Modelling estrogen receptor alpha-positive breast cancer by transformation of normal human mammary epithelial cells

Excerpt

Two-thirds of breast cancers express estrogen receptor alpha (ERα) and are estrogen-dependent for growth, yet the unavailability of accurate in vivo models has long impeded the characterisation of critical events that lead to the development of these luminal subtypes of the disease. Previously, our group successfully created an ERα-positive breast cancer model by quantitative transformation of normal human mammary epithelial cells (HMECs) derived from reduction mammoplasties. HMECs were grown as mammospheres in suspension to enrich for progenitor cells, which were then transformed using lentiviral vectors encoding ERα and TERT as well as the polycomb gene BMI1 and MYC, both of which have been implicated in ERα-positive breast cancer. Injection of transformed HMECs into mammary fad pads of NOD/SCID mice resulted in the formation of estrogen-dependent tumours that metastasised to multiple organs [1], confirming the creation of a model that mirrors the characteristics of human estrogen-dependent breast cancer. Somewhat surprisingly, we observed islands of squamous differentiation in the tumours that formed in the NOD/SCID mice, whereas the large majority of human breast tumours are adenocarcinomas. To address this discrepancy, we are currently testing a combination of our established protocol with new HMECs in vitro culture conditions that have recently been shown to abrogate the squamous phenotype of the resulting tumours in mice [2]. Our model system is a powerful tool for the in vivo characterisation of candidate genes that have been implicated in development of ERα-positive breast cancer. Genes of interest include TNRC9, which has recently been identified in genome-wide association studies as a potential novel breast cancer susceptibility gene [3], as well as TBX3, which is known to play a role in mammary gland development as well as breast tumourigenesis. We are currently testing these genes in our model using overexpression and knockdown approaches. …