Transcriptome analysis has been extensively used to understand the heterogeneity of breast tumours, which defines 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 vivo characterization of cell type compositions through unsupervised sampling and modelling of transcriptional states in single cells. Here we use Genomics Cytometry to elucidate the function and cellular composition of breast tumours. We use the MMTV-PyMT mouse mammary tumour model to provide high-resolution landscapes of the disease progression, delivering simultaneous observation of cellular events that result in the acquisition of the metastatic phenotype. We show how breast cancer cell are organised in cell lineages that resemble the mammary gland epithelial hierarchy, revealing cell plasticity of cancer cells during disease progression and identifying the cells responsible for the hallmarks of cancer progression. Finally we provide the molecular mechanism of-cell-to-cell communication in the context of functional key events during breast cancer progression: EMT, collagen deposition, inflammation and hypoxia.
In summary, we provide a large-scale single-cell transcriptional landscape of breast tumours that allows unprecedented understanding of breast heterogeneity and deep analysis of the events that result in cancer progression. scRNA-seq technology is generating a paradigm-shift in our understanding of biology, applied to tumour biology will lay the first stone for the development of more specific cancer therapies.