Computational Analysis of Microtubule Dynamics for Personalized Cancer Therapy

个性化癌症治疗的微管动力学计算分析

• 批准号: 8868262
• 负责人: Alexandre Matov
• 金额: $ 4.7万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8868262
• 关键词: Affect; Algorithms; Androgen Receptor; Basic Science; Behavior; Binding; Biological Markers; Biology; Cancer Etiology; Cancer Patient; Cell Nucleus; Cell physiology; Cessation of life; Computer Analysis; Computer Assisted; Computer Vision Systems; Computers; Coupled; Cytoskeleton; Data; Data Set; Dependency; Diagnosis; Disease; Drug Targeting; Dynein ATPase; Event; Eye; FDA approved; Feedback; Gene Targeting; Genes; Genetic Transcription; Goals; Growth; Health; Homeostasis; Image; Image Analysis; In Vitro; Individual; Label; Link; Malignant neoplasm of prostate; Measurable; Measures; Medical; Metastatic Prostate Cancer; Microscope; Microtubule Polymerization; Microtubule Stabilization; Microtubules; Modeling; Motor; Nuclear; Optics; Outcome; PC3 cell line; Paclitaxel; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Phase; Physicians; Polymers; Process; Property; Protein Isoforms; Proteins; RNA Splicing; Renal Cell Carcinoma; Research; Research Project Grants; Resistance; Scheme; Second Primary Neoplasms; Taxane Compound; Testing; Therapeutic; Time; Transcriptional Regulation; Translational Research; Treatment Efficacy; Tubulin; Variant; Work; anticancer research; base; behavior test; cancer cell; cancer therapy; cellular targeting; chemotherapy; clinical decision-making; clinically relevant; design; disease phenotype; docetaxel; in vivo; inhibitor/antagonist; insight; male; mathematical model; novel; programs; prostate cancer cell; receptor binding; response; taxane; tool; trafficking; transcription factor; transcriptome sequencing; tumor

DESCRIPTION (provided by applicant): Optimized optics and feedback-controlled microscope hardware permit efficient acquisition of large, high- quality image datasets. Computer-based analyses deliver fast processing of high volume of image data which manually cannot be accomplished. The most exciting contribution that computer vision systems can make to translational cancer research, however, is to give access to image-based information that is inaccessible by eye. Computer vision programs can be directly coupled to mathematical models that describe the relation between hidden, invisible processes and measurable image events. Changes in the behavior of hidden processes are thus detectable as changes in the image. This study envisages the use of such algorithms to obtain statistically representative results for the differential effects of each of the three FDA-approved taxanes on the microtubule cytoskeleton in prostate cancer (PC) cell lines. My previous work in basic research has demonstrated the ability of computer-based analysis of the microtubule (MT) cytoskeleton to distinguish between weak disease phenotypes and establish links to MT dynamics in renal cell carcinoma. Therefore, the proposed translational research project can impact clinical decision-making by equipping physicians for the first time with a computer-aided tool allowing the design of an effective personalized MT-targeting chemotherapy of metastatic PC patients. Metastatic PC is treated primarily by means of taxane-based chemotherapy with one of the three FDA- approved taxanes (paclitaxel, docetaxel and cabazitaxel). However, currently there is no way of selecting the taxane for chemotherapy based on the particular pattern of dynamic behavior of the MT cytoskeleton in individual patients. In addition, recent data have indicated that AR binds MTs in order to traffic to the nucleus and that there are several clinically relevant AR splice variants i metastatic PC patients. To date, there is no information available on the potential effects of wild type or variant AR on MT dynamics and consequently no information on differential metastatic PC cell response to taxane treatment as a function of cellular AR content. Based on preliminary research, we hypothesize that there are inherent differences in tumor MT dynamics among individual PC patients, and that the presence of AR variants affects specific parameters of MT polymerization dynamics. If correct, this hypothesis has very significant implications for PC treatment. Because different microtubule-targeting drugs (even from within the same class like the taxanes) affect distinct parameters of MT dynamics, it is conceivable that we can match each drug with an individual tumor-specific "MT-dynamics signature" for maximum therapeutic efficacy.

描述（由申请人提供）：优化的光学和反馈控制的显微镜硬件允许有效地获取大型，高质量的图像数据集。基于计算机的分析提供了人工无法完成的大量图像数据的快速处理。然而，计算机视觉系统对转化性癌症研究最令人兴奋的贡献是，它可以提供人眼无法获得的基于图像的信息。计算机视觉程序可以直接耦合到描述隐藏的、不可见的过程和可测量的图像事件之间关系的数学模型。因此，隐藏过程的行为变化可以作为图像的变化来检测。本研究设想使用这种算法来获得具有统计学代表性的结果，以说明fda批准的三种紫杉烷对前列腺癌（PC）细胞系微管细胞骨架的不同影响。我之前在基础研究方面的工作证明了微管（MT）细胞骨架的计算机分析能力，可以区分弱疾病表型，并建立肾细胞癌中微管（MT）动力学的联系。因此，拟议的转化研究项目可以影响临床决策，首次为医生配备计算机辅助工具，允许为转移性PC患者设计有效的个性化mt靶向化疗。转移性PC的治疗主要是紫杉醇为基础的化疗，使用FDA批准的三种紫杉醇（紫杉醇、多西紫杉醇和卡巴他赛）中的一种。然而，目前还没有办法根据个体患者MT细胞骨架的特定动态行为模式来选择紫杉烷进行化疗。此外，最近的数据表明，AR结合mt以运输到细胞核，并且在转移性PC患者中存在几种临床相关的AR剪接变异体。到目前为止，还没有关于野生动物潜在影响的信息