Probing the malignant potential of mutant clones in healthy mammary tissue by successive mutagenesis

Introduction

Mutant cells are abundant in normal tissues, but only very few will transform into life-threatening cancer. The triggers for these mutant clones to transform are unclear. Phylogenetic studies of solid cancers hinted towards an impact of the order of mutation acquisition on the malignant potential of mutant clones. However, to date, no functional studies exist validating these findings in the in vivo context.

Interaction with Environment

Mutant clones closely interact with their environment. Intravital microscopy experiments from my lab showed that phenotypically normal, but mutant clones in the mammary epithelium, the tissue of origin of breast cancer, dynamically rewire their environment in a mutation-specific manner.

Hypothesis

Hence, I hypothesize that the transforming potential of a mutant clone does not lie in the combination of mutations, but rather in the order in which these mutations were acquired. This sequential acquisition leads to the way in which the environment of the mutant clone was rewired, resulting in either a permissive or a resistant environment for tumor initiation.

Research Gap

Tools to study the sequential acquisition of mutations in vivo do not exist. Therefore, the possibility that one mutation may need to precede the other for a mutant clone to transform has not been studied.

Project Overview

SUCCESSion will fill this gap and develop a novel sequential somatic gene editing technology. This will be combined with intravital imaging and transcriptional profiling to study for the first time the effects of different sequences of mutations on cell behavior and the sequential rewiring of the micro-environment in the mammary gland.

Impact

SUCCESSion will have a groundbreaking impact from both a technical and biological point of view:

1. It will establish a novel way of modeling pre-cancer in vivo.
2. It will elucidate the temporal evolution of pre-cancer in unprecedented detail.

This will lead to a better understanding of tumor initiation and improve risk prediction, enabling early interventions to stop cancer before it starts.