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