Modelling the Accumulation of Mutations During Cancer Progression without Explicit Time

Understanding the order in which mutations accumulate during cancer progression could help us identify effective therapeutic targets. Yet, our insights are limited by the fact that we can only observe a single snapshot of a specific cancer tissue and that we do not know its position on the cancer evolution time scale. In this talk, I will share with you our recent progress that makes the estimate of order as well as disease progression time more accurate by scaling up continuous-time Markov chains to hundreds of mutations.

Learning Objectives:

1. List some important limitations of the data that is available on cancer patients.

2. Discuss how even seemingly independent items (mutations) provide valuable information about disease progression time.

3. Explain the main idea how we scale up continuous time Markov chains to take this relevant information into account.