Large-scale Bayesian modelling of drug resistance and evolution in human cancers at single-cell resolution

Recent advances in next generation sequencing (NGS) technologies have led to the ability to measure gene expression and DNA mutations across thousands of cells in cancer tumors at the single-cell level. This allows us to quantify the effect of chemotherapeutic drugs on the way tumors mutate and answer questions about why particular groups of cells (known as clones) evade treatment and cause relapse. However, the vast quantities of data produced by such measurements combined with the low signal-to-noise ratio makes analysis and interpretation particularly difficult. This project aims to develop a suite of state-of-the-art Bayesian methods (e.g., sequential Monte Carlo (SMC) and black-box variational inference) for learning from single-cell cancer genomics data with a focus on scalable inference to help address these challenges. Development of these tools will enable precision medicine by equipping clinicians the ability to better predict which treatment(s) will work best, and adjust appropriately, for each individual cancer patient.