Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning
Abstract
Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)–stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87–0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77–0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 03, 2022
- Source ID
- 10.1158/0008-5472.can-21-2318
Entities
People
- Alana Christie
- Alexander Parker
- Bradley C. Leibovich
- Dinesh Rakheja
- James Brugarolas
- Jay Jasti
- John Cheville
- Paul H. Acosta
- Payal Kapur
- Satwik Rajaram
- Vandana Panwar
- Vipul Jarmale
- Vitaly Margulis
Organizations
- Cancer Prevention and Research Institute of Texas
- Mayo Clinic
- Mayo Clinic School of Medicine
- National Institutes of Health
- United States Department of Defense
- University of Florida
- University of Texas Southwestern Medical Center
- University of Texas at Austin