Identifying functional drivers of Hepatoblastoma outcomes via agent-based modeling and transcriptomics

Hepatoblastoma (HB) is the most common pediatric liver cancer and represents a major clinical challenge, due to the lack of effective therapies for advanced stages and disease relapse. In this work, we use the results of a previously HB-tailored agent-based model of the immune system to investigate whether model-derived variables can be of use in the prediction of patients' outcomes. To this aim, we apply factor analysis to the results of a simulated cohort of HB patients, to identify combinations of key immunological variables able to discriminate disease outcomes in the simulator, and we then assess the coherence of such predictions with independent results of differential expression and enrichment analyses on HB transcriptomics. Our analysis proposes that the ability of immune cells, particularly natural killer and CD8+ cytotoxic T cells, to recognize tumor-associated antigens and exert cytotoxic activity is essential for disease control following treatment.