학술논문

Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms' Tumors Using Unsupervised Machine Learning.
Document Type
Article
Source
International Journal of Molecular Sciences. Feb2023, Vol. 24 Issue 4, p3532. 14p.
Subject
*NEPHROBLASTOMA
*KIDNEY development
*KIDNEY tumors
*TUMOR classification
*HETEROGENEITY
Language
ISSN
1661-6596
Abstract
Wilms' tumors are pediatric malignancies that are thought to arise from faulty kidney development. They contain a wide range of poorly differentiated cell states resembling various distorted developmental stages of the fetal kidney, and as a result, differ between patients in a continuous manner that is not well understood. Here, we used three computational approaches to characterize this continuous heterogeneity in high-risk blastemal-type Wilms' tumors. Using Pareto task inference, we show that the tumors form a triangle-shaped continuum in latent space that is bounded by three tumor archetypes with "stromal", "blastemal", and "epithelial" characteristics, which resemble the un-induced mesenchyme, the cap mesenchyme, and early epithelial structures of the fetal kidney. By fitting a generative probabilistic "grade of membership" model, we show that each tumor can be represented as a unique mixture of three hidden "topics" with blastemal, stromal, and epithelial characteristics. Likewise, cellular deconvolution allows us to represent each tumor in the continuum as a unique combination of fetal kidney-like cell states. These results highlight the relationship between Wilms' tumors and kidney development, and we anticipate that they will pave the way for more quantitative strategies for tumor stratification and classification. [ABSTRACT FROM AUTHOR]