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Single-cell multi-omic lineage tracing uncouples tumour initiation and drug tolerance in breast cancer

Friday, January 20, 2023 - 10:30
Francesca Nadalin
LCQB Kitchen, Campus Jussieu, Bâtiment C 4e étage 4 place Jussieu, 75005 PARIS
EMBL postdoctoral fellow Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milano, Italy European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Camb. UK

Cancer is a heterogeneous disease, where multiple, phenotypically distinct subpopulations co-exist. Tumour evolution is a result of a complex interplay of genetic and epigenetic factors. Predicting the different cancer phenotypes requires linking the molecular state of a clone to its fate with high precision. Single-cell lineage tracing techniques are being successfully applied to the study of cancer evolution. I will show how single-cell assays endowed with genetic barcodes can be exploited to extract transcriptional stability properties of cancer clones, link molecular states to phenotypes, and study tumour heterogeneity, using a breast cancer model as a case study. Next, I will show how a new framework, called GALILEO, capturing lineage tracing, transcriptomic, and chromatin accessibility information simultaneously and at single-cell resolution, can be used to gain deep insights on aggressive cancer phenotypes. By tracing cancer cells in mice, we observe that the highly tumorigenic niche properly contains two transcriptionally distinct clonal subpopulations. Importantly, we identify a major epigenetic module common to them, which may explain the phenotypic relationship between these tumour-initiating subpopulations. In contrast, when exposed to cytotoxic treatment, the two cancer subpopulations show different drug sensitivity, both in vitro and in vivo. Finally, by tracing clonal response to chemotherapy across time, we identify two distinct paths to drug tolerance, each showing a gene expression bias that is reminiscent of the initial, untreated state. The possibility to associate the molecular features of cancer clones to their different outcomes, especially in the presence of epigenetic drivers, makes the proposed approach extremely valuable for the study of the evolution of cancer diseases at single-cell resolution.

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