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

Yaron Trink, Achia Urbach, Benjamin Dekel, Peter Hohenstein, Jacob Goldberger, Tomer Kalisky

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1 Scopus citations


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.

Original languageEnglish
Article number3532
JournalInternational Journal of Molecular Sciences
Issue number4
StatePublished - Feb 2023

Bibliographical note

Funding Information:
Y.T. and T.K. were supported by the Israel Science Foundation (ICORE no. 1902/12 and Grants no. 1634/13 and 2017/13), the Israel Cancer Association (Grant no. 20150911), the Israel Ministry of Health (Grant no. 3-10146), the EU-FP7 (Marie Curie International Reintegration Grant no. 618592), the Data Science Institute at Bar-Ilan University, and the ICRF (Grant no. 19-101-PG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2023 by the authors.


  • Wilms’ tumors
  • cellular deconvolution
  • pareto task inference
  • topic modeling


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