DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcinogenesis has been extensively studied. High-throughput DNA methylation assays have been used broadly in cancer research. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis. In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. We demonstrate that classical machine learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole slide images. Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Our results provide new insights into the link between histopathological and molecular data.

DNA甲基化是调节基因表达的重要表观遗传机制，其在癌发生中的作用已被广泛研究。高通量 DNA 甲基化检测已广泛应用于癌症研究。鉴于组织取样仍然是诊断的临床金标准，组织病理学图像通常在癌症治疗中获得。在这项工作中，我们研究了癌症组织病理学图像和 DNA 甲基化谱之间的相互作用，以在表观遗传学水平上更好地理解肿瘤病理学。我们证明经典的机器学习算法可以将癌症样本的 DNA 甲基化谱与从整个幻灯片图像中提取的形态特征相关联。此外，将基因分组为甲基化簇大大提高了模型的性能。良好预测的基因富含致癌作用的关键途径，包括神经胶质瘤的缺氧和肾细胞癌的血管生成。我们的结果为组织病理学和分子数据之间的联系提供了新的见解。