Bayesian models for cancer prognosis by integrating diverse types of data

描述(申请人提供)：癌症预后的一个新策略是基于来自不同来源的综合信息，包括患者的传统临床和人口学信息，如年龄、分级和肿瘤大小等，以及最近出现的遗传信息，如基因或蛋白质标记物的表达。实施这样的策略需要有效的量化模型，将临床测量和遗传测量结合在一起来预测预后。这项应用的长期目标是改善癌症预防、诊断和预后方面的风险预测、治疗选择和亚型分类。短期目标是通过开发创新的统计模型来提高对癌症患者治疗反应的预测，该模型集成了三种不同类型的数据，包括两种信息学数据，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，即标准临床和人口学数据。我们将通过追求以下五个具体目标来实现这一应用的目标：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相结合。2)建立贝叶斯非参数模型，将多条遗传途径与蛋白质表达测量相结合。3)在前两个AIMS提出的贝叶斯模型的基础上，开发了贝叶斯分类程序。4)将临床和人口学测量整合到贝叶斯模型中，并使用包含500多名白血病患者的蛋白质表达测量和临床测量的综合数据集应用贝叶斯分类程序。5)通过进行生物实验来验证统计结果，这将由我们的合作生物学家完成。这项拟议的研究有望为肿瘤学家提供基于综合信息的定量预测工具。拟议研究的影响将是重大的，因为在这一应用中开发的模型可以应用于各种癌症类型，从而潜在地改善不同类型癌症患者的预后。 公共卫生相关性：整合蛋白质表达数据、蛋白质途径数据和临床数据有望显著改善医疗决策，如治疗选择。改进的决定预计将改善整体患者护理。例如，通过准确预测某些治疗对癌症患者无效，该患者将不再浪费时间尝试治疗，并将有更好的机会找到其他更有效的治疗方法。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Integrating the protein expression data, the protein pathway data, and the clinical data is expected to significantly improve medical decision making such as treatment selection. The improved decisions are expected to improve the overall patient care. For example, by accurately predicting that certain treatment will not be effective for a cancer patient, this patient will no longer waste time trying out the treatment and will have a better chance finding some other more effective therapies.