Meaningful and Faithful Computational, Multi-omics Based Simulations of Cancer Evolution for Validation and Prediction

The number of analysis methods and tools developed to hash interpretations out of "data" (e.g., bulk and single-cell sequencing data, or imaging data for spatial transcriptomics) being published each year is enormous. Each of these tools needs "validation", which is usually done by running extensive simulations of the scenarios the researchers have in front.

However, there is not a clear notion yet of a "digital twin" against which to run these validation tests (*). This is particularly true in the field of (solid) tumor evolution.

We are therefore interested in pursuing the goal of producing a multi-omics and AI integrated tumor digital-twin capable of recapitulating spatial growth of a solid tumor in a tissue while tracking its genotype at the single-cell level. For this we are leveraging our previous work on colon cancer development and the distribution of a prototype single-cell lineage-aware simulator called J-Space.

(*) Towards credible digital twins for basic and preclinical research, Colleen E. Clancy & Bennett A. Landman, Nature Reviews, 2026-01-08.