Comprehensive genomic and transcriptomic analysis of high grade serous ovarian cancer using single-cell RNA-sequencing.

In this project, we employ an innovative technique called single-cell RNA-sequencing (scRNA-seq) on thousands of randomly dissociated celis from consecutive tumour biopsies of high grade serous ovarian cancer (HGSOC) patients before and during first-line treatment but also at moment of relapse. ScRNA-seq enables us to detect transcriptomic heterogeneity, both in cancer cells and their surrounding stromal celis  (Le. microenvironment) down to the single-ceIl resolution, while previous research on sample-averaged measurements (Le. bulk RNA data) failed to differentiate cell types or isolate rare subpopulations of celis. This dynamic map of the tumour ecosystem is essential to find relevant subpopulations of cells predicting therapeutic response,
especially towards new targeted therapies, and to monitor genealogic phenotypes of ceils during disease progression. Consequently, the ability to map and understand single-cell functional States during the course of the disease is the key to a more personalized therapy and to the development of novel anti-cancer treatments.