Defining bone ecosystem effects on metastatic prostate cancer evolution and treatment response using an integrated mathematical modeling approach

项目摘要 意义：骨转移性前列腺癌(MPCA)目前是一种不治之症。虽然标准是 护理治疗(雄激素剥夺疗法-ADT，化疗)最初是有效的，这是不同的 疾病往往演变成耐药，因此是一个重大的临床挑战。我们组也是 表明骨骼生态系统有助于耐药MPCA的出现，但 反过来，影响生态系统疗效的护理标准治疗是我们知识中的一个重大缺口。 生物驱动的数学模型为解决这些复杂问题提供了一种新颖而有效的方法 由于癌症进化和骨生态系统对应用治疗的反应可以快速测试， 针对延缓耐药疾病的发作进行了有效性优化，并随后进行了实验验证。 基本原理：使用经验数据，我们将生成一个基于代理的数学模型来描述 异种MPCA细胞与周围骨微环境的相互作用。在Silico中，我们将测试 ADT(路普隆)和化疗(多西紫杉醇)对肿瘤生长的影响 随着时间的推移。该模型可以识别这些处理对MPCA细胞的影响，也可以识别其他骨骼的作用 疾病进展中的细胞类型，如间充质基质细胞(MSCs)。基于这一理由，我们 假设实验提供动力的HCA可以用来剖析骨生态系统对MPCA的影响 进化和优化治疗策略，以防止出现耐药疾病。为了测试这一点 假设，我们提出了三个跨学科的目标。 方法：在目标1中，人前列腺癌细胞株(VCaP和LAPC4)的生长参数将为 基于混合元胞自动机(Hca)智能体的异质骨MPCA数学模型。这个 将研究模型对标准护理治疗(ADT和/或多西紫杉醇)的反应，并验证结果 在活体内。在目标2中，我们将探讨骨骼生态系统，特别是骨髓间充质干细胞在控制 出现对标准护理治疗的抵抗力。人类数据将被用来评估临床 生态进化的人类健康评估的适用性。在目标3中，将使用进化算法(EA)来指导 标准护理治疗的适应性应用。 创新/影响：我们的创新研究将：1)生成稳健的生态进化数学模型 可以用来剖析骨微环境在抵抗出现中的作用的骨MPCA， 2)确定标准护理治疗对异质癌细胞和骨生态系统的影响 以及，3)允许快速确定考虑到 对骨骼生态系统的贡献。我们相信，拟议的研究将显著影响 治疗适用于被诊断为骨骼MPCA的男性，并最终提高他们的总体存活率。

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.