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

ABSTRACT: 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 genot

SUBMITTER: Diaz-Colunga J 

PROVIDER: S-EPMC8730404 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature