Statistical methods for cancer progression delineation and subtype identification

癌症进展描述和亚型识别的统计方法

• 批准号: 10368994
• 负责人: Chi Wang
• 金额: $ 7.49万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10368994
• 关键词: Adenocarcinoma Cell; Advocate; Affect; Algorithms; Appalachian Region; Basic Science; Biological; Cancer Cluster; Cancer Patient; Cells; Clinical; Clinical Research; Colon Carcinoma; Complex; Computing Methodologies; DNA sequencing; Data; Development; Disease; Future; Genes; Genomics; High-Throughput DNA Sequencing; Knowledge; Lung Adenocarcinoma; MAP Kinase Gene; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Modeling; Molecular; Mutation; Outcome; Pathway interactions; Patients; Phylogenetic Analysis; Population; Process; Prognosis; Publications; Research; Resolution; Sample Size; Somatic Mutation; Squamous cell carcinoma; Statistical Methods; Structure; The Cancer Genome Atlas; Transforming Growth Factor beta; Trees; WNT Signaling Pathway; anticancer research; base; cancer classification; cancer subtypes; cancer type; carcinogenesis; clinical practice; cohort; design; drug development; genomic data; improved; individual patient; insight; machine learning model; molecular subtypes; new therapeutic target; p53 Signaling Pathway; personalized medicine; single cell sequencing; statistical and machine learning; success; tool; treatment response; treatment strategy; tumor; tumor heterogeneity; tumor progression; tumorigenesis

Project Summary Carcinogenesis is a complex process involving somatic mutations in a number of key biological pathways and processes. Full study of the temporal order of somatic mutation occurrences is very important to understand biological mechanisms of cancer development and to inform new therapeutic targets and treatment options. The first and most recognized example of order of mutations is from colon cancer, which is frequently initiated by mutations that affect the Wnt signaling pathway, and then progress upon subsequent mutations in genes involved in MAPK, PI3K, TGF-beta, and p53 signaling pathways. However, for many other cancer types, temporal orders of mutations are still largely unknown. Somatic mutation profiling via high throughput DNA sequencing has provided an unprecedented opportunity for using statistical/computational methods to study cancer progression. We and others have developed methods to infer temporal order of somatic mutations based on combining mutation profile data from a cohort of patients. However, one major limitation of current methods is that they only consider presence or absence of mutations in a patient’s tumor, but do not take into account intra-tumoral heterogeneity (ITH). The ITH refers to the presence of multiple cell populations, i.e. subclones, with distinct mutation profiles within a patient’s tumor. The ITH, which can be inferred from either single-/multi-region bulk sequencing or single cell sequencing, is usually characterized by a phylogenetic tree with nodes in the tree indicating different subclones and edges indicating the evolutionary relationships of subclones. As a phylogenetic tree describes the temporal order of mutations within an individual patient’s tumor, incorporating such in-depth intra-patient information into the tumor progression analysis across patients is likely to substantially increase the power and accuracy of the analysis. Another important priority in cancer research is to identify molecular subtypes. As cancer is a complex disease, patients of the same cancer type may have very different prognoses and responses to therapy. Further classifying patients into subtypes allows clinicians to better predict a patient’s clinical outcomes and design more personalized treatment strategies. By harnessing omics profiling data, statistical/machine learning has emerged as a powerful tool to identify molecular cancer subtypes. However, due to the high complexity of cancer omics data and limited sample size, it is still challenging to obtain stable and biologically interpretable results. Recently, it has been advocated that incorporating biological knowledge and structure into the construction of statistical/machine learning models is a viable approach to improve the mechanistic interpretability and robustness of the models. To advance current capabilities, we propose to develop new statistical methods to better estimate the temporal order of pathway mutations by integrating ITH, pathway and mutational functional annotation information, and thereby, to classify patients into biologically meaningful subtypes.

项目摘要 癌变是一个复杂的过程，涉及许多关键生物学途径的体细胞突变 和过程。全面研究体细胞突变发生的时间顺序对于 了解癌症发展的生物学机制，并为新的治疗靶点和治疗提供信息 选项.第一个也是最公认的突变顺序的例子来自结肠癌，结肠癌通常是 由影响Wnt信号通路的突变启动，然后在随后的突变中进展。 参与MAPK、PI 3 K、TGF-β和p53信号通路的基因。然而，对于许多其他癌症类型， 突变的时间顺序在很大程度上仍然是未知的。通过高通量DNA进行体细胞突变分析 测序为使用统计/计算方法来研究提供了前所未有的机会 癌症进展我们和其他人已经开发出推断体细胞突变时间顺序的方法 基于组合来自一组患者的突变谱数据。然而，目前的一个主要限制是， 方法的一个重要特点是，他们只考虑患者肿瘤中突变的存在或不存在，而不考虑 考虑肿瘤内异质性（ITH）。ITH是指存在多个细胞群体，即 亚克隆，在患者的肿瘤内具有不同的突变谱。ITH，可以从以下两种情况中推断出来： 单/多区域批量测序或单细胞测序通常以系统发生树为特征 树中的节点表示不同的亚克隆，边表示 亚克隆系统发生树描述了个体患者的基因组中突变的时间顺序， 肿瘤，将这种深入的患者内信息纳入患者间的肿瘤进展分析 很可能会大大提高分析的能力和准确性。癌症治疗的另一个重点 研究的目的是确定分子亚型。由于癌症是一种复杂的疾病， 可能会有非常不同的症状和对治疗的反应进一步将患者分类为亚型， 临床医生可以更好地预测患者的临床结果，并设计更个性化的治疗策略。通过 利用组学分析数据，统计/机器学习已经成为一种强大的工具， 分子癌症亚型。然而，由于癌症组学数据的高度复杂性和有限的样本量， 获得稳定的和生物学上可解释的结果仍然具有挑战性。最近，有人主张， 将生物学知识和结构纳入统计/机器学习模型的构建中， 一个可行的方法，以提高机械的可解释性和鲁棒性的模型。推进 目前的能力，我们建议开发新的统计方法，以更好地估计时间顺序 通过整合ITH、途径和突变功能注释信息， 把病人分成有生物学意义的亚型