Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype
Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype
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DOI：
10.1109/tcbb.2023.3290394
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发表时间：
2023-09-01
影响因子：
4.5
通讯作者：
Kabuka, Mansur R.
中科院分区：
文献类型：
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作者：
Furtney, Isaac;Bradley, Ray;Kabuka, Mansur R.
乳腺癌是一种异质性疾病，由不同的基因组突变和临床特征组成。乳腺癌的分子亚型与预后和治疗选择密切相关。我们研究了使用深度图学习来收集来自多个诊断学科的患者因素，以更好地表示乳腺癌患者信息并预测分子亚型。我们的方法将乳腺癌患者数据建模为具有提取的特征嵌入的多关系有向图，以直接表示患者信息和诊断测试结果。我们开发了一个放射图像特征提取管道，以在DCE-MRI中产生乳腺癌肿瘤的矢量表示，并开发了一种基于自动编码器的基因组变体嵌入方法，将变体检测结果映射到低维潜在空间。我们利用相关域迁移学习来训练和评估关系图卷积网络，以预测个体乳腺癌患者图的分子亚型概率。我们的工作发现，利用来自多个多模态诊断学科的信息改善了模型的预测结果，并为乳腺癌患者产生了更独特的学习特征表示。这项研究展示了图神经网络和深度学习特征表示在乳腺癌领域执行多模态数据融合和表示的能力。
Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.