Bayesian methods for complex, high-dimensional functional data in cancer research

 描述(申请人提供)：复杂的高维数据，如多平台基因组学和成像数据，可用于发现生物标记物，为癌症病因、自然病史、预后和治疗反应预测提供洞察。然而，现有的分析方法是不够的，因为大多数方法要么忽略了数据中的重要结构，要么将分析局限于没有使用数据中所有信息的简单总结。这项研究将开发一套灵活的、自动化的、新颖的贝叶斯方法，用于对复杂的高维功能数据进行回归分析，以使用解释其复杂结构的模型来发现生物标记物，产生调整多重测试的推断，并可扩展到高维设置。虽然这些方法普遍适用，但这些方法将在我们的合作者进行的两项研究的背景下开发，这两项研究旨在发现膀胱癌自然历史中的早期基因组和表观遗传学事件，以及与戒烟成功相关和预测戒烟成功的神经成像生物标志物。具体目标1：将多平台基因组数据建模为函数，我们将在由我们的合作伙伴Czerniak开发的新的膀胱癌模型生成的晶格上开发空间相关基因组数据的功能响应回归方法。我们将应用这些方法来识别膀胱癌的基因组和表观遗传学变化，并确定在疾病的自然历史中首次观察到的时间，揭示作为潜在疾病驱动因素的早期异常。我们将开发推理策略来执行基因组水平的测试，然后在调整多样性的同时确定基因组区域。具体目标2：我们将开发事件相关电位(ERPs)的函数回归方法，以测试不同的情绪刺激是否会引起不同的神经反应，确定成功戒烟和不成功戒烟的个体之间这些影响是否不同，并评估ERPs是否是成功戒烟的独立预测因素。我们的方法将通过空间函数过程或张量基函数灵活地捕获电极间相关性，并捕获电极内相关性 使用基函数建模，并使用策略来确定哪种基础最适合事件相关事件。具体目标3：我们将为我们的戒烟试验中的功能磁共振数据开发功能回归方法，首先在受试者水平上识别不同视觉刺激激活的大脑区域，然后引入一种策略，将我们的方法扩大到群体水平的分析，以表征人群水平的神经差异，将它们与戒烟成功联系起来，并评估它们相对于事件相关电位和标准人口统计学、心理测量学和遗传预测因子的预测能力。我们的纵向相关体积数据模型将通过基本函数建模来捕获体积内相关性，引入了一种新颖的混合基本函数建模策略，该策略以一种考虑已知解剖结构、空间邻近和功能连接引起的远距离相关性的方式捕获脑内相关性。具体目标4：我们将把这些新方法集成到一套通用的贝叶斯方法中，用于复杂、高维函数的空间和纵向相关的功能反应回归、判别和推理，以及可广泛应用的免费、自动化、可扩展的软件。

 DESCRIPTION (provided by applicant): Complex, high-dimensional data like multi-platform genomics and imaging data can be used to discover biomarkers providing insight into cancer etiology, natural history, prognosis, and prediction of response to therapy. Existing analytical methods are not adequate, however, as most either ignore important structure in the data or limit analysis to simple summaries that do not use all of the information in the data. This research will develop a general suite of flexible, automated, novel Bayesian methods for performing regression analyses on complex, high dimensional functional data to discover biomarkers using models that account for their intricate structure, yield inference that adjusts fo multiple testing, and are scalable to high-dimensional settings. While generally applicable, these methods will be developed in the context of two studies conducted by our collaborators to discover early genomic and epigenetic events in the natural history of bladder cancer and neuroimaging biomarkers associated with and predicting smoking cessation success. Specific Aim 1: Modeling multi-platform genomic data as functions, we will develop methods for functional response regression for spatially correlated genomics data on a lattice generated by a novel bladder cancer model developed by our co-I Czerniak. We will apply these methods to identify genomic and epigenetic changes in bladder cancer and determine when first observed in the disease's natural history, revealing early aberrations that are potential disease drivers. We will develop inferential strategies to perform genome-level tests and then ag genomic regions while adjusting for multiplicity. Specific Aim 2: We will develop functional regression approaches for event-related potentials (ERPs) from a randomized smoking cessation trial conducted by our co-Is Cinciripini and Versace to test whether different emotional stimuli evoke differential neurological response, determine whether these effects vary between individuals successful or unsuccessful in their smoking cessation attempt, and assess whether ERPs are independent predictors of success. Our methods will flexibly capture inter-electrode correlation via spatial functional processes or tensor basis functions, and capture intra-electrode correlation using basis function modeling, with strategies to determine which basis is best for ERPs. Specific Aim 3: We will develop functional regression approaches for fMRI data from our smoking cessation trial, first at the subject level to identify brain regions differentially activaed by different visual stimuli, and then introducing a strategy to scale our approach up to group-level analyses to characterize population-level neurological differences, relate them to cessation success, and assess their predictive ability relative to ERP and standard demographic, psychometric, and genetic predictors. Our models for longitudinally correlated volumetric data will capture intra-volume correlation through basis functional modeling, introducing a novel hybrid basis function modeling strategy that captures within-brain correlation in a manner that accounts for known anatomy, spatial proximity, and distant correlations induced by functional connectivity. Specific Aim 4: We will integrate these new methods into a general suite of Bayesian methods for spatially and longitudinally correlated functional response regression, discrimination, and inference for complex, high-dimensional functions along with freely available, automated, scalable software that can be broadly applied.