Predicting when a cancer will be resistant to drugs

Dr Lan Nguyen (left) and his PhD student, Anthony Hart (right) who has recently submitted his PhD
Dr Lan Nguyen (left) and his PhD student, Anthony Hart (right) who has recently submitted his PhD

The emergence of drug resistance is an ever present, and often almost inevitable, aspect of cancer therapy, yet it remains difficult to predict and harder to overcome. Monash researchers have developed a framework to predict first, how a cancer will become resistant to a drug, and second - given that everyone is different - what the significant differences are that allow some cancers to resist the drug.

Dr Lan Nguyen, a lab head in the Monash Biomedicine Discovery Institute (BDI), is senior author on a paper published in eLife which details a new computational model for comprehensive and predictive characterisation of a cancer cell’s adaptive response to anti-cancer drugs.

Dr Nguyen said that cancer often develops resistance to the drugs used to fight it.

“One of the ways it does this is by exploiting the flexibility of protein signalling systems inherent to healthy tissue, allowing cancer cells to continue to function even when exposed to the drug,” Dr Nguyen said.

“Another reason that drugs don’t always work is that every person’s cancer is at least a little bit different from every other person’s cancer, a feature often referred to as ‘heterogeneity’. So even though they might have, for example, breast cancer, because of these differences some breast cancer patients will be sensitive to a given drug while others will be resistant.

“In this research we demonstrate a link between heterogeneity and the ability of cancer to exploit protein signalling flexibility to protect itself from drug treatment. We show that small differences in protein interactions can enable some cancer cells to resist drug treatment while others cannot.

"We also show that despite there being an unfathomably large number of possible small differences that can potentially exist between different patients’ cancers, there is a much smaller number of ways that cancer can exploit flexible signalling to resist drug treatment.”

First author Mr Anthony Hart has recently submitted his PhD under the supervision of Dr Nguyen, investigating how drug resistance arises during treatment using a combination of mathematics, programming and biological experimentation.

Mr Hart explained that treating a cancer with drugs is like trying to stop traffic in a city using roadblocks. Cancer frequently adapts by changing its internal protein signalling to find ways to get around the roadblocks i.e. becomes drug resistant.

“In this paper we explored all the different ways that traffic (cancer cells) might get around a roadblock (drug) in a particular city (patient). There are lots of different kinds of cities (different patients) with their own road networks and traffic organisation. When putting up the same roadblock in a different city, there is likely to be different ways that the traffic can get around the roadblock,” Mr Hart said.

“We looked at all the different possible types of cities and how traffic might get around the roadblocks and we figured out that there were actually only a limited number of different ways that traffic can get around roadblocks. Despite the large number of different cancer contexts across patients, there are far fewer ways for cancers to develop resistance.”

Dr Nguyen said, “In addition, our new method allows us to explore deeper into the mechanistic causes of drug resistance. This is critical, because with this knowledge we can predict potential therapeutic options, such as combination therapies, that can be deployed to overcome resistance.”

In the future, Dr Nguyen said, rather than waiting for drug resistance to emerge in patients and then attempting to respond after the fact, their framework would allow oncologists to be one step ahead of cancer, allowing medical practitioners to monitor resistance more pro-actively and respond more quickly.

“We envision that in the long term, patients may have their cancers profiled using our framework, providing their oncologists with a ranked list of possible resistance mechanisms and effective therapeutic strategies that can overcome each resistance mechanism. It may also be used by pharmaceutical companies to better stratify patients based on predicted resistance mechanisms, thereby increasing the likelihood of clinical trial success.”

Read the eLife paper, Systematic analysis of network-driven adaptive resistance to CDK4/6 and estrogen receptor inhibition using meta-dynamic network modelling

DOI: 10.7554/eLife.87710.1

This research was supported by a Victorian Cancer Agency Mid-Career Research Fellowship and an Australian Government Research Training Program (RTP) Scholarship.


About the Monash Biomedicine Discovery Institute

Committed to making the discoveries that will relieve the future burden of disease, the Monash Biomedicine Discovery Institute (BDI) at Monash University brings together more than 120 internationally-renowned research teams. Spanning seven discovery programs across Cancer, Cardiovascular Disease, Development and Stem Cells, Infection, Immunity, Metabolism, Diabetes and Obesity, and Neuroscience, Monash BDI is one of the largest biomedical research institutes in Australia. Our researchers are supported by world-class technology and infrastructure, and partner with industry, clinicians and researchers internationally to enhance lives through discovery.