Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.
Cell Rep Med
; 4(9): 101189, 2023 09 19.
Article
in En
| MEDLINE
| ID: mdl-37729872
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Carcinoma
/
Carcinoma, Renal Cell
/
Deep Learning
/
Kidney Neoplasms
Limits:
Humans
Language:
En
Journal:
Cell Rep Med
Year:
2023
Document type:
Article