New Statistical Methods for Modelling Cancer Outcomes

项目概要/摘要 肺癌是全世界最常见的死亡原因之一。放射组学特征已被证明 为预测肺癌结果提供预后价值。定量成像特征，通常在 从肿瘤区域提取的数量惊人。然而，并非所有这些提取的特征都是 对于肿瘤表征很有用，特征选择是获得最佳性能的关键。我们计划开发 可行的统计方法来选择相关特征并进行特征学习，即发现 从原始数据中进行特征检测所需的表示。 在分子水平上，一些已知基因（例如KDM4基因）的表达和遗传变异已经 尽管人们对表观遗传修饰的作用知之甚少，但它与肺癌预后有关。更少 研究调查了 DNA 甲基化与共存慢性阻塞性肺疾病之间相互作用的影响 肺部疾病（COPD；主要临床危险因素）对肺癌风险的影响。统计、推断 当预测因子（临床指标和甲基化位点）数量超过回归中的样本量时 设置，例如广义线性模型、Cox 比例风险模型和删失分位数回归 模型，非常具有挑战性。我们计划建立一个新的框架来根据这些进行推断 复杂的模型。 越来越多的证据表明，可以通过突变或失调来更好地了解癌症 通路或网络而不是个体 DNA 突变和肺癌的机制涉及 细胞异质性、无数功能失调的分子和遗传网络的相互作用。我们计划 开发新模型来分析这些大规模网络/路径数据并研究它们的动态如何 网络结构可以根据DNA突变来预测。 利用包含 11,164 例肺癌病例的丰富的波士顿肺癌生存队列数据库，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。我们的承诺 初步结果表明所提议工作的可行性，为放射组学和分子学研究提供了坚实的基础 为预测肺癌结果奠定了基础。核心方法将开源分发，免费提供 软件，自然会为研究人员和从业者带来可实施的程序。

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.