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

• 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
• 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

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显示出广泛的遗传和转录 异质性，多个子克隆在同一癌症种群中共存。1即使经过数十年的体外， 生长，已建立的细胞培养物继续发展。2癌细胞系在时空上的异质性 结晶成三个未满足的需求：i）细胞培养方案，可提供有关体外生长的高时间分辨率 动力学; ii）密切监测基因型和表型测量与表型之间的时间分离和 iii）核对反复高通量多摩变测量的成本良好性质 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二元实验 互动成一个称为CloneID的软件解决方案。后端中的SOL数据库，Java Core和一个 R用户界面将组合在一起形成两个模块：一个将记录在 实验室和使用计算机视觉监视表型变化，例如可变增长率。第二个模块 将从不同的高吞吐量测定与彼此之间和表型联系起来 从第一个模块。在AIM 1中，我们将开发第一个模块并使用它来证明监视的可行性 具有高时间分辨率的克隆ID的细胞系的表型过渡，没有任何专业设备。 AIM 2将与同一单元线的现有单细胞测序结合使用此数据以开发和 测试第二个模块，并使用它来识别生长的亚克隆特异性生物标志物。这些目标将共同 为更复杂的癌变数学模型铺平了道路，这些模型不必依靠简化 假设单个驾驶员突变具有相等的适应性效果，并且单个子电源具有固定 增长率。将来，这里开发的框架将成为部署计算机愿景的基础 早期检测形态学变化，包括从体内到体外生长和支原体的适应 污染。