Research
With the aim of quantifying, predicting and controlling cancer evolution, here are the ongoing projects in the lab.
Quantifying Phenotypic Diversity using Cellular Activities
Cancer cells stray from normal hierarchies and the expression markers to identify them during tissue homeostasis. They however display strong convergence on the phenotypic traits they must acquire to survive and invade distant organs (the Cancer “Hallmarks”). We thus aim to phenotypically describe cancer cells according to what they do, rather than what they are, using single-cell technologies. We are validating specific signatures adequate to single-cell data, via exogenous inductions of selected key cell functions in vitro. This will help us quantify phenotypic diversity and follow its dynamics in different cancer settings.
Relevant Publications:
- Assessing Cell Activities rather than Identities to Interpret Intra-Tumor Phenotypic Diversity and Its Dynamics. iScience, 2020.
Dynamics of Resistance in Breast Cancer
We aim to understand how plasticity and genetic instability shape the dynamics of resistance, using in vitro models of triple negative breast cancers. This is a collaboration with the Puisieux team (CRCL / Institut Curie). We aim to understand how plasticity and genomic instability influence how resistance emerges and what the dynamics of the process are. In collaboration with experimental biology (A Vigneron, CRCL) and mathematics research groups (E Grenier, INRIA / ENS Lyon), we further are developing a framework to personalise the scheduling of metabolism-targeting therapies, based on the intrinsic evolutionary characteristics of each cancer.
Relevant publications:
- Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes. Laboratory Investigation, 2023.
- A stemness-related ZEB1-MSRB3 axis governs cellular pliancy and breast cancer genome stability. Nature Medicine, 2017.
Dynamics of Progression in Oral Squamous Cell Cancers (OSCC)
We aim understand the genetic makeup of normal oral squamous mucosa as tissue ages, in order to model its genetic composition over time for both healthy and malignant evolutionary outcomes. This integrates a larger effort led by the Saintigny Team and European colleagues (EuroHNPACT) to understand the dynamics of progression, from normal to pre-malignancy to cancer in OSCC, and identify novel treatment and surveillance strategies.
Relevant publications:
- Quantifying local malignant adaptation in tissue‐specific evolutionary trajectories by harnessing cancer’s repeatability at the genetic level. Evolutionary Applications, 2019.
- Separating the Local and Malignant Dimensions of Cancer Adaptation. Cancer Informatics, 2019.