Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach
Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach
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DOI：
10.1109/jbhi.2023.3249354
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发表时间：
2023-02
影响因子：
7.7
通讯作者：
Xiangyu Liu;Zhenyu Liu;Ye Yan;Kai Wang;Aodi Wang;Xiongjun Ye;Liwei Wang;Wei Wei-Wei;Baowei Li;Caixia Sun;Wei He;Xuehua Zhu;Zenan Liu;Jiangang Liu;Jian Lu;Jie Tian
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文献类型：
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作者：
Xiangyu Liu;Zhenyu Liu;Ye Yan;Kai Wang;Aodi Wang;Xiongjun Ye;Liwei Wang;Wei Wei-Wei;Baowei Li;Caixia Sun;Wei He;Xuehua Zhu;Zenan Liu;Jiangang Liu;Jian Lu;Jie Tian
计算病理学的快速发展为利用组织病理学图像进行预后预测带来了新的机遇。然而，现有的深度学习框架缺乏对图像和其他预后信息之间关系的探索，导致可解释性差。肿瘤突变负荷（TMB）是一种很有前途的预测癌症患者生存结局的生物标志物，但其测量成本高。其异质性可反映在组织病理学图像中。在这里，我们报告了一个两步框架，使用全载玻片图像（WSIs）的预后预测。首先，该框架采用深度残差网络对WSI的表型进行编码，并通过聚合和降维后的深度特征对患者级TMB进行分类。然后，通过在分类模型开发期间获得的TMB相关信息对患者的预后进行分层。深度学习特征提取和TMB分类模型构建是在透明细胞肾细胞癌（ccRCC）的295个苏木精和伊红染色的WSI的内部数据集上进行的。预后生物标志物的开发和评价在具有304个WSI的癌症基因组膀胱-肾ccRCC（TCGA-KIRC）项目上进行。我们的框架实现了良好的TMB分类性能，在验证集上的受试者工作特征曲线（AUC）下的面积为0.813。通过生存分析，我们提出的预后生物标志物可以实现患者总生存期的显著分层（P <0.05），并在晚期疾病患者的风险分层中优于原始TMB签名。结果表明，从WSI中挖掘TMB相关信息，实现逐步预后预测的可行性。
The rapid development of computational pathology has brought new opportunities for prognosis prediction using histopathological images. However, the existing deep learning frameworks lack exploration of the relationship between images and other prognostic information, resulting in poor interpretability. Tumor mutation burden (TMB) is a promising biomarker for predicting the survival outcomes of cancer patients, but its measurement is costly. Its heterogeneity may be reflected in histopathological images. Here, we report a two-step framework for prognostic prediction using whole-slide images (WSIs). First, the framework adopts a deep residual network to encode the phenotype of WSIs and classifies patient-level TMB by the deep features after aggregation and dimensionality reduction. Then, the patients' prognosis is stratified by the TMB-related information obtained during the classification model development. Deep learning feature extraction and TMB classification model construction are performed on an in-house dataset of 295 Haematoxylin & Eosin stained WSIs of clear cell renal cell carcinoma (ccRCC). The development and evaluation of prognostic biomarkers are performed on The Cancer Genome Atlas-Kidney ccRCC (TCGA-KIRC) project with 304 WSIs. Our framework achieves good performance for TMB classification with an area under the receiver operating characteristic curve (AUC) of 0.813 on the validation set. Through survival analysis, our proposed prognostic biomarkers can achieve significant stratification of patients' overall survival (P $< $ 0.05) and outperform the original TMB signature in risk stratification of patients with advanced disease. The results indicate the feasibility of mining TMB-related information from WSI to achieve stepwise prognosis prediction.