Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question “Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?” or, shortly, “What genotype comes next?”. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s results when key assumptions do not hold.Author summary: Predicting cancer progression would allow for the systematic application of targeted, personalized therapies. In recent years, several methods have been developed to predict cancer evolution from cross-sectional sequencing data, which are increasingly available. However, the quality of these predictions is hampered by violations of the methods’ assumptions, often in conflict with the evolutionary dynamics of real tumors. Since predicting the short-term progression of a tumor after its detection could be more relevant from a clinical perspective, we examine the feasibility of short-term predictions, hypothesizing that these could be successful even when long-term predictions are not possible: even if the methods’ assumptions are not satisfied in general, they could still hold for specific evolutionary steps. We examined whether 13 methods could accurately predict the short-term evolution of over 25 million simulated tumors, and identified the conditions for predictions to be accurate. We analyzed 25 real cancer data sets and found indications that forecasting the evolution of a tumor could be possible when specific mutants are found in it. Our analysis highlights the importance of conditioning predictions on the detected tumor composition, and opens new avenues for developing and adapting methods aimed to predict cancer evolution.