Understanding the clonality of drug resistance in cancer

We are interested in understanding the clonality of drug resistance in cancer (primarily in a haematological cancer called Multiple Myeloma (MM)). To achieve our goals, we have developed state of the art long-read single-cell sequencing approaches (termed scCOLOR-seq) that allow us to measure single clones within patient samples. This method allows for the simultaneous measurement of gene expression, exon mutations, exon SNPs and translocations. We apply this technology and develop cutting edge computational analysis solutions to understand the relationship between clonality and drug resistance in oncology. Furthermore, our lab is also working as part of the Human Cell Atlas (HCA) project and we have several international collaborations with immunologists, cancer biologists to support our work.

The aim of this project is to develop computational analysis strategies aimed at better defining specific MM clones within patients that are resistant to first line therapeutics. The work will involve combining long-read (Oxford Nanopore Technology) multi-modal datasets and performing machine learning approaches to better define high risk patients. This work is important to better understand drug resistance mechanisms in MM and identify patients that may respond less well to therapy.