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

dataset
posted on 07.06.2022, 05:34 authored by Satwik RajaramSatwik Rajaram, Paul Acosta, Vandana Panwar, Vipul Jarmale, Alana Christie, Jay Jasti, Vitaly Margulis, Dinesh Rakheja, John Cheville, Bradley C Leibovich, Alexander Parker, James Brugarolas, Payal Kapur

This item contains whole slide images (in SVS format) of the tissue microarray slides used as testing cohorts 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 whole slide images for the TMA cohorts (TMA1, TMA2 and PDX1). 


See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5983795

Funding

NIH (P50 CA196516) CPRIT (RP180192) CPRIT (RP180191) NIH (R01CA244579 , R01CA154475 , and R01DK115986) DOD ( W81XWH1910710 ) CPRIT ( RP200233 ). Lyda Hill Department of Bioinformatics

The University of Texas Southwestern Medical Center SPORE in Kidney Cancer

National Cancer Institute

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History

Research Institution(s)

University of Texas Southwestern Medical Center, Mayo Clinic

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