Uncovering the molecular networks underlying non-genetic heterogeneity in cancer cell populations

揭示癌细胞群体非遗传异质性背后的分子网络

基本信息

批准号：
10469459
负责人：
Leonard Alfredo L. Harris
金额：
$ 18.76万
依托单位：
UNIVERSITY OF ARKANSAS AT FAYETTEVILLE
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：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10469459
关键词：
ATAC-seq Antineoplastic Agents Apoptosis BRAF gene Bacteria Biochemical Bioinformatics Biological Models Cancer Model Cancer Patient Cancer cell line Cell Cycle Cell Death Cell Line Cell physiology Cells Cessation of life Chromatin Complex Computer Models Cues DNA Sequence Alteration Decision Making Development Diffusion Drug Tolerance Ensure Epidermal Growth Factor Receptor Epigenetic Process Equilibrium Exhibits Foundations Future Gaussian model Goals Growth Growth Factor Heterogeneity Immunocompromised Host In Vitro Individual Knowledge Lead Malignant Neoplasms Malignant neoplasm of lung Mathematics Messenger RNA Metabolism Methods Modeling Molecular Molecular Profiling Mus Mutate Nature Non-Small-Cell Lung Carcinoma Normal Statistical Distribution Oncogenes Pathway interactions Patients Pharmaceutical Preparations Pharmacotherapy Phenotype Play Population Probability Process Proteins Receptor Protein-Tyrosine Kinases Recurrence Research Resistance Role Signal Pathway Signal Transduction Source System Systems Analysis Techniques Time Transcript Treatment Failure Tumor stage Tyrosine Kinase Inhibitor Validation Work anticancer research base biochemical model biological adaptation to stress cancer cell cancer therapy career career development drug distribution drug sensitivity drug-sensitive dynamic system epithelial to mesenchymal transition exome sequencing experimental study fitness genetic resistance high dimensionality in silico in vitro Model in vivo information processing insight interest kinetic model mRNA Expression melanoma mouse model mutant non-genetic novel novel therapeutics predictive modeling prevent receptor resistance mutation response signature molecule simulation single-cell RNA sequencing skills stem cell differentiation stressor theories therapy resistant treatment strategy tumor tumor heterogeneity

项目摘要

PROJECT SUMMARY Tumor heterogeneity is a major contributor to variable response and treatment failure in cancer patients. Usually, heterogeneity in cancer is thought of in terms of resistance-conferring genetic mutations that pre- exist or emerge during treatment. However, recent studies, including our own, increasingly point to non- genetic sources of heterogeneity as critical factors in the early stages of tumor response. Non-genetic mechanisms are known to underlie cellular processes such as stem cell differentiation and epithelial-to- mesenchymal transitions. In bacteria, isogenic cell populations have been shown to diversify in the absence of perturbations (e.g., drugs) into a variety of cellular phenotypes, each with differential fitness to potential stressors. This “bet hedging” strategy increases the odds that a portion of the population will survive a future, unknown challenge. We, and others, have recently hypothesized that cancer cells employ a similar survival strategy to withstand the initial onslaught of anticancer drugs. So-called “drug tolerant” cells may persist within a patient for extended periods of time before acquiring genetic resistance mutations that lead to tumor recurrence. The objective of this proposal is to uncover the molecular factors that control non-genetic heterogeneity in cancer cell populations using a combined computational and experimental approach. In Aim 1, I propose to construct a detailed kinetic model of the biochemical signaling networks that control division and death decisions in individual cancer cells. It is well established that complex biochemical networks can give rise to multiple stable equilibrium states, known as “attractors.” Each attractor corresponds to a cellular phenotype and can be conceptualized as a basin within an “epigenetic landscape.” Cells can transition between phenotypes with rates dependent upon the depths of the basins and the heights of the barriers separating them. Using a dynamical systems analysis approach, I will mathematically solve for the epigenetic landscape of the biochemical division/death model and quantify molecule signatures for all attractors. In Aim 2, using BRAF-mutant melanoma and EGFR- mutant lung cancer as in vitro model systems, I will use clonal and single-cell RNA sequencing and chromatin accessibility sequencing (ATAC-seq) to enumerate the number and molecular signatures of non-genetic phenotypic states. I will also utilize whole-exome sequencing to establish the non-genetic nature of the phenotypes and immunocompromised mouse models to validate model predictions. Differences between the experimental and in silico molecular signatures will lead to model refinement and further experimentation. Quantifying the epigenetic landscapes of cancer cells will lay the groundwork for novel therapies based on rationally modifying the landscape to favor phenotypes with increased drug sensitivity, an approach termed “targeted landscaping.” This would reduce the size of the drug-tolerant pool and delay, perhaps indefinitely, the acquisition of genetic resistance mutations and tumor recurrence.

项目摘要肿瘤异质性是癌症患者可变反应和治疗失败的主要因素。通常，癌症中的异质性是通过抗药性的基因突变来考虑的在治疗过程中存在或出现。但是，最近的研究，包括我们自己的研究，越来越指向非 - 异质性的遗传来源是肿瘤反应早期的关键因素。非遗传学已知机制是细胞分化和上皮到 - 间充质转变。在细菌中，同生细胞群体已显示出多种多样没有扰动（例如，药物）进入多种细胞表型，每个表型都具有不同的适应性潜在的压力源。这种“赌注对冲”策略增加了一部分人口的几率在未来的未知挑战中生存。我们和其他人最近假设癌细胞员工一种类似的生存策略，可以承受抗癌药物最初的攻击。所谓的“药物在获得遗传抗性之前导致肿瘤复发的突变。该提议的目的是发现分子因素使用合并的计算和实验方法。在AIM 1中，我建议构建生化的详细动力学模型控制单个癌细胞中分裂和死亡决策的信号网络。它已经建立了复杂的生化网络可以产生多种稳定的平衡状态，称为 “吸引者。”每个吸引子对应于细胞表型，可以概念化为盆地在“表观遗传景观”中。细胞可以在表型之间过渡，速率取决于低音的深度和将它们分开的障碍物的高度。使用动态系统分析方法，我将在数学上解决生化划分/死亡模型的表观遗传景观并量化所有吸引子的分子签名。在AIM 2中，使用BRAF突变药物黑色素瘤和EGFR- 突变肺癌作为体外模型系统，我将使用克隆和单细胞RNA测序以及染色质可及性测序（ATAC-SEQ），以列举数量和分子特征非遗传表型状态。我还将利用全外观测序来建立非基因表型和免疫功能低下的小鼠模型的性质以验证模型预测。实验和计算机分子特征之间的差异将导致模型的细化，并且进一步的实验。量化癌细胞的表观遗传景观将为基于合理修改景观以增加药物的表型的新型疗法灵敏度，一种称为“有针对性的景观”的方法。这将减少耐药剂的大小池和延迟，也许是无限期的，是遗传抗性突变和肿瘤复发的获取。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia.

DOI：
10.3389/fgene.2023.1254966
发表时间：
2023
期刊：
FRONTIERS IN GENETICS
影响因子：
3.7
作者：
Velleuer, Eunike;Dominguez-Huettinger, Elisa;Rodriguez, Alfredo;Harris, Leonard A.;Carlberg, Carsten
通讯作者：
Carlberg, Carsten