Collaborative Research: New Bayesian Nonparametric Paradigms of Personalized Medicine for Lung Cancer

快速的技术进步使得单个肿瘤样本可以跨多个领域进行分子图谱分析，支持许多疾病的临床决策，特别是癌症。关键的挑战是有效地吸收这些领域的信息，以确定可能成为药物靶标的基因组特征和生物实体，为未来的患者开发准确的风险预测概况，并确定新的患者亚群，以便进行量身定制的治疗和监测。该项目的主要目标是开发一个创新的、灵活的和可扩展的统计框架，用于分析多领域、复杂结构和高通量的现代阵列和基于下一代测序的组学数据集。这项工作的动机是与肺癌相关的几项调查；然而，所提出的方法和计算工具广泛适用于涉及高维数据的各种环境。从更广泛的科学角度来看，将这些新方法应用于激励临床和基因组数据集将允许原则性的“结构搜索”。这将提供更准确的临床结果预测，更大的统计能力来检测重要的生物可操作生物标记物，以改进癌症诊断和预后的风险估计和治疗选择，并更好地利用生物领域知识来发现不同平台之间的关系。为了实现这些目标，我们提出了以下具体目标：(1)发展通用和灵活的统计技术，在混合的、不同规模的单域数据集中识别肺癌的差异基因组特征，这些数据来源于阵列和基于下一代测序的研究。基于合理的理论证明，将使用高效、可扩展的算法来开发和实现一类通用的非参数贝叶斯模型。这些模型提供了生物学上可解释的摘要，并使其能够适用于各种高通量数据集。(2)为海量的多域数据建立综合的概率框架，将域内和域间的相关性有机地结合在一起，以准确地检测肿瘤亚型和预测临床结果，从而提供与癌症分类相关的基因组异常目录。(3)培育大规模并行算法和高性能计算和推理工具，大大减少计算时间，提高高通量数据集的可扩展性。这些可扩展的推理过程能够吸收来自几个平台的信息，并选择具有适当依赖结构的灵活模型，同时检测用于预测和识别肿瘤亚型的最佳稀疏、非线性机制。由于这些公式是完全概率的，它们通过考虑不同的变异来源并提供推理不确定性的测量，比纯粹的算法方法提供了实质性的改进。由于现有的基于模拟的算法不适用于海量数据集，因此将利用这些模型的理论属性来设计数据挤压算法以进行有效的推理。此外，由于传统CPU受到能源消耗、发热量和内存访问的限制，将开发利用图形处理单元(GPU)等低成本大规模并行计算工具的能力的软件，并免费提供。

Rapid technological advances have allowed for molecular profiling across multiple domains from a single tumor sample, supporting clinical decision making in many diseases, especially cancer. Key challenges are to effectively assimilate information across these domains to identify genomic signatures and biological entities that may be targeted by drugs, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. The primary objective of this project is the development of an innovative, flexible and scalable statistical framework for analyzing multi-domain, complex-structured, and high throughput modern array and next generation sequencing-based 'omics datasets. The work is motivated by several investigations related to lung cancer; however, the proposed methods and computational tools are broadly applicable in a variety of contexts involving high-dimensional data. From a broader scientific perspective, the application of these novel methodologies to the motivating clinical and genomic datasets will allow for principled "structure hunting". This will provide more accurate prediction of clinical outcomes, greater statistical power to detect important biologically actionable biomarkers for improved risk estimation and treatment selection for cancer diagnosis and prognosis, and better utilization of biological domain knowledge to find relationships between different platforms. It will lead to subsequent implementation of rational biomarker-based and individualized clinical trials that increase the success rate of personalized therapies based on molecular markers.To achieve these goals, the following specific aims are proposed: (1) Develop versatile and flexible statistical techniques for identifying differential genomic signatures for lung cancer in mixed, heterogeneously scaled single-domain datasets arising from array and next-generation sequencing based studies. A general class of nonparametric Bayesian models based on sound theoretical justifications will be developed and implemented using efficient, scalable algorithms. These models provide biologically interpretable summaries and enable applicability to a wide variety of high-throughput datasets. (2) Formulate integrative probabilistic frameworks for massive multiple-domain data, which coherently incorporate dependence within and between domains to accurately detect tumor subtypes and predict clinical outcomes, thus providing a catalogue of genomic aberrations associated with cancer taxonomy. (3) Foster massively parallel algorithms and high-performance computational and inferential tools that drastically reduce the computation times and increase scalability of high-throughput datasets. These scalable inferential procedures are able to assimilate information from several platforms and select flexible models with the appropriate dependence structures, while detecting optimally sparse, non-linear mechanisms for predicting and identifying tumor subtypes. Because these formulations are fully probabilistic, they offer substantial improvements over purely algorithmic approaches by accounting for different sources of variation and providing measures of inference uncertainty. Since existing simulation-based algorithms do not scale for massive datasets, theoretical properties of these models will be exploited to devise data-squashing algorithms for efficient inference. Furthermore, as traditional CPUs are limited by energy consumption, heat generation and memory access, software that harnesses the power of low-cost massively parallel computing tools such as graphics processing units (GPUs) will be developed and made freely available.