CANDiT: A machine learning framework for differentiation therapy in colorectal cancer.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41118768.
- Also identified by DOI 10.1016/j.xcrm.2025.102421 and PMC identifier 12711679.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Reactivating lineage commitment to differentiate, and hence eliminate, cancer stem cells (CSCs) remains a therapeutic challenge. Here, we present CANDiT (cancer-associated nodes for differentiation targeting), a machine learning framework that identifies transcriptomic vulnerabilities for differentiation therapy in colorectal cancer (CRC). Centering on CDX2-a master intestinal lineage factor lost in high-risk, poorly differentiated CRCs-we identify PRKAB1, a stress polarity sensor, as a top therapeutic target. A clinical-grade PRKAB1 agonist reactivates lineage programs, dismantles Wnt/YAP-driven stemness, and selectively eliminates CDX2-low CSCs across CRC cell lines, xenografts, and patient-derived organoids (PDOs). Multivariate analysis reveals a strong therapeutic index tied to the CDX2-low state. A 50-gene response signature, derived from integrated modeling across all platforms, predicts ∼50% reduction in recurrence and mortality risk. Like immunotherapy, CANDiT resurrects a physiologic program-differentiation-to selectively eliminate CSCs, offering a scalable, precision framework for lineage restoration in solid tumors.
Medical subject headings
- Colorectal Neoplasms
- Machine Learning
- Cell Differentiation