Modelling breast cancer progression using single-cell RNA-seq
Garvan Institute, 384 Victoria St Darlinghurst, NSW Australia.
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 the cell type agnostic, high-throughput microfluidic-based, single-cell RNA-seq method Drop-seq to elucidate the function and cellular composition of breast tumours. We use the MMTV-PyMT ± Elf5 mouse mammary tumour model to provide high-resolution landscapes of the disease and highlight cellular events that result in the acquisition of the metastatic phenotype. We show breast cancer cell composition and tumour heterogeneity with unprecedented definition, elucidating the cellular and molecular complexity of tumour progression within the context of a complex multicellular environment.