New Statistical Methods for Modelling Cancer Outcomes

癌症结果建模的新统计方法

• 批准号: 10542801
• 负责人: Yi Li
• 金额: $ 33.61万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10542801
• 关键词: Biological Markers; Boston; Cancer Model; Cancer Prognosis; Cations; Chronic Obstructive Pulmonary Disease; Clinical; Cohort Studies; Computer software; Cox Proportional Hazards Models; DNA; DNA Methylation; DNA Sequence Alteration; Dana-Farber Cancer Institute; Data; Data Reporting; Databases; Dependence; Disease Outcome; Enrollment; Ensure; Epidermal Growth Factor Receptor; Epigenetic Process; Gene Chips; Gene Expression; General Hospitals; Genes; Genetic; Genetic Variation; Goals; Heterogeneity; Image; Individual; Learning; Leukocytes; Linear Models; Link; Literature; Long-Term Survivors; Malignant Neoplasms; Malignant neoplasm of lung; Massachusetts; Medical; Methodology; Methods; Methylation; Modeling; Modification; Molecular; Mutate; Mutation; Oncogenic; Outcome; Pathology; Pathway interactions; Performance; Play; Procedures; Prognostic Marker; Proportional Hazards Models; Research Personnel; Risk Factors; Role; Sample Size; Serum; Site; Smoking; Solid; Statistical Methods; Statistical Models; Structure; Texture; Treatment outcome; Tumor Tissue; Uncertainty; Work; cancer risk; cancer survival; clinical risk; cohort; design; dietary; disorder prevention; feature detection; feature selection; gene network; high dimensionality; interest; kernel methods; learning strategy; molecular marker; mortality; mutational status; novel; novel marker; open source; precision medicine; prognostic value; quantitative imaging; radiomics; sex; sound; survival outcome; targeted treatment; transcriptome; transcriptome sequencing; treatment response; treatment strategy; tumor

PROJECT SUMMARY/ABSTRACT Lung cancer is one of the most common causes of mortality worldwide. Radiomic features have been shown to provide prognostic values in predicting lung cancer outcomes. Quantitative imaging features, often in dauntingly large numbers, are extracted from tumor regions. However, not all these extracted features are useful for tumor characterization, and feature selection is key for best performance. We plan to develop feasible statistical methods to select relevant features and conduct feature learning, i.e. discovery of representations needed for feature detection from the raw data. On the molecular level, expression and genetic variation of some known genes, such as KDM4 genes, have been linked to lung cancer prognosis, though little is known about epigenetic modifications' roles. Even fewer studies have investigated the impact of the interplay of DNA methylation and coexisting chronic obstructive pulmonary disease (COPD; a major clinical risk factor) on lung cancer risks. Statistically, drawing inference when the predictors (the clinical indicators and the methylation sites) outnumber the sample size in regression settings, e.g. generalized linear models, Cox proportional hazards models and censored quantile regression models, is very challenging. We plan to establish a new framework to draw inferences based on these complicated models. Growing evidence has suggested that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual DNA mutations and mechanism of lung cancer involves the interplay of the cellular heterogeneity, the myriad of dysfunctional molecular and genetic networks. We plan to develop new models to analyze those large scale network/pathway data and investigate how their dynamic network structures can be predicted based on DNA mutations. Leveraging the rich Boston Lung Cancer Survival Cohort database with 11,164 lung cancer cases, we expect that our new statistical methods will help identify novel biomarkers linked to lung cancer. Our promising preliminary results indicate the feasibility of the proposed work, which provides a solid radiomic and molecular basis for prediction of lung cancer outcomes. Core methods will be distributed in open-source, freely available software, naturally leading to implementable procedures for researchers and practitioners.

项目摘要/摘要 肺癌是全球死亡率最常见的原因之一。放射素特征已显示为 在预测肺癌预后提供预后价值。定量成像功能，通常在 艰巨的数量是从肿瘤区域提取的。但是，并非所有这些提取的功能都是 对于肿瘤表征有用，特征选择是最佳性能的关键。我们计划发展 可行的统计方法选择相关特征并进行特征学习，即发现 从原始数据中需要进行功能检测所需的表示。 在分子水平上，某些已知基因（例如KDM4基因）的表达和遗传变异具有 与肺癌预后有关，尽管对表观遗传修饰的作用知之甚少。更少 研究研究了DNA甲基化和共存慢性阻塞性的相互作用的影响 肺癌风险的肺疾病（COPD；主要的临床危险因素）。从统计上讲，绘制推理 当预测因子（临床指标和甲基化位点）的回归中的样本量超过样本量 设置，例如广义线性模型，COX比例危害模型和审查的分位数回归 模型，非常具有挑战性。我们计划建立一个新框架，以根据这些提出推论 复杂的模型。 越来越多的证据表明，可以通过突变或失调更好地理解癌症 途径或网络而不是单个DNA突变和肺癌机制涉及 细胞异质性的相互作用，无数功能障碍的分子和遗传网络。我们计划 开发新模型来分析这些大型网络/途径数据并研究其动态如何 可以根据DNA突变预测网络结构。 利用丰富的波士顿肺癌生存队列数据库具有11,164例肺癌病例，我们预计 我们的新统计方法将有助于识别与肺癌相关的新型生物标志物。我们有希望的 初步结果表明拟议工作的可行性，该工作提供了固体放射线和分子 预测肺癌预后的基础。核心方法将以开源，可自由使用的方式分发 软件，自然会为研究人员和从业人员提供可实施的程序。