Training Cohort Slide Images for "Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning"

This item contains whole slide images (in SVS format) of all samples from the Training cohort analyzed in the paper "Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning" by Acosta et al in Cancer Research (https://doi.org/10.1158/0008-5472.CAN-21-2318). This work demonstrates that deep learning (DL) models can predict the intratumor heterogeneity in driver mutation status purely from Hematoxylin and Eosin (H&E) stained slides. Specifically, we trained and validated DL models that predict the status of three of the most frequently mutated driver genes (BAP1, PBRM1, and SETD2) in clear cell renal cell carcinoma. The DL models were trained on a large cohort of whole slide images (N=1282, referred to as WSI cohort in the paper/code) and tested on several independent cohorts including the TCGA KIRC (N=363 patients), two human tissue microarray (TMA) cohorts (referred to as TMA1 with 118 patients and TMA2 with 365 patients respectively) and a patient-derived xenograft TMA (referred to as PDX1). The current dataset contains the H&E stained whole slide images for the WSI cohort. See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5983795

本数据集收录Acosta团队发表于《癌症研究》（Cancer Research）的论文《基于深度学习解析肾癌驱动基因突变异质性的瘤内特征》（英文原题：Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning，DOI: 10.1158/0008-5472.CAN-21-2318）中分析的训练队列所有样本的全视野数字切片（whole slide image, WSI），文件格式为SVS。该研究证实，深度学习（deep learning, DL）模型仅依托苏木精-伊红（Hematoxylin and Eosin, H&E）染色切片，即可精准预测驱动基因突变状态下的肿瘤内异质性。具体而言，研究团队针对透明细胞肾细胞癌中三种最常见突变的驱动基因——BAP1、PBRM1与SETD2，训练并验证了对应的深度学习模型。上述深度学习模型以大规模全视野数字切片队列（共1282例，论文与代码中称其为WSI队列）开展训练，并在多个独立队列中完成测试：包括TCGA KIRC队列（363例患者）、两人源组织微阵列（tissue microarray, TMA）队列（分别为含118例患者的TMA1队列，以及含365例患者的TMA2队列），以及患者来源异种移植组织微阵列队列（简称PDX1）。本数据集收录上述WSI队列的H&E染色全视野数字切片。相关配套材料可参阅集合页面：https://doi.org/10.25452/figshare.plus.c.5983795