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

使用综合数学建模方法定义骨生态系统对转移性前列腺癌演变和治疗反应的影响

• 批准号: 10189536
• 负责人: DAVID BASANTA GUTIERREZ
• 金额: $ 51.51万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-11 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10189536
• 关键词: Address; Algorithms; Automobile Driving; Behavior; Biological; Biological Models; Biology; Biopsy; Bone Diseases; Caring; Cell Line; Clinical; Clinical Trials; Coculture Techniques; Complex; Coupled; Data; Diagnosis; Disease; Disease Progression; Disease Resistance; Dose; Ecosystem; Enantone; Enhancing Lesion; Evolution; Flow Cytometry; Goals; Growth; Heterogeneity; Histologic; Histology; Human; Hybrids; In Vitro; Knowledge; LAPC4; Label; Malignant Bone Neoplasm; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Mesenchymal Stem Cells; Metastatic Prostate Cancer; Modeling; Monitor; Mus; Osteoblasts; Osteoclasts; Outcome; Output; Pain; Phenotype; Prostate Cancer therapy; Refractory Disease; Resistance; Role; Schedule; Testing; Time; Translating; Treatment Efficacy; Tumor Burden; VCaP; androgen deprivation therapy; base; bone; bone cell; burden of illness; cancer cell; cancer heterogeneity; cell type; chemotherapy; clinical application; data modeling; docetaxel; effective therapy; experimental study; human data; human disease; iliac artery; improved; in silico; in vivo; in vivo Model; innovation; long bone; mathematical model; men; mesenchymal stromal cell; novel; osteoblast differentiation; predictive test; prevent; prostate cancer cell; prostate cancer cell line; refractory cancer; response; single photon emission computed tomography; standard of care; therapy resistant; treatment optimization; treatment response; treatment strategy

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.

项目概要 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 护理治疗（雄激素剥夺疗法-ADT、化疗）最初是有效的，这种异质性 疾病往往会演变为耐药性，因此是一项重大的临床挑战。我们组还有 表明骨生态系统有助于耐药性 mPCa 的出现，但如何 生态系统反过来又影响护理治疗标准的功效，这代表了我们知识上的重大差距。 生物学驱动的数学模型提供了一种新颖有效的方法来解决这些复杂的问题 由于可以快速测试癌症进化和骨生态系统对应用疗法的反应， 针对延迟耐药性疾病发生的功效进行了优化，并随后进行了实验验证。 理由：使用经验数据，我们将生成一个基于主体的数学模型来描述 异质 mPCa 细胞与周围骨微环境的相互作用。在计算机上，我们将测试 标准护理治疗 ADT（Lupron）和化疗（多西他赛）对癌症生长的影响 随着时间的推移。该模型可以识别这些治疗对 mPCa 细胞的影响，以及其他骨骼的作用 细胞类型，例如疾病进展中的间充质基质细胞 (MSC)。基于这个道理，我们 假设实验驱动的 HCA 可用于剖析骨生态系统对 mPCa 的影响 进化并优化治疗策略，以防止耐药性疾病的出现。为了测试这个 假设，我们提出三个跨学科目标。 方法：在目标 1 中，人类前列腺癌细胞系（VCaP 和 LAPC4）生长参数将为 基于混合细胞自动机 (HCA) 代理的骨中异质 mPCa 数学模型。这 将研究模型对标准护理治疗（ADT 和或多西他赛）的反应并验证结果 体内。在目标 2 中，我们将探讨骨生态系统（特别是 MSC）在控制 出现对标准护理治疗的耐药性。人体数据将用于评估临床 生态进化HCA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）生成稳健的数学生态进化模型 骨 mPCa 可用于剖析骨微环境在阻力出现中的作用， 2) 确定标准护理疗法对异质癌细胞和骨生态系统的影响 3）允许快速确定优化的适应性疗法，其中考虑到 骨骼生态系统的贡献。我们相信拟议的研究将对这一方式产生重大影响 治疗适用于诊断患有骨转移性前列腺癌的男性，并最终提高他们的总体生存率。