Significance: Bone metastatic prostate cancer (mPCa) is currently an incurable disease. While standard of care treatments (androgen deprivation therapy-ADT, chemotherapy) are initially effective, this heterogeneous disease often evolves to become resistant, thus representing a major clinical challenge. Our group also demonstrates that the bone ecosystem contributes to the emergence of resistant mPCa but how the ecosystem in turn, impacts the efficacy of standard of care treatment represents a major gap in our knowledge. Biology driven mathematical models offer a novel and effective means with which to address these complex issues since cancer evolution and bone ecosystem responses to applied therapies can be rapidly tested, optimized for efficacy to delay the onset of resistant disease, and subsequently, validated experimentally. Rationale: Using empirical data, we will generate an agent-based mathematical model to describe the interactions of heterogeneous mPCa cells with the surrounding bone microenvironment. In silico, we will test the effect of standard of care treatments ADT (Lupron) and chemotherapy (docetaxel) on the growth of cancer over time. The model can identify the impact of these treatments on mPCa cells but also the role of other bone cell types such as, mesenchymal stromal cells (MSCs) in disease progression. Based on this rationale, we hypothesize that experimentally powered HCAs can be used to dissect the bone ecosystem effects on mPCa evolution and optimize treatment strategies so as to prevent the emergence of resistant disease. To test this hypothesis, we propose three interdisciplinary aims. Approaches:
In Aim 1, human prostate cancer cell line (VCaP and LAPC4) growth parameters will power a hybrid cellular automaton (HCA) agent-based mathematical model of heterogeneous mPCa in bone. The response of the model to standard of care therapy (ADT and or docetaxel) will be studied and results validated in vivo.
In Aim 2, we will explore the role of the bone ecosystem, specifically MSCs, in controlling the emergence of resistance to standard of care treatments. Human data will be used to assess the clinical applicability of the eco-evolutionary HCA.
In Aim 3, evolutionary algorithms (EA) will be used to guide the adaptive application of standard of care therapy. Innovation/Impact: Our innovative studies will; 1) generate a robust mathematical eco-evolutionary model of bone mPCa that can be used to dissect the role of the bone microenvironment in the emergence of resistance, 2) identify the effects of standard of care therapies on heterogeneous cancer cells and the bone ecosystem and, 3) allow for the rapid determination of optimized adaptive therapies that take into account the contributions of the bone ecosystem. We believe the proposed studies will significantly impact the way treatments are applied to men diagnosed with bone mPCa and ultimately improve their overall survival.
Standard of care treatments (androgen deprivation therapy-ADT, chemotherapy) are initially effective for the treatment of bone metastatic prostate cancer (mPCa) but the disease evolves to become resistant and incurable due to cancer cell heterogeneity and bone ecosystem effects. To dissect the underlying complex interactions, we will generate a robustly parameterized and validated agent-based mathematical model that will allow us to understand the impact and role of the bone ecosystem and standard of care treatments on mPCa evolution. We will then use the math model to optimize and experimentally test evolutionary-informed standard of care adaptive care treatment strategies that will extend therapeutic efficacy and, ultimately, will translate into improving the overall survival of men suffering with incurable bone mPCa.
