Speaker
Description
Oncogene amplification on circular extrachromosomal DNA (ecDNA) has been linked to poor prognosis and higher treatment resistance in multiple types of human cancer. ecDNA are mobile genetic elements lacking centromeres that are partitioned unevenly into daughter cells at mitosis. While random segregation of ecDNA contributes to gene-copy-number heterogeneity among tumour cells, how ecDNA contribute to phenotypic heterogeneity, via mRNA and proteins, and how this affects targeted therapy outcomes is still not understood. In fact, cancer cell populations with ecDNA show remarkable heterogeneity in mRNA and protein concentrations, which is not explained by copy-number heterogeneity alone. In this talk, I will present recent work on modelling gene expression in cancer cells with ecDNA and demonstrate how a simple mathematical model that takes into account stochasticity in gene expression is able to fit single-cell measurements of mRNA counts from cell-line data. Further, I will discuss how our model predicts different outcomes for different kinds of targeted therapy and how this might be used to better understand the sources of treatment resistance in ecDNA containing cancers
