权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Instability of Cancer Cell States in Tumor progression (ICCS)

肿瘤进展过程中癌细胞状态的不稳定性 (ICCS)

基本信息

批准号：
10491691
负责人：
Amy Brock
金额：
$ 47.94万
依托单位：
INSTITUTE FOR SYSTEMS BIOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-21 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10491691
关键词：
Animal Model Animals Awareness Behavior Biological Biological Markers Cell Count Cell Culture Techniques Cell Density Cell physiology Cells Characteristics Climate Clinical Complement Complex Data Dependence Detection Diagnosis Ecosystem Event Exhibits Exposure to Genes Genetic Goals Growth In Vitro Indolent Lesion Link Logistics Malignant Neoplasms Mathematics Measurable Measures Microscopic Modeling Molecular Profiling Mus Mutation Pathway interactions Patients Pattern Phase Phenotype Play Population Population Dynamics Population Growth Population Process Pre-Clinical Model Recurrence Regulator Genes Regulatory Pathway Relapse Research Personnel Resolution Risk Role Scheme Screening for cancer Signal Transduction Stochastic Processes Stratification System Systems Theory Testing Translating Tumor Escape Work austin base cancer cell cancer genome cancer invasiveness cell dimension design driver mutation dynamic system experimental study gene regulatory network genome sequencing high dimensionality in vivo indexing mouse model mutant neoplastic cell non-genetic novel strategies prognostic single cell analysis theories transcriptome transcriptome sequencing tumor tumor progression

项目摘要

PROJECT SUMMARY ICCS2020-A1 This multi-PI project conducts experiments to study cell state instability in tumor cells, motivated by the theory of “critical transitions” (CT). CTs are abrupt shifts of behavior of a complex non-linear system and are preceded by system state destabilization. A cancer cell population represents a statistical ensemble of cells, each of which is a nonlinear stochastic dynamical system. The latter is embodied by the gene regulatory network (GRN) and cells are normally in stable attractor states. We hypothesize that cancer cells in small lesions can be poised between either staying dormant or exiting dormancy (“escape”) and that this binary decision is a CT. This implies that to be in such a poised state, the cell state has to be destabilized. Thus, detecting cell state instability, manifest in the cell transcriptomes, can discern if a small tumor is safely in a stable state or poised in the above sense. Many an observation suggests that cell density of the dormant tumor may be a “bifurcation parameter” that drives GRN dynamics, via instability toward the CT, at which a cancer cell population can jump to the state of steady growth. SPECIFIC AIMS. The proposed study is experimental but grounded in theory: Cell state instability is manifest in an increase of the quantity IC that we derived from theory and requires single-cell (sc) transcriptomes in a popu- lation to compute (=dynamics of a statistical ensemble of GRNs). Aim 1 (in vitro) uses large ensembles of micro- cultures (=cancer cell populations) to quantitatively show destabilization and bifurcations of growth behaviors. Aim 2 (in vivo) reevaluates old mouse tumor models in a new scheme that exposes the binary decision (dor- mancy vs. “tumor-take”) to test the hypothesis that clinical dormancy escape is preceded by cell state instability. APPROACH: In Aim 1, using massively-parallel micro-cultures, bulk RNASeq and scRNAseq, we examine hith- erto undistinguished growth modes of cancer cells and measure bistability as a function of cell density (dormancy vs. “take-off”). In Aim 2 we examine our intriguing observations in many mouse models: under specific condi- tions, identified by titrating inoculum cell numbers in creating dormant tumors, some mice exhibit stable dor- mancy and others a robust tumor-take despite same initial conditions. This finding suggests a poised state and defines a bistable regime. Tumor models using cells studied in Aim 1 will be evaluated in our scheme to expose bistable behaviors and Ic computed from scRNAseq data. We anticipate that tumors in unstable dormancy poised to take-off display higher cell state instability (higher IC) than the stably dormant tumors. But sc-transcriptomes will also reveal the genes that drive the CT and how they are linked to the risk of impending dormancy escape. SIGNIFICANCE: While this first-in-its-class study analyzes abstract principles rather than specific molecules, its potential impact is tangible: It predicts the fate trajectory of indolent tumors in a new way, complementing current quest for molecular signatures to classify tumors by prognostic groups, by detecting in single-cell resolution cell population data signs of destabilization that herald an approach to the CT or “tipping point” of dormancy escape. This work also raises awareness of non-linear behaviors for the design of more relevant animal tumor models.

项目总结