Personalization of graphical models using multi-omics data for subtype discovery and prognosis

摘要 最近癌症治疗的临床进展归因于靶向特定的基因，如ER-𝛼， HER2等。然而，相当大比例的患者对靶向治疗没有反应或发展 随着时间的推移产生抵抗力。这意味着目前的肿瘤特征和治疗干预方法 都不够准确。特别是，目前的疾病/患者亚型方法都在寻找差异 在单个基因水平上，忽略可能包含癌症关键特征的途径水平的相互作用 差距。这个项目的主要目标是开创一种新的疾病/患者分型方法， 与传统范式不同：在路径级别使用个性化的子类型化和特征化 途径图谱，而不是在基因水平。推动这项工作的假设是，一种新出现的情况 可以通过不同的基因和分子水平(例如，转录组， 表观基因组等。)但可能涉及相同的机制(S)。这是因为，虽然受影响的更改 不同患者的基因可能非常不同，涉及的途径可能是相同的。它的创新之处在于 工作是开发一种新的方法，能够通过以下方式计算单个患者的路径分布 有效地考虑了基因拓扑和路径串扰。这种方法的基本原理是有效的 整合多组学和多队列数据，以利用相互补充的信息 不同的数据类型，并解决与许多队列相关的小尺寸问题。这个项目的目标是 将通过四个具体目标来实现：1)确定单个患者的受影响路径，1)整合 突变、拷贝数变化、甲基化、微RNA和基因表达，2)整合多队列数据， 3)确定每个亚型的通路特征，以及4)验证所提出的通路水平亚型 利用公共数据以及来自两项临床研究的数据进行方法和相关风险预测 在UPMC希尔曼癌症中心。拟议工作的意义在于它有可能提供新的 更好的癌症管理和预后的方法和工具。从长远来看，个性化途径 分析将提高我们对疾病机制和对治疗的抵抗力的理解，使 开发个性化药物的新治疗方法。这些方法和工具将可用 通过一个开放访问的网络应用程序和一个cran R包。

SUMMARY Recent clinical advances in cancer treatments have been attributed to targeting specific genes such as ER-𝛼, HER2, etc. However, a significant percentage of patients do not respond to targeted therapies or develop resistance over time. This implies that current methods for tumor characterization and therapeutic interventions are not sufficiently accurate. In particular, current disease/patient subtyping approaches all look for differences at the level of individual genes, ignoring pathway-level interactions that can hold key characteristics of cancer disparities. The main goal of this project is to pioneer a new approach to disease/patient subtyping that departs from the traditional paradigm: subtyping and characterization at the pathway level, using personalized pathway profiles, rather than at the gene level. The hypothesis driving this work is that an emerging condition for an individual patient can be triggered through different genes and molecular levels (e.g., transcriptome, epigenome, etc.) but might involve the same mechanism(s). This is because, while alterations of impacted genes could be very diverse between patients the pathways involved could be the same. The innovation of this work is the development of a novel approach able to compute pathway profiles of individual patients by effectively taking into account gene topology and pathway crosstalk. Fundamental to this approach is effective integration of multi-omics and multi-cohort data to take advantage of complimentary information among different data types and address the small size problem associated with many cohorts. The goal of this project will be achieved through four specific aims: 1a) identify impacted pathways in individual patients, 1b) integrate mutation, copy number variation, methylation, microRNA, and gene expression, 2) integrate multi-cohort data, 3) identify pathway signatures for each subtype, and 4) validate the proposed pathway-level subtyping methodology and associated risk prediction by leveraging public data as well as data from two clinical studies at UPMC Hillman Cancer Center. The significance of the proposed work lies on its potential to provide new methods and tools for better cancer management and prognosis. In the longer term, personalized pathway analysis will improve our understanding of disease mechanisms and resistance to treatments, enabling the development of new treatments for personalized medicine. The methods and tools will be made available through an open-access web application and a CRAN R package.