Bayesian models for cancer prognosis by integrating diverse types of data

项目总结/摘要 癌症预后的一种新策略是根据来自不同来源的综合信息做出决定， 包括患者的传统临床和人口统计学信息，例如年龄、等级和肿瘤大小， 以及近年来出现的基因表达或蛋白质标记等遗传信息。执行 这种策略的实施需要有效的定量模型，该模型整合了临床测量和遗传分析。 测量结果进行预测。该应用程序的长期目标是改善风险预测， 治疗选择和癌症预防、诊断和预后中的亚型分类。短期 目的是通过开发创新的统计学方法， 模型集成了三种不同类型的数据，包括两种信息学数据亚型，即蛋白质 途径数据和高通量蛋白质表达数据，以及第三种类型，这是标准的临床和 人口统计数据。我们将通过以下五项具体措施来实现本申请的目标 目的：1）开发贝叶斯参数模型，将已知的遗传途径与高通量 蛋白质表达测量。2)开发集成多个遗传因子的贝叶斯非参数模型 与蛋白质表达测量途径。3)根据贝叶斯分类法， 在前两个目标中提出的贝叶斯模型。4)将临床和人口统计学测量整合到 贝叶斯模型和应用贝叶斯分类程序使用一个全面的数据集， 包含了500多名患者的蛋白质表达测量和临床测量， 白血病5)通过进行生物实验来验证统计结果，这些实验将由我们的 合作的生物学家拟议的研究有望为以下疾病提供定量预测工具： 肿瘤学家基于综合信息。拟议研究的影响将是重大的，因为 在本申请中开发的模型可以应用于各种癌症类型， 不同类型癌症患者的预后。

Project Summary/Abstract A new strategy in cancer prognosis is to base the decision on integrated information from different sources, including the traditional clinical and demographic information of patients, such as age, grade, and tumor size, etc, and the recently emerged genetic information like expression of gene or protein markers. Implementation of such a strategy requires efficient quantitative models that integrate the clinical measurements and genetic measurements together for prognosis. The long-range goal of this application is to improve risk predication, treatment selection, and subtype classification in cancer prevention, diagnosis, and prognosis. The short-term objective is to improve prediction of treatment response for cancer patients by developing innovative statistical models that integrate three different types of data, including two subtypes of informatics data, namely protein pathway data and high-throughput protein expression data, and a third type, which is the standard clinical and demographic data. We will accomplish the objective of this application by pursuing the following five specific aims: 1) Develop Bayesian parametric models that integrate a known genetic pathway with high-throughput protein expression measurements. 2) Develop Bayesian nonparametric model that integrate multiple genetic pathways with protein expression measurements. 3) Develop Bayesian classification procedures based on the Bayesian models proposed in previous two aims. 4) Integrate clinical and demographic measurements into the Bayesian models and apply the Bayesian classification procedures using a comprehensive data set that contains protein expression measurements and clinical measurements for more than 500 patients with leukemia. 5) Validate statistical findings by performing biological experiments, which will be done by our collaborating biologists. The proposed research is expected to provide quantitative prognostic tools for oncologists based on integrated information. The impact of the proposed research will be significant because models developed in this application can be applied to various cancer types and thus potentially improve the prognosis for patients with different types of cancer.