Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada.
Adv Exp Med Biol. 2022;1361:199-213. doi: 10.1007/978-3-030-91836-1_11.
The growth of multi-omic tumour profile datasets along with knowledge of genome regulatory networks has created an unprecedented opportunity to advance precision oncology. Achieving this goal requires computational methods that can make sense of and combine heterogeneous data sources. Interpretability and integration of prior knowledge is of particular relevance for genomic models to minimize ungeneralizable models, promote rational treatment design, and make use of sparse genetic mutation data. While networks have long been used to capture genomic interactions at the levels of genes, proteins, and pathways, the use of networks in precision oncology is relatively new. In this chapter, I provide an introduction to network-based approaches used to integrate multi-modal data sources for patient stratification and patient classification. There is a particular emphasis on methods using patient similarity networks (PSNs) as part of the design. I separately discuss strategies for inferring driver mutations from individual patient mutation data. Finally, I discuss challenges and opportunities the field will need to overcome to achieve its full potential, with an outlook towards a clinic of the future.
随着多组学肿瘤特征数据集和基因组调控网络知识的增长，为推进精准肿瘤学提供了前所未有的机会。要实现这一目标，需要能够理解和整合异构数据源的计算方法。可解释性和先验知识的整合对于基因组模型尤为重要，可减少不可泛化的模型、促进合理的治疗设计，并利用稀疏的遗传突变数据。虽然网络长期以来一直被用于捕获基因、蛋白质和途径水平的基因组相互作用，但网络在精准肿瘤学中的应用相对较新。在本章中，我将介绍用于整合多模态数据源以进行患者分层和患者分类的基于网络的方法。特别强调使用患者相似性网络（PSN）作为设计的一部分的方法。我分别讨论了从单个患者突变数据推断驱动突变的策略。最后，我讨论了该领域需要克服的挑战和机遇，以充分发挥其潜力，并展望未来的临床实践。
