Integrating Genomic Data and Biological Knowledge to Learn Context-Specific Gene

描述（由申请人提供）：癌症是一种相当复杂的疾病。癌症起源于基因组中的各种各样的改变，这些改变通常导致细胞控制电路的急剧改变，从而在癌细胞中运作的分子机制中产生大量多样性。当试图从不同的人群中进行推断时，网络监管连接或操作规则的异质性模糊了关系，并迅速降低了准确识别监管互动的能力。这给那些试图确定在所有可见的变化中哪些变化是必然的人带来了相当大的挑战。然而，了解这些调控变化将有助于发现预后标志物，并提供强有力的候选药物靶点。目前的分析方法很少考虑这种复杂性和异质性。此外，目前的逆向工程调控机制的方法主要涉及一组几乎同质的组件，这些组件几乎同质地共同调控。本申请提出开发计算方法，该计算方法可以搜索异质样品集以鉴定样品子集，其中协作执行特定病理功能的基因集被均匀调节。然后，该方法利用具有较高同质性的样本的识别子集来学习上下文特定的调节机制。该方法将使用多种分子和临床表征的同时分析，以及在高异质性背景下检测有限的行为同质性的分析策略。这些方法将通过随后对NIH资助的三个项目（包括多发性骨髓瘤、胶质母细胞瘤和胰腺癌）的组合基因组数据、临床信息和可用生物学知识的分析进行验证。所开发的算法将作为一套具有图形用户界面的计算机软件来实施，这些软件将公开提供。 该项目旨在通过同时分析多种类型的生物医学数据来产生专门设计用于识别癌症中协作分子行为的分析类型，并以易于使用的软件形式提供给生物医学研究人员，以便任何拥有这些类型生物医学数据的人都可以使用该项目中开发的方法。即使是在预测哪些患者将从哪些治疗中受益的能力方面的适度改进也会显着改善患者护理。如果能认识到协同作用药物的组合，就可能产生更大的影响。

DESCRIPTION (provided by applicant): Cancer is a disease of considerable complexity. Cancer originates from and is supported by a wide variety of alterations in the genome that often lead to drastic alterations of the cell's control circuitry, producing a great deal of diversity in the molecular mechanisms operating in cancer cells. When attempting to make inferences from a diverse population, heterogeneity in either the network regulatory connections or operating rules blurs the relationships and rapidly reduces the ability to accurately discern regulatory interactions. This provides a considerable challenge to those attempting to determine which, of all the changes that can be seen, are consequential. However, knowledge of these regulatory changes will facilitate the discovery of prognostic markers and provide strong candidate drug targets. Current analytic methods rarely consider such complexity and heterogeneity. In addition, current methods to reverse-engineer regulatory mechanisms mostly concern a nearly homogeneous set of components that are nearly homogeneously co-regulated. This application proposes to develop computational methods that can search through heterogeneous sample sets to identify subsets of samples in which sets of genes that collaborate to carry out particular pathologic functions are homogeneously regulated. The method then utilizes identified subsets of samples with higher homogeneity to learn context-specific regulatory mechanisms. The method will use simultaneous analysis of a variety of molecular and clinical characterizations, and an analytic strategy that detects limited homogeneity of behavior against a background of high heterogeneity. The methods will be validated via subsequent analysis of combined genomic data, clinical information and available biological knowledge from three NIH funded projects which include multiple myeloma, glioblastoma and pancreatic cancers. Developed algorithms will be implemented as a set of computer software with graphical user-interface, which will be publicly available. This project is intended to produce types of analysis that are specifically designed to identify collaborative molecular behaviors in cancer via simultaneous analysis of multiple types of biomedical data, and to make them available to biomedical researchers, in the form of easily utilized software so that anyone with these types of biomedical data can use the methods developed in this project. Even a modest improvement in the ability to predict what patients would benefit from what treatments would significantly improve patient care. Understandings that would identify sets of synergistic drugs could have even higher impact.