ena-STUDY-GARVAN INSTITUTE OF MEDICAL RESEARCH-22-03-2019-01:05:02:757-69
Single cell transcriptomics reveals marked heterogeneity for intrinsic molecular subtype and cellular function in breast cancer models and clinical tissue
Breast cancers exhibit substantial clinical heterogeneity, consisting of a number of intrinsic subtypes that predict prognosis and influence clinical treatment. To explore epithelial cellular heterogeneity within breast cancers we used single cell transcriptomics to examine thousands of cells from an Estrogen Receptor (ER) positive breast cancer cell-line, a treatment-na•ve PDX model of ER+ breast cancer and an untreated primary ER+ tumour. We show that intrinsic subtype, established from bulk tumours, can be ascribed to individual cells and is heterogenous across cells within tumours and cell-lines. We observed significant heterogeneity in the expression and transcriptional activity of ER, an important driver and therapeutic target, which correlated with expression of two known transcriptional co-regulatory factors of ER, Forkhead Box A1 (FOXA1) and Progesterone Receptor (PR). Intriguingly, we show that ER can differentially induce or suppress gene expression in distinct cellular subpopulations. Unsupervised clustering identified two distinct sub-populations of cells, and their putative transcriptional drivers, that possess unique signatures of transcriptional plasticity and de novo resistance to endocrine therapies. Finally, we extended our study to a clinical ER+ breast cancer and highlight the complex epithelial heterogeneity present in primary tumours, recapitulating many of the features observed in the models. Overall, these results suggest a high degree of epithelial heterogeneity within breast cancer that is present in primary tumours, as well as models of disease, including cell lines commonly considered to be homogenous. We show that single-cell transcriptomics can be used to identify sub-populations of cells with phenotypes of potential clinical relevance, and their putative transcriptional drivers. This approach may lead to new insights into de novo drug resistance.
Breast cancers exhibit substantial clinical heterogeneity, consisting of a number of intrinsic subtypes that predict prognosis and influence clinical treatment. To explore epithelial cellular heterogeneity within breast cancers we used single cell transcriptomics to examine thousands of cells from an Estrogen Receptor (ER) positive breast cancer cell-line, a treatment-na•ve PDX model of ER+ breast cancer and an untreated primary ER+ tumour. We show that intrinsic subtype, established from bulk tumours, can be ascribed to individual cells and is heterogenous across cells within tumours and cell-lines. We observed significant heterogeneity in the expression and transcriptional activity of ER, an important driver and therapeutic target, which correlated with expression of two known transcriptional co-regulatory factors of ER, Forkhead Box A1 (FOXA1) and Progesterone Receptor (PR). Intriguingly, we show that ER can differentially induce or suppress gene expression in distinct cellular subpopulations. Unsupervised clustering identified two distinct sub-populations of cells, and their putative transcriptional drivers, that possess unique signatures of transcriptional plasticity and de novo resistance to endocrine therapies. Finally, we extended our study to a clinical ER+ breast cancer and highlight the complex epithelial heterogeneity present in primary tumours, recapitulating many of the features observed in the models. Overall, these results suggest a high degree of epithelial heterogeneity within breast cancer that is present in primary tumours, as well as models of disease, including cell lines commonly considered to be homogenous. We show that single-cell transcriptomics can be used to identify sub-populations of cells with phenotypes of potential clinical relevance, and their putative transcriptional drivers. This approach may lead to new insights into de novo drug resistance.