of cancer signalling networks to dynamically adapt and rewire in response to single-drug treatment, ultimately evading the drug effect. Consequently, combination therapies are being actively investigated to combat single-agent resistance. However, given the vast number of possible target (drug) combinations but clinical trials are slow and expensive, how can we rationally predict and prioritize optimal drug combinations? Further, due to the plasticity of cancer signalling networks increasing evidence suggests that sequential drug combinations, where the first drug ‘primes’ the network for effective inhibition by the second drug, may be superior than concurrent combinations where both are administered simultaneously. Yet, under which contexts such sequential combination therapies may be favoured is poorly understood. I will describe how we try to integrate computational network modelling with experimental analysis to address these important questions. This integrative approach has enabled us to predict and prioritise synergistic drug combinations targeting a multi-pathway RTK signalling network in triple-negative breast cancer (TNBC), an aggressive form of breast cancer with no current targeted treatment. Analysis of clinical data and patient-specific model simulations further allowed us to stratify the patients for optimal benefit from the combinatorial treatment. I will also present new results where we have developed a computational framework to identify, among thousands of possible network topologies, those conferring better sensitivity to sequential over concurrent drug combinations. This analysis yields a design table highlighting a finite set of circuits susceptible to sequential treatments that are enriched with feedback loops, which provide a useful framework to guide future application of sequential targeted therapies.
Dr Lan Nguyen, Monash University
Dr Lan Nguyen
Dr Lan Nguyen currently heads the Integrative Network Modelling Laboratory at the Biomedicine Discovery Institute (BDI), Monash University and currently holds a Victorian Cancer Agency midcareer fellowship. Prior to establishing his independent research lab at the BDI, he undertook postdoctoral training with systems biology pioneers Boris Kholodenko and Walter Kolch at Systems Biology Ireland, a Europeanleading institute dedicated to systems biology and systems medicine research. He became a junior group leader at Systems Biology Ireland in 2014 before moving across continents to Monash University late 2015. His research interest revolves around network biology, specifically the exploitation of computational in close integration with experimental methods to obtain systemslevel understanding of complex biological networks, and to design networkbased strategies for the treatment of cancer. He also has a strong passion in training the next generation of interdisciplinary biomedical scientists; as such, his lab comprises of both dry and wet resources and expertise.