Engineering model-based systems to monitor and steer subclonal dynamics

基于工程模型的系统来监测和引导亚克隆动态

基本信息

批准号：
10633383
负责人：
Noemi Andor
金额：
$ 23.63万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10633383
关键词：
Adopted Back Behavior Biological Biological Assay Biological Markers Biopsy Cancer Biology Cancer cell line Carrying Capacities Cell Count Cell Culture Techniques Cell Line Cell Lineage Cell division Cells Clinical Clinical Trials Collaborations Complex Computer Analysis Computer Vision Systems Computer software Crystallization DNA Data Data Collection Databases Drug Screening Early Diagnosis Engineering Environment Equipment Evolution Exhibits Experimental Designs Failure Foundations Future Generations Genetic Genetic Transcription Genome Genomic Instability Genotype Growth Habits Harvest Health Heterogeneity Image In Vitro Individual Java Laboratory Research Laws Link Malignant Neoplasms Mathematics Measurement Methodology Modeling Monitor Morphology Mycoplasma Nature Nutrient Oncology Pathway interactions Periodicals Pharmaceutical Preparations Phase Phenotype Population Primary Neoplasm Proteome Protocols documentation Publishing Reagent Reproducibility Research Personnel Resolution Scientist Silicon Dioxide System Testing Time cancer heterogeneity carcinogenesis contrast imaging cost driver mutation environmental adaptation environmental change experimental analysis experimental study fitness flexibility genetic pedigree high throughput screening in vivo live cell imaging malignant stomach neoplasm mathematical model multiple omics pre-clinical prototype response single cell sequencing single-cell RNA sequencing specific biomarkers success temporal measurement transcriptome transcriptome sequencing tumor

项目摘要

ABSTRACT Primary tumors as well as cancer cell lines have been shown to exhibit extensive genetic and transcriptional heterogeneity, with multiple subclones co-existing in the same cancer population.1 Even after decades of in-vitro growth, established cell cultures continue to evolve.2 The heterogeneity of cancer cell lines over space and time crystallizes into three unmet needs: i) cell culture protocols that offer a high temporal resolution on in-vitro growth dynamics; ii) close monitoring of the temporal separation between genotypic and phenotypic measurements and iii) reconciling the cost-prohibitive nature of repeated high-throughput multi-omic measurements with ceaseless changes in subclonal composition. To fill these needs, we propose to engineer how in-vitro and in-silica experiments interact into a software solution called CLONEID. An SOL database in the backend, a Java core and an R user interface will come together to form two modules: One will record the pedigree of lineages grown in a lab and use computer vision to monitor phenotypic changes, such as variable growth rates. The second module will link subclonal multi-omics profiles from different high throughput assays to each other and to the phenotypes from the first module. In aim 1 we will develop the first module and use it to demonstrate feasibility of monitoring phenotypic transitions of cell lines with CLONE ID at high temporal resolution, without any specialized equipment. Aim 2 will use this data in conjunction with existing single cell sequencing of the same cell lines to develop and test the second module and use it to identify subclone-specific biomarkers of growth. Together these aims will pave the way to more complex mathematical models of carcinogenesis, that do not have to rely on the simplifying assumption that individual driver mutations have equal fitness effects and that individual subclones have a fixed growth rate. In the future, the framework developed here will serve as foundation to deploy computer vision for early detection of morphological changes, including adaptation from in-vivo to in-vitro growth and mycoplasma contamination.

抽象的原发性肿瘤以及癌细胞系已被证明表现出广泛的遗传和转录异质性，多个亚克隆共存于同一癌症群体中。1 即使经过数十年的体外研究生长，已建立的细胞培养物继续进化。2 癌细胞系随空间和时间的异质性具体化为三个未满足的需求：i) 为体外生长提供高时间分辨率的细胞培养方案动力学; ii) 密切监测基因型和表型测量之间的时间间隔 iii) 协调重复高通量多组学测量的成本高昂性质与不断的亚克隆组成的变化。为了满足这些需求，我们建议设计如何进行体外和硅内实验与名为 CLONEID 的软件解决方案进行交互。后端有一个 SOL 数据库、一个 Java 核心和一个 R 用户界面将组合在一起形成两个模块：一个模块将记录在一个环境中生长的谱系谱系。实验室并使用计算机视觉来监测表型变化，例如可变增长率。第二模块将来自不同高通量检测的亚克隆多组学概况相互关联并与表型关联从第一个模块开始。在目标 1 中，我们将开发第一个模块并用它来演示监控的可行性无需任何专门设备，即可在高时间分辨率下使用 CLONE ID 进行细胞系的表型转变。目标 2 将使用这些数据与相同细胞系的现有单细胞测序相结合来开发和测试第二个模块并用它来识别亚克隆特异性生长生物标志物。这些目标共同将为更复杂的致癌数学模型铺平道路，该模型不必依赖于简化假设个体驱动突变具有相同的适应度效应并且个体亚克隆具有固定的适应度效应增长率。将来，这里开发的框架将作为部署计算机视觉的基础早期检测形态变化，包括从体内到体外生长和支原体的适应污染。