Despite the impressive catalog of mutations amassed from sequencing human breast cancer genomes, most genomes decoded so far derive from early-stage disease, which likely leaves many genetic events driving disease progression undiscovered. Ongoing efforts to decode the more complex genomes from more advanced and drug-resistant breast cancers hold great promise, but the higher mutation burden in these cancers complicates the difficult task of distinguishing causative driver events from innocuous passengers events. Animal models that offer a genome-wide view of the mutations acquired during breast cancer progression can provide cross-species validation crucial for validating candidate driver genes. Drawing on the breast cancer genomics landscape and our own mouse modeling work, we formulated the hypothesis that breast cancer progression depends on driver mutations acquired in a disease stage- specific manner, obscured by continuously accruing passenger mutations. Based on this hypothesis, we developed the following long-term goal: discover novel drivers of breast cancer progression by performing genetic screens in mouse breast cancer models using timed mobilization of transposons (so-called ?jumping genes?) at discrete disease stages. In unpublished preliminary studies, we engineered new mouse models for discovering and validating candidate mammary oncogenes. To enable oncogene discovery, we generated an inducible version of the Sleeping Beauty (SB) transposition system, which enables timed transposon mobilization in the mouse breast. We confirmed that this system provides an efficient cancer gene discovery platform by identifying known and novel oncogenes that cooperate with Wnt pathway activation to drive mammary tumorigenesis in vivo. To complement this high-throughput cancer gene discovery platform, we designed a novel strategy for efficiently validating candidate oncogenes and probing their mechanisms of action by monitoring the growth of mammary tissue fragments (organoids) grown in 3D culture. This strategy employs live-cell imaging for quantitative scoring of the oncogene-driven events that culminate in mammary cell overgrowth. We will address our hypothesis by completing three Specific Aims.
In Aim 1, transposon-based gene discovery will be employed in the context of mouse models engineered to express known breast cancer- relevant oncogenes. Our goal is to identify novel candidate genes that drive the transition from mammary hyperplasia to focal mammary cancer.
In Aim 2, delayed transposon mobilization will be initiated within established mammary cancers arising in the classic MMTV-Neu breast cancer model. Our goal is to identify candidate genes that drive resistance to Lapatinib, a clinically important drug that blocks Her2/Neu signaling.
In Aim 3, we will optimize our live-cell imaging platform and apply it to test whether and how candidate cancer genes confer malignant capabilities to mammary cells grown in 3D culture.
The mutations and genetic pathways responsible for driving breast cancer progression toward advanced, drug- refractory disease remain poorly defined. Due to this knowledge gap, important drug targets likely remain undiscovered. Using innovative mouse modeling strategies, we will perform genetic screens, as well as downstream validation studies and cross-species comparisons, to identify novel genetic pathways driving breast cancer progression.