Transcriptome analysis has been extensively used to understand the heterogeneity of breast tumours. Patterns of gene expression defined intrinsic molecular subtypes and signatures able to predict response to therapy and patient outcome. This molecular phenotyping has fostered crucial therapeutic advances. However, cancer cell diversity constitutes a challenge for cancer treatment and deeply impact the outcome of cancer patients. A simultaneous overview of cancer cells and associated stromal cells is critical for the design of improved therapeutic regimes.
Single-cell RNA-seq has emerged as a powerful method to unravel heterogeneity of complex biological systems; this has enabled in vivocharacterization of cell type compositions through unsupervised sampling and modelling of transcriptional states in single cells. Here we use single-cell RNAseq technology to elucidate the function and cellular composition of breast tumours providing high-resolution landscapes during the progression to metastatic disease.
We have developed an inducible model of metastatic disease based on the MMTV-PyMT mouse mammary tumour model(1). We discovered that breast cancer cells are organised in lineages that resemble the mammary gland epithelial hierarchy. We uncovered developmental pathways that control mammary cell fate that are also responsible for the acquisition of metastatic traits, allowing us to characterise the cell of origin responsible for the hallmarks of metastatic progression. Furthermore, our characterisation at single cell resolution allowed us to draw molecular networks of cell-to-cell communication within the tumour microenvironment that ultimately resulted in the acquisition of metastatic disease.
In summary, our approach discovered the cells that contribute to the progression to metastatic disease and characterised their mechanisms of action. Thus, using our preclinical model, we provide proof-of-concept for the design of precise therapeutic strategies for the treatment of advanced breast cancer.