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Fighting cancer is a lot like playing a high-stakes game of chess.  Every time doctors and researchers make a clever move with a new treatment, cancer often responds with a move of its own—sometimes one that no one saw coming.  This back-and-forth is one of the biggest challenges in cancer treatment, especially with targeted therapies, which are designed to attack the disease’s specific weak points.

One such weak point is a protein called FGFR4, which plays an important role in certain breast and liver cancers.  Promising new drugs have been developed to block FGFR4, and early lab results have been encouraging.  But as so often happens, cancer eventually adapts, finding ways to bypass the drug and continue growing.

SAiGENCI’s Computational Systems Oncology team, led by Professor Lan Nguyen, aims to stay ahead of the disease.  Together with their collaborators from Monash University, they turned to an approach that combines the power of computer modelling with traditional laboratory experiments.  They built a detailed “virtual version” of how FGFR4 works inside a cancer cell and how the cell might respond if FGFR4 was blocked.

This computer model became a testing ground where they could run hundreds of “what if” scenarios—quickly, safely, and far more cheaply than in the lab.  It allowed them to spot cancer’s likely escape routes and figure out which drug combinations could block them.

“Cancer can be unpredictable, but it’s not random. By modelling its behaviour, we can see patterns and anticipate how it might resist treatment. This gives us a chance to plan several moves ahead—just like in chess.” Professor Lan Nguyen

One key finding came from triple-negative breast cancer, an aggressive form of the disease.  When FGFR4 was blocked, cancer cells switched on another survival pathway.  The model predicted that blocking both at the same time could work far better than current strategies—and lab experiments confirmed it.

The team also adapted the model to reflect hundreds of different cancer types, showing that not all cancers behave the same way.  This means the best drug combinations can be tailored to each cancer’s unique “personality,” paving the way for more personalised treatment.

The impact of this work is clear:  by predicting how cancer might fight back before it happens, doctors can plan smarter treatments from the start.  This approach doesn’t just apply to FGFR4—it could be used for many targeted therapies, helping to out-think cancer and stay one step ahead in the most important game we’ll ever play.