Inferring time-aware models of cancer progression using Timed Hazard Networks

Analysis of the sequential accumulation of genetic events, such as mutations and copy number alterations, is key to understanding disease dynamics and may provide insights into the design of targeted therapies. Oncogenetic graphical models are computational methods that use genetic event profiles from cross-sectional genomic data to infer the statistical dependencies between events and thereby deduce their temporal order of occurrence. Existing research focuses mainly on the development of graph structure learning algorithms. However, no algorithm explicitly links the oncogenetic graph with the temporal differences of samples in an analytic way. In this paper, we propose a novel statistical framework Timed Hazard Networks (TimedHN), that treat progression times as hidden variables and jointly infers oncogenetic graph and pseudo-time order of samples. We modeled the accumulation process as a continuous-time Markov chain and developed an efficient gradient computation algorithm for the optimization. Experiment results using synthetic data showed that our method outperforms the state-of-the-art in graph reconstruction. We highlighted the differences between TimedHN and competing methods on a luminal breast cancer dataset and illustrated the potential utility of the proposed method. Implementation and data are available at https://github.com/puar-playground/TimedHN