Examining systematically how spatial structure influences tumour evolution

But knowing the ratio of the final scores is not enough. In high-scoring basketball, it’s unlikely that one team will score twice as many points as their opponents unless they are vastly superior. In soccer, by contrast, it’s not unusual for the better team to lose 2-1 by a stroke of bad luck. To make accurate inferences, you need to know the rules of the game.

Much as sports teams compete to score points, so groups of closely related cells – known as clones – compete within tumours for the space and resources they need to survive and multiply. Oncologists use genetic sequencing to determine the relative sizes of these clones when a patient comes to the clinic. If one clone is larger than another then it might be because its cells have so-called “driver” mutations that cause them to proliferate faster.

But the effect of mutations on tumour development depends on how cells interact with one another, which is governed by the tumour’s spatial structure. Much as coronavirus spreads more slowly when people stay home and avoid mixing, so driver mutations spread more slowly within tumours if cells are confined to small patches, with only rare movement between patches. The rules matter in this game, too.

The new study is the first to systematically examine how spatial structure influences tumour evolution. To do this, the D-BSSE researchers in Niko Beerenwinkel’s group developed a computational model with the flexibility to simulate alternative spatial structures and types of cell dispersal. They then ran thousands of simulations with different structures and parameter values and compared the results to recent, state-of-the-art DNA sequencing data from actual human tumours.

The team found that the diverse spatial structures of human tumours can cause them to evolve in vastly different ways. The computer model predictions are consistent with clinical data for cancer types with matching structures.

“Our findings have important implications for interpreting cancer genetic data,” says Robert Noble, lead author of the team’s paper published in Nature Ecology & Evolution. “A major goal of modern cancer research is to characterise the evolutionary process within tumours. We have shown that to get an accurate picture of what’s going on, you need to account for each tumour’s particular spatial structure.”

Robert, who recently left the Beerenwinkel group to start his own group at City, University of London, concludes that “By mechanistically connecting tumour architecture to the mode of tumour evolution, our work provides the blueprint for a new generation of patient-specific models for forecasting tumour progression and for optimising therapy.”