Oncology Abstracts

The plastic cancer cell model establishes that genetically identical cancer cells undergo bi-directional conversions between the highly aggressive tumour-initiating (TIC) state and the non-TIC cell state. We have identified subpopulations of breast cancer cells that readily switch from the non-TIC to TIC state, through activation of the EMT transcription factor ZEB-1. We have shown that non-TICs of basal BrCa are uniquely endowed with this plastic phenotype due to the cell’s ability to maintain the chromatin at the ZEB-1 promoter in a poised state, ready for activation. This bivalent regulation confers non-TICs with the ability to convert toward more aggressive cellular states, acquiring metastatic and adaptive potential. Characterizing cellular plasticity in clinical samples, and the molecular networks underlying it, will allow us to better understand tumour progression, chemoresistance and recurrence. We are currently testing cell plasticity dynamics in human cell models and patient derived xenografts (PDXs), combining leading-edge genomic techniques (single cell transcriptomic analysis, RNA-seq and MINT-CHIP) with functional assays and in vivo models of tumorigenesis. By FACS (Fluorescence activated cell sorting) analysis we have observed matching profiles of TIC and non-TIC populations present in basal-like cell lines and triple negative BrCa PDXs. Using newly identified subpopulations we are currently defining the molecular network that controls non-TIC to TIC inter-conversions, as well as TICs evolution, by crosschecking epigenetic and transcriptomic data. Our preliminary results point to a role of the canonical and non-canonical Wnt/Notch signaling pathways, adhesion and immune system response signals in regulating the transition between cellular states. Through a clearer understanding of the mechanisms that drive non-TIC to TIC plasticity, we aim to discover novel therapeutic strategies that can target phenotypic switching to more aggressive cellular states, ultimately aiming to improve patient outcomes.