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）目前是一种无法治愈的疾病。虽然标准的 护理治疗（雄激素剥夺疗法-ADT，化疗）最初有效，这种异质性 疾病往往演变成耐药性，因此代表了一个主要的临床挑战。本集团亦 这表明，骨生态系统有助于耐药mPCa的出现， 生态系统反过来又影响标准治疗的疗效，这是我们知识中的一个重大空白。 生物学驱动的数学模型提供了一种新颖而有效的手段来解决这些复杂的问题 由于癌症演变和骨生态系统对应用疗法的反应可以快速测试， 针对延迟抗性疾病的发作的功效进行优化，并且随后进行实验验证。 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 异质mPCa细胞与周围骨微环境的相互作用。在计算机模拟中，我们将测试 标准治疗ADT（Lupron）和化疗（多西他赛）对癌症生长的影响 随着时间该模型可以识别这些治疗对mPCa细胞的影响，也可以识别其他骨细胞的作用。 细胞类型，如间充质基质细胞（MSC）在疾病进展中的作用。基于这一理论，我们 假设实验动力HCA可用于剖析骨生态系统对mPCa的影响 发展和优化治疗策略，以防止耐药疾病的出现。为了验证这一 假设，我们提出了三个跨学科的目标。 方法：在目标1中，人前列腺癌细胞系（VCaP和LAPC 4）生长参数将为前列腺癌细胞系（VCaP和LAPC 4）的生长提供动力。 混合细胞自动机（HCA）代理人为基础的数学模型的异质mPCa在骨。的 将研究模型对标准治疗（ADT和/或多西他赛）的反应，并验证结果 in vivo.在目标2中，我们将探索骨生态系统，特别是MSC，在控制骨组织中的作用。 出现对标准护理治疗的耐药性。人类数据将用于评估临床 生态进化HCA的适用性。在目标3中，进化算法（EA）将用于指导 标准治疗的适应性应用。 创新/影响：我们的创新研究将：1）生成一个强大的数学生态进化模型， 骨mPCa可用于解剖骨微环境在抵抗出现中的作用， 2)确定标准治疗对异质性癌细胞和骨生态系统的影响 以及，3）允许快速确定考虑到以下因素的优化适应性疗法： 骨骼生态系统的贡献。我们认为，拟议的研究将大大影响 治疗应用于诊断为骨mPCa的男性，并最终改善他们的总体存活率。

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.