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
基本信息
- 批准号:10403652
- 负责人:
- 金额:$ 32.32万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAlgorithmsAutomobile DrivingBehaviorBiologicalBiological ModelsBiologyBiopsyBone DiseasesCaringCell LineClinicalClinical TrialsCoculture TechniquesComplexCoupledDataDiagnosisDiseaseDisease ProgressionDisease ResistanceDoseEcosystemEnantoneEnhancing LesionEvolutionFlow CytometryGoalsGrowthHeterogeneityHistologicHistologyHumanHybridsIn VitroKnowledgeLAPC4LabelMalignant Bone NeoplasmMalignant NeoplasmsMalignant neoplasm of prostateMathematicsMesenchymal Stem CellsMetastatic Prostate CancerModelingMonitorMusOsteoblastsOsteoclastsOutcomeOutputPainPhenotypeProstate Cancer therapyRefractory DiseaseResistanceRoleScheduleTestingTimeTranslatingTreatment EfficacyTumor BurdenVCaPandrogen deprivation therapybasebonebone cellburden of illnesscancer cellcancer heterogeneitycell typechemotherapyclinical applicationdata modelingdocetaxeleffective therapyexperimental studyhuman datahuman diseaseiliac arteryimprovedin silicoin vivoin vivo Modelinnovationlong bonemathematical modelmenmesenchymal stromal cellnovelosteoblast differentiationpredictive testpreventprostate cancer cellprostate cancer cell linerefractory cancerresponsesingle photon emission computed tomographystandard of caretherapy resistanttreatment optimizationtreatment responsetreatment strategy
项目摘要
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
DAVID BASANTA GUTIERREZ其他文献
DAVID BASANTA GUTIERREZ的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('DAVID BASANTA GUTIERREZ', 18)}}的其他基金
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 - 财政年份:2020
- 资助金额:
$ 32.32万 - 项目类别:
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:
10667554 - 财政年份:2020
- 资助金额:
$ 32.32万 - 项目类别:
Multiscale Modeling of Bone Environment Responses to Metastatic Prostate Cancer
骨环境对转移性前列腺癌反应的多尺度建模
- 批准号:
9292278 - 财政年份:2016
- 资助金额:
$ 32.32万 - 项目类别:
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 32.32万 - 项目类别:
Continuing Grant