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

总结 癌症治疗的最新临床进展归因于靶向特定基因如ERβ， 然而，相当大比例的患者对靶向治疗没有反应或发展为 时间的阻力。这意味着目前用于肿瘤表征和治疗干预的方法 不够准确。特别是，目前的疾病/患者亚型方法都在寻找差异 在单个基因的水平上，忽略了可以保持癌症关键特征的通路水平的相互作用 差距。该项目的主要目标是开创一种疾病/患者亚型的新方法， 从传统的范式出发：在通路水平上进行分型和表征，使用个性化 而不是在基因水平上。推动这项工作的假设是， 可以通过不同的基因和分子水平触发（例如，转录组， 表观基因组等）但可能涉及相同的机制。这是因为，虽然变化的影响 患者之间的基因可能非常不同，所涉及的途径可能相同。的创新之处 工作是开发一种新的方法，能够计算个别患者的途径概况， 有效地考虑了基因拓扑和通路串扰。这种方法的根本是有效 整合多组学和多队列数据，以利用 不同的数据类型，并解决与许多队列相关的小规模问题。这个项目的目标 将通过四个具体目标实现：1a）确定个体患者中受影响的途径，1b）整合 突变、拷贝数变异、甲基化、microRNA和基因表达，2）整合多队列数据， 3)识别每个亚型的途径特征，以及4）验证所提出的途径水平亚型 方法和相关风险预测，利用公共数据以及两项临床研究的数据 在UPMC希尔曼癌症中心拟议工作的重要性在于其提供新的 用于更好的癌症管理和预后的方法和工具。从长远来看，个性化的途径 分析将提高我们对疾病机制和对治疗的抵抗力的理解， 开发个性化医疗的新疗法。将提供方法和工具， 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.