Project Summary
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.
