Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms' Tumors Using Unsupervised Machine Learning

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.

Automated summary

Wilms' tumors are pediatric malignancies that arise from faulty kidney development. Computational approaches were used to characterize the continuous heterogeneity in high-risk blastemal-type Wilms' tumors, revealing a triangle-shaped continuum in latent space bounded by three tumor archetypes with different characteristics resembling developmental stages of the fetal kidney. These findings provide insights into the relationship between Wilms' tumors and kidney development, and have the potential to improve tumor stratification and classification strategies.