Recording the natural history of cancer progression using a Crainbow model of HER2+ cancer

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PROJECT SUMMARY/ABSTRACT Background: The step-wise accumulation of somatic mutations and progressive changes to cell behavior provides a rationale for the cancer screening era. However, this fails to explain de novo diagnosis of lethal metastatic disease in the apparent absence of screen detectable precancerous lesions. Accumulating evidence suggests that invasive cancers may initiate without a prolonged and detectable precancerous phase. In addition, proliferative lesions do not deterministically progress, suggesting that many early cancers are overtreated. Although a dramatic increase in early-detection has been observed, a similar dramatic decrease in lethal metastatic disease has not been observed. Some have even argued that many screen-detected early lesions spontaneously resolve or regress. In contrast, the most lethal cancers escape detection and metastasize prior to detection. Hypothesis: We hypothesize that cancer progression, like normal tissue homeostasis, is stochastic and subject to defined spatial and microenvironmental constraints. Specific Aims: Aim 1. To define the homeostatic principles underlying the progression of breast cancer in a mouse model of HER2+ breast cancer. Aim 2. To dissect the role of HER2 isoforms on the metastatic cascade of breast tumors. Study design/Methods: Here we will use a proven cancer rainbow modeling system for visualizing and analyzing tumor growth rates in vivo. State-of-the-art multiphoton live imaging will be used to image and quantify tumor progression potential in space and time within the mammary gland along with microenvironmental features (ECM, macrophages, blood vessels). Longitudinal imaging over eight weeks will capture the rates of tumor progression and the origin of invasive breast cancers in an in vivo and immune intact mouse model. Relevance: More than six decades ago, surgical oncologists seeking to explain why earlier surgical intervention had not improved breast cancer survival, postulated that adaptations between the tumor and host set the trajectory of an invasive cancer many years before diagnosis. Proponents argued that the formative early years of tumor-TME adaptation had already destined some tumors to become invasive and lethal. In addition, many breast cancer lesions may actually regress suggesting that cancer progression may be stochastic. Novel imaging modalities and sophisticated tumor lineage training strategies are needed to solve this challenging problem. Our experiments utilizing mouse models and high-resolution longitudinal imaging during the course of tumor progression will determine whether cancer progression is deterministic or not, adding an important value towards understanding breast cancer progression.