Induction of CDX2 as a differentiation therapy in colorectal cancers [xenograft]
Background: Lack of expression of the transcription factor CDX2 identifies a subset of poorly differentiated colorectal cancers (CRCs) that are associated with lower overall (OS) and disease-free (DFS) survival, independent of ethnicity, MSI status, or stage. CDX2 expression lowers risk of death and disease recurrence by ~50%. Induction of CDX2 as a therapeutic strategy is yet to be implemented in clinical practice. Methods: We used a novel network-based computational model to identify and prioritize therapeutic targets that can robustly augment CDX2 expression and, consequently, differentiation. The top candidate with available clinical-grade pharmacophore was tested for efficacy in 3 models: CRC lines in vitro, xenografts in mice, and in prospective cohorts of CRC patient-derived organoids (PDOs; n = 23). Results:  Our network-derived gene clusters reliably classified healthy and CRC tissues in diverse publicly available gene expression datasets (>10,000 human and >250 mouse samples). The target showed potent anti-cancer activity, induced crypt differentiation signatures in all models tested, and shifted the network in predictable ways. Effective pairing of therapy (IC50 of the target) and CDX2 expression (companion biomarker) was confirmed in an independent cohort of CRC PDOs. A multivariate analysis revealed KRAS mutation and high-CDX2 as key determinants of resistance to the target. Conclusions: Induction of CDX2 expression by the target augments differentiation and shows potent anti-cancer effect on CRCs that lack CDX2 expression. This benefit of drug-biomarker pairing should be considered for confirmatory studies in randomized clinical trials. Overall design: CRC xenograft nude mice model representing the colorectal cancer was used in  this study. Standard cell biological, functional and phenotypic assays were used to assess the prodifferantiation and anticancer actions of the PF compound : 1) prodifferantiation induction, 2) cytotoxicity through calculating the tumor volume