bioRxivCancer Biology
Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states
J. Nyman, T. Denize, Z. Bakouny, C. Labaki, B. M. Titchen, K. Bi, S. N. Hari, J. Rosenthal, N. Mehta, B. Jiang, B. Sharma, K. Felt, R. Umeton, D. A. Braun, S. Rodig, T. K. Choueiri, S. Signoretti, E. M. Van Allen
Abstract
Background Abstract Background Results Discussion Methods Data Availability Code Availability Author Contributions Competing Interests Extended Data References Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide and is comprised of several histological subtypes1. The clear cell histological subtype (ccRCC) is the most common form of RCC and accounts for the vast majority (75-80%) of metastatic cases1. In addition to highly recurrent mutations in hypoxia (VHL) and chromatin regulator genes (e.g. PBRM1, BAP1, SETD2), ccRCC exhibits extensive genomic intratumoral heterogeneity (ITH)2, which wa ...
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- approach: deep learning
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