Abstract
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.
Abstract (translated)
DNA 甲基化是一种表观遗传机制，通过向 DNA 添加甲基基团来调控基因表达。异常的甲基化模式会干扰基因表达，并且与癌症发展有关联。为了量化 DNA 甲基化程度，通常使用专门的检测试剂。然而，这些试剂往往成本高昂，处理时间长，从而限制了它们在常规临床实践中的广泛应用。相比之下，大多数癌症患者的整个载玻片图像（WSIs）更容易获取。因此，在 WSIs 可用性较高的情况下，探索 WSIs 与 DNA 甲基化模式之间的潜在关系显得尤为必要。 为解决这一问题，我们提出了一种基于图神经网络的端到端弱监督学习框架来预测具有共同特征的不同样本基因组群的甲基化状态。使用来自癌症基因组图谱（TCGA）三个队列的数据 - TCGA-LGG（低级别胶质瘤脑癌）、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比当前最先进的方法在 AUROC 分数上提高了超过 20%。我们对基因组群进行了基因集富集分析，结果显示大多数基因组都在重要的标志物和路径中显著富集。我们还生成了空间富集热图以进一步调查组织学模式与 DNA 甲基化状态之间的联系。据我们所知，这是第一个使用弱监督深度学习探索多种癌症类型中空间解析组织学模式与基因群甲基化状态之间关联的研究。
URL
https://arxiv.org/abs/2504.05403
