SUPREME: multiomics data integration using graph convolutional networks.
SUPREME: multiomics data integration using graph convolutional networks.
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DOI：
10.1093/nargab/lqad063
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发表时间：
2023-06
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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作者：
为了为癌症精准医疗铺平道路，应该将具有相似生物学特征的患者分为相同的癌症亚型。利用高维多组学数据集，已经开发出综合方法来发现癌症亚型。最近，人们发现图神经网络可以利用图结构数据上的节点特征和关联来学习节点嵌入。已经开发了一些综合预测工具，利用这些进步在多个网络上使用，但存在一些局限性。为了解决这些限制，我们开发了SUPREME，这是一个节点分类框架，它在图结构数据上集成了多种数据模式。在乳腺癌亚型分型方面，与现有工具不同，SUPREME利用多组学特征从多个相似网络中生成患者嵌入，并将其与原始特征集成以捕获互补信号。在三个数据集的乳腺癌亚型预测任务上，SUPREME优于其他工具。supreme推断的亚型有显著的生存差异，大多数比基础真理更显著，并且优于其他九种方法。这些结果表明，通过适当的多组学数据利用，SUPREME可以揭开癌症亚型中未被发现的特征的神秘面纱，这些特征会导致显著的生存差异，并可以改善主要依赖于一种数据类型的基础真相标签。此外，为了显示SUPREME的模型无关性，我们将其应用于另外两个数据集，并取得了明显的优异表现。
To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals. On breast cancer subtype prediction tasks from three datasets, SUPREME outperformed other tools. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth, and outperformed nine other approaches. These results suggest that with proper multiomics data utilization, SUPREME could demystify undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve ground truth label, which depends mainly on one datatype. In addition, to show model-agnostic property of SUPREME, we applied it to two additional datasets and had a clear outperformance.
影响因子：
3.7
作者：
Baysan M;Bozdag S;Cam MC;Kotliarova S;Ahn S;Walling J;Killian JK;Stevenson H;Meltzer P;Fine HA
通讯作者：
Fine HA