Abstract

In this talk I will discuss recent developments in scaled single cell whole genome sequencing approaches, enabling granular timeseries observations of cell population dynamics in cancer. Moving through in vitro, ex vivo and patient regimes, I will outline how large scale whole genome sequencing of thousands of cancer cells observed over time coupled with phylogenetic and fitness modeling leads to statistical frameworks to predict the fate of clonal populations. The data reveal how segmental aneuploidies and in particular accumulation of high level amplifications associate with increased fitness. Coupled with single cell transcriptome sequencing I will also show how phenotypes of clones with higher fitness can be inferred using joint computational inference techniques to integrate single cell genome and single cell transcriptome sequencing. Finally, I will outline progress in deploying single cell techniques to study patient samples in the context of prospective collections and longitudinal monitoring of disease progression.