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

• Main Authors: Diaz-Colunga, J. (Author), Diaz-Uriarte, R. (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2021
• Subjects: article; biological model; biology; cancer growth; clinical article; Computational Biology; disease exacerbation; Disease Progression; Evolution, Molecular; genetics; genotype; Genotype; human; Humans; Models, Genetic; molecular evolution; mutation; Mutation; neoplasm; Neoplasms; pathology; prediction; probability; procedures; simulation; tumor growth
• Online Access: View Fulltext in Publisher

 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. © 2021 Diaz-Colunga, Diaz-Uriarte. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.