Shi Zhiao, Lei Jonathan T, Elizarraras John M, Zhang Bing
Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Nat Cancer. 2025 Jan;6(1):205-222. doi: 10.1038/s43018-024-00869-z. Epub 2024 Dec 11.
Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.
大规模组学分析揭示了大量的体细胞突变和癌症相关蛋白，这对其功能解读提出了巨大挑战。在此，我们提出一种基于网络的方法，该方法以FunMap为核心，FunMap是一个泛癌功能网络，它利用监督机器学习，基于来自11种癌症类型的1194名个体的广泛蛋白质组学和RNA测序数据构建而成。FunMap包含10525个蛋白质编码基因，以前所未有的精度连接功能相关基因，超越了传统的蛋白质-蛋白质相互作用图谱。网络分析可识别功能性蛋白质模块，揭示与癌症特征和临床表型相关的层次结构，深入了解已确定的癌症驱动因素，并预测研究较少的癌症相关蛋白的功能。此外，将基于图神经网络的深度学习应用于FunMap可发现低突变频率的驱动因素。本研究将FunMap确立为一种强大且无偏见的工具，用于解释体细胞突变和研究较少的蛋白质，对推进癌症生物学和为治疗策略提供信息具有广泛意义。
