一种连接组织学图像与DNA甲基化的新方法
A Novel Approach to Linking Histology Images with DNA Methylation
AI总结：
本文提出一种基于图神经网络的弱监督学习框架，利用全切片图像预测基因组DNA甲基化状态，在TCGA多个癌症队列上AUROC较现有方法提升超20%，并首次系统揭示了空间组织学模式与甲基化的关联。
摘要：
DNA甲基化是一种通过向DNA添加甲基基团来调控基因表达的表观遗传机制。异常的甲基化模式会扰乱基因表达，并已被证实与癌症的发生发展相关。为了定量检测DNA甲基化，通常需要使用专门的检测方法。然而，这些检测方法往往成本高昂且处理时间长，这限制了它们在常规临床实践中的广泛可及性。相比之下，大多数癌症患者的全切片图像（WSIs）更容易获得。因此，鉴于WSIs的易得性，迫切需要探索WSIs与DNA甲基化模式之间的潜在关系。为了解决这一问题，我们提出了一种端到端的基于图神经网络的弱监督学习框架，用于预测在样本间表现出连贯模式的基因组甲基化状态。利用来自癌症基因组图谱（TCGA）的三个队列数据——TCGA-LGG（脑低级别胶质瘤）、TCGA-GBM（多形性胶质母细胞瘤）（n=729）和TCGA-KIRC（肾透明细胞癌）（n=511）——我们证明了所提出的方法取得了显著高于最先进（SOTA）方法的AUROC分数，提升幅度超过20%。我们对这些基因组进行了基因集富集分析，结果表明大多数基因组在重要的标志性特征和通路中显著富集。我们还生成了空间富集热图，以进一步研究组织学模式与DNA甲基化状态之间的联系。据我们所知，这是第一项利用弱监督深度学习在多种癌症类型中探索空间分辨的组织学模式与基因组甲基化状态之间关联的研究。
英文摘要：
DNA methylation is an epigenetic mechanism that regulates gene expression by adding methyl groups to DNA. Abnormal methylation patterns can disrupt gene expression and have been linked to cancer development. To quantify DNA methylation, specialized assays are typically used. However, these assays are often costly and have lengthy processing times, which limits their widespread availability in routine clinical practice. In contrast, whole slide images (WSIs) for the majority of cancer patients can be more readily available. As such, given the ready availability of WSIs, there is a compelling need to explore the potential relationship between WSIs and DNA methylation patterns. To address this, we propose an end-to-end graph neural network based weakly supervised learning framework to predict the methylation state of gene groups exhibiting coherent patterns across samples. Using data from three cohorts from The Cancer Genome Atlas (TCGA) - TCGA-LGG (Brain Lower Grade Glioma), TCGA-GBM (Glioblastoma Multiforme) ($n$=729) and TCGA-KIRC (Kidney Renal Clear Cell Carcinoma) ($n$=511) - we demonstrate that the proposed approach achieves significantly higher AUROC scores than the state-of-the-art (SOTA) methods, by more than $20\%$. We conduct gene set enrichment analyses on the gene groups and show that majority of the gene groups are significantly enriched in important hallmarks and pathways. We also generate spatially enriched heatmaps to further investigate links between histological patterns and DNA methylation states. To the best of our knowledge, this is the first study that explores association of spatially resolved histological patterns with gene group methylation states across multiple cancer types using weakly supervised deep learning.
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