Predicting tumour evolution and drug resistance from heterogenous longitudinal cancer data

We introduce biPOD, a model-based bayesian framework that leverages longitudinal phenotypic (e.g., tumour volume, cell counts) or genotypic (e.g., mutation frequency) data to infer critical parameters of tumour progression within a single patient. Here we release the data and code to reproduce the analysis on synthetic and real datasets presented in the preprint, while the R package can be consulted at https://github.com/caravagnalab/biPOD/