Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states
Jackson Nyman, Thomas Denize, Ziad Bakouny, Chris Labaki, Breanna M. Titchen, Kevin Bi, Surya N. Hari, Jacob Rosenthal, Nicita Mehta, Bowen Jiang, Bijaya Sharma, Kristen D. Felt, Renato Umeton, David A. Braun, Scott J. Rodig, Toni K. Choueiri, Sabina Signoretti, Eliezer M. Van Allen
Abstract
Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n = 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8 + lymphocytes and increased tumor-immune interactions. Our work reveals spatially interacting tumor-immune structures underlying ccRCC biology that may also inform selective response to ICI.