基于空间转录组学数据和深度学习的乳腺癌组织病理图像基因表达预测
Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning
AI总结：
本文提出BrST-Net深度学习框架，利用空间转录组学数据从乳腺癌组织病理图像预测基因表达，通过引入辅助网络提升泛化能力，在250个基因中识别出237个正相关基因，显著优于以往方法。
摘要：
乳腺癌中的肿瘤异质性给预测预后和治疗反应带来了挑战。空间转录组学技术可能应对这些挑战，因为它们在细胞水平上提供了丰富的基因表达信息，但它们价格昂贵，阻碍了其在大规模临床肿瘤学研究中的应用。从苏木精-伊红染色的组织学图像预测基因表达为此类研究提供了一种更经济实惠的替代方案。在此，我们提出BrST-Net，这是一个利用空间转录组学数据从组织病理图像预测基因表达的深度学习框架。利用该框架，我们在不使用预训练权重的情况下训练并评估了10个最先进的深度学习模型，用于预测250个基因。为了增强主网络的泛化性能，我们在框架中引入了一个辅助网络。我们的方法优于以往的研究，识别出237个具有正相关的基因，其中包括24个中位相关系数大于0.50的基因。这比以往的研究有显著改进，以往的研究只能预测出102个具有正相关的基因，最高相关值范围为0.29至0.34。
英文摘要：
Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated 10 state-of-the-art deep learning models without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
