Modeling Neoplastic Progression in Barrett's Esophagus - Renewal -2

巴雷特食管肿瘤进展建模 - 更新 -2

基本信息

批准号：
10594704
负责人：
Carlo Maley
金额：
$ 49.89万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
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：
2023-02-07 至 2028-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10594704
关键词：
Barrett Esophagus Bayesian Analysis Benign Biological Markers Biopsy Cancer Patient Cell model Cells Cessation of life Classification Clinic Clinical Clonal Expansion Collection Consensus Data Decision Trees Detection Development Diagnosis Disease Early Intervention Epigenetic Process Equilibrium Esophageal Adenocarcinoma Esophageal Tissue Esophagectomy Evolution Future Generations Genes Genetic Genetic Models Genomics Heritability Individual Intervention Length Life Malignant Neoplasms Malignant neoplasm of esophagus Measures Methylation Modeling Morbidity - disease rate Mutation Neoplasms Outcome Parameter Estimation Pathway interactions Patients Persons Phenotype Phylogenetic Analysis Phylogeny Physicians Population Genetics Population Sizes Precancerous Conditions Process Prospective cohort Resources Risk Risk Assessment Selection for Treatments Signal Transduction Single Nucleotide Polymorphism Stress Surface System Testing Time Tissues Trees cancer risk cohort cost genome sequencing genome wide methylation high risk indexing mortality risk overtreatment patient subsets predictive modeling premalignant prevent random forest risk stratification standard of care stem cell population stem cells tool treatment response tumor tumor progression whole genome

项目摘要

Patients with Barrett's Esophagus are at increased risk of developing esophageal adenocarcinoma, but there is currently no reliable way to determine who is at high versus low risk. We propose to quantify the evolutionary dynamics in Barrett's and determine if those measures predict cancer risk. Neoplastic progression is fundamentally a stochastic evolutionary process at the cell level. However, there are often many different genetic and epigenetic alterations that can contribute to the development of a cancer, and the ones that evolve in any given cancer are random. This makes it difficult to assess the risk that a pre-cancerous tissue will evolve into cancer based simply on what mutations have occurred previously. We propose a different approach: Measure the dynamics of cell level evolution rather than the products of that evolution. We hypothesize that the rate of cellular evolution in a tissue should predict how likely and how quickly that tissue will evolve into a cancer. We will test this hypothesis in Barrett’s esophagus. We propose to fit both population genetic and phylogenetic models to data from Barrett’s esophagus biopsies spread over space and time in order to estimate parameters of the rate of cellular evolution and test if they are predictive of progression to esophageal adenocarcinoma. Five parameters determine the rate of evolution: 1) stem cell population size, 2) stem cell generation time, 3) mutation rate, 4) the strength of selection, and 5) heritability of the alterations. We have recently shown that we can measure all those parameters in clinical biopsies. We will measure all five parameters in Barrett’s esophagus based on CpG methylation and copy number alterations using methylation arrays, as well as single nucleotide variants identified in whole genome sequencing, in a prospective cohort of Barrett’s patients having either stable, benign disease or validated cancer outcomes. We will use those measures of the rate of evolution in Barrett’s esophagus to derive multivariable risk models of progression to esophageal adenocarcinoma as well as a classification system for the types of evolution present, that are clinically useful for the management of Barrett’s esophagus. If we are successful, we will reduce morbidity in patients likely to progress through earlier intervention, reduce suffering and stress in low-risk patients, and focus resources and interventions on those high-risk patients who will benefit from them. Our proof of principle would also justify using our tools to measure evolution in other pre-cancers to predict progression. In full-blown cancers, our measures may also predict their response to therapy and to select therapies that are matched to the evolutionary dynamics of a patient’s cancer.

巴雷特食管患者患食管腺癌的风险增加，但目前没有可靠的方法来确定谁处于高风险和低风险，我们建议量化进化。巴雷特氏症的动态并确定这些措施是否可以预测癌症进展。从根本上讲，这是细胞水平上的随机进化过程，但是，通常有许多不同的过程。可能导致癌症发展的遗传和表观遗传改变，以及那些进化的改变任何特定癌症的发生都是随机的，这使得评估癌前组织进化的风险变得困难。我们提出了一种不同的方法：测量细胞水平进化的动态而不是该进化的产物。组织中细胞进化的速率应该可以预测该组织进化成细胞的可能性和速度。我们将在巴雷特食管中测试这一假设，我们建议将其与群体遗传和癌症相结合。巴雷特食管活检数据的系统发育模型分布在空间和时间上，以便估计细胞进化速率的参数并测试它们是否可以预测进展为食管腺癌的进化速度由五个参数决定：1) 干细胞群大小，2) 干细胞世代时间，3）突变率，4）选择强度，以及5）改变的遗传力。最近表明我们可以在临床活检中测量所有这些参数我们将测量所有五个参数。基于 CpG 甲基化和使用甲基化的拷贝数改变的 Barrett 食管参数阵列，以及在全基因组测序中鉴定的单核苷酸变异，在一个前瞻性队列中我们将使用那些具有稳定、良性疾病或经过验证的癌症结果的巴雷特患者。测量巴雷特食管的进化速度，以得出进展为多变量风险模型食管腺癌以及目前进化类型的分类系统，即在临床上对于巴雷特食管的治疗很有用，如果我们成功的话，我们将降低发病率。患者可能通过早期干预取得进展，减少低风险患者的痛苦和压力，以及将资源和干预措施集中在那些将从中受益的高危患者身上。也证明使用我们的工具来测量其他癌症前期的进化来预测全面的进展是合理的。对于癌症，我们的措施还可以预测他们对治疗的反应，并选择与癌症相匹配的治疗方法患者癌症的进化动态。