Predicting Prostate Cancer Aggressiveness

预测前列腺癌的侵袭性

• 批准号: 8332789
• 负责人: Alexander Robertson Allan Anderson
• 金额: $ 57.42万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-14 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8332789
• 关键词: Address; Biological; Cancer Patient; Cells; Characteristics; Clinical; Complex; Computer Simulation; Data; Development; Disease; Disease Progression; Elements; Environment; Epithelium; Genetic Programming; Human; Instruction; Libraries; Malignant Neoplasms; Malignant neoplasm of prostate; Methodology; Microscopy; Modeling; Molecular; Outcome; Output; Pathway interactions; Patients; Phase; Process; Resources; Sampling; Signal Transduction; Stromal Cells; Testing; Therapeutic; Time; Triage; Triplet Multiple Birth; Validation; base; cell type; cohort; extracellular; human data; human disease; in vivo; mathematical model; neoplastic cell; prognostic; tool; tumor; tumor progression

DESCRIPTION (provided by applicant): Cancer is a complex disease that is driven by interactions between tumor cells but also stromal cells and the microenvironment. We hypothesize that the interaction between the different cellular components of the tumor and the molecular signaling networks within each cell can delineate aggressive prostate cancer. We have selected intracellular and extracellular pathways and cell types that are representative of fundamental processes in human prostate cancer. We will use data from a large cohort of prostate cancer patients. These inputs, derived using state of the art methodology, and provided on a cell-per-cell basis will be used to derive a multi-scale mathematical model. This wealth of human data has not previously been achievable and will serve to both parameterize (on multiple scales) and validate the model. Mathematical modeling will generate a library of network triplets (i.e. intracellular signaling networks for tumor epithelium, normal stroma and reactive stroma) whose interactions can describe patient outcome. Networks will be selected using a genetic algorithm to identify and fix the fittest triplets. Triplets will then be selected based upon their ability to reflect invasive or non-invasive disease over a biologically relevant time period and subject to triage based upon their representation of histochemical characteristics. The most representative triplets will be validated against biological endpoints using in vivo experimentation. The validation phase will recapitulate key elements of the mathematical model in vivo to identify those models most functionally-relevant to human disease. These will be tested in vivo to make predictions that confirm or refute these results in silico. The most robust models (those that pass the testing and validation phases) will be compared to a test cohort of human clinical samples to correlate their characteristics against survival endpoints. Our unique combination of resources and team expertise represents an unparalleled environment providing a synergistic approach to understand prostate cancer beyond the limitations of currently applied scientific methodology. Our models begin and end with human data, assuring that the final products will provide new understanding of human prostate cancer. Three specific aims will be addressed: Specific aim 1) Develop and Parameterize a Multi-scale Mathematical Model of Prostate Cancer Specific Aim 2) Biological Validation and Testing of Candidate Mathematical Outcomes. Specific Aim 3) Clinical Validation of Mathematical Outcomes. RELEVANCE (See instructions): Mathematical models have the potential to act as useful prognostic tools but have not yet been well developed for the study of cancer progression. By basing models on data-rich outputs from deconvolution microscopy examination of clinical samples new models with unparalleled detail will be created and tested. This will allow for the development of new prognostic tools and therapeutic strategies to control disease progression.

描述（由申请人提供）：癌症是一种复杂的疾病，是由肿瘤细胞之间的相互作用，基质细胞和微环境驱动的。我们假设肿瘤的不同细胞成分与每个细胞内的分子信号网络之间的相互作用可以描绘侵袭性的前列腺癌。我们选择了代表人类前列腺癌基本过程的细胞内和细胞外途径和细胞类型。我们将使用来自大量前列腺癌患者的数据。这些输入是使用艺术方法学得出的，并以细胞细胞为基础提供的输入将用于得出多规模的数学模型。以前，这种丰富的人类数据尚无法实现，并且将用于参数化（在多个尺度上）并验证模型。数学建模将生成一个网络三胞胎库（即肿瘤上皮，正常基质和反应性基质的细胞内信号网络），其相互作用可以描述患者的结果。将使用遗传算法选择网络来识别和修复优胜品三重态。然后，将根据其在生物学相关的时间段内反映侵入性或非侵入性疾病的能力来选择三胞胎，并根据其对组织化学特征的表现进行分类。最具代表性的三胞胎将使用体内实验对生物学终点进行验证。验证阶段将概括体内数学模型的关键要素，以识别与人类疾病最相关的这些模型。这些将在体内进行测试，以做出确认或反驳这些结果的预测。将最强大的模型（通过测试和验证阶段的模型）与人类临床样本的测试队列进行比较，以将其特征与生存终点相关联。我们的独特资源和团队专业知识组合代表了一个无与伦比的环境，该环境提供了一种协同的方法来了解前列腺癌的局限性，超出了当前应用的科学方法论的局限性。我们的模型从人类数据开始和结束，确保最终产品将提供对人类前列腺癌的新了解。将解决三个具体目标：具体目标1）开发和参数化前列腺癌特异性目标的多尺度数学模型2）生物学验证和候选数学结果的测试。特定目的3）数学结果的临床验证。相关性（请参阅说明）：数学模型有可能充当有用的预后工具，但尚未得到很好的发展来研究癌症的进展。通过将模型基于对临床样本的反卷积显微镜检查的数据丰富的输出，将创建和测试具有无与伦比的细节的新模型。这将允许开发新的预后工具和控制疾病进展的治疗策略。