TRACTION Platform, Division of Therapeutics Discovery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO Clin Cancer Inform. 2022 Jul;6:e2200040. doi: 10.1200/CCI.22.00040.
Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining.
We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project.
Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype.
The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.
生物测量技术的进步使人们能够在多个组学平台上对患者队列进行大规模研究。对这些数据进行整体分析可为转化研究提供可操作的见解，并需要新的方法来进行数据集成和挖掘。
我们提出了一种基于最近邻共享算法的跨平台数据集成新方法，并将其用于创建非小细胞肺癌免疫基因组分析项目的多平台数据网络。
基准测试表明，基于最近邻共享的网络方法在捕获已建立的相互作用方面优于传统的基因-基因网络，同时基于不同平台的测量值之间的相互作用提供了新的相互作用。当用于检查感兴趣的患者特征时，我们的方法提供了与复发和 TP53 癌基因型相关的特征以及新的线索。
所开发的网络提供了对非小细胞肺癌免疫基因组分析的前所未有的整体视图，可以通过我们构建的配套交互浏览器进行探索。
