Abstract

Despite advances in artificial intelligence (AI) within cancer research, its application toward realizing differentiation therapy in solid tumors remains limited. Using colorectal cancer (CRC) as a model, we developed a machine learning (ML) framework, CANDiT (Cancer Associated Nodes for Differentiation Targeting), to selectively induce differentiation and death of cancer stem cells (CSCs)--a key obstacle to durable response. Centering on one node, CDX2, a master differentiation factor lost in high-risk, poorly differentiated CRCs, we built a transcriptomic network to identify therapeutic strategies for CDX2 restoration. Network-based prioritization identified PRKAB1, a stress polarity sensor, as a top target. A clinical-grade PRKAB1 agonist reprogrammed transcriptional networks, induced crypt differentiation, and selectively eliminated CDX2-low CSCs in CRC cell lines, xenografts and patient-derived organoids (PDOs). Multivariate analyses in PDOs revealed a strong therapeutic index, linking efficacy (IC) to the biomarker-defined CDX2-low state. A 50-gene response signature--derived from an integrated analyses of all three models and trained across multiple datasets--revealed that CDX2 restoration therapy may translate into a [~]50% reduction in recurrence and mortality risk. Mechanistically, treatment activated a differentiation-associated stress polarity signaling axis while dismantling Wnt and YAP-driven stemness programs essential to CSC survival. Thus, CANDiT offers a scalable path to CSC-directed therapy in solid tumors by translating transcriptomic vulnerabilities into precision treatments. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=174 SRC="FIGDIR/small/557628v4_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@16eeb1eorg.highwire.dtl.DTLVardef@10e308borg.highwire.dtl.DTLVardef@9512f0org.highwire.dtl.DTLVardef@10e74eb_HPS_FORMAT_FIGEXP M_FIG C_FIG One sentence summaryIn this work, Sinha et al. introduce a machine learning-guided framework to identify and target transcriptomic vulnerabilities in colorectal cancer, demonstrating that differentiation therapy selectively eliminates cancer stem cells and reduces recurrence risk. HighlightsO_LIAn ML framework (CANDiT) identifies target for differentiation therapy for CRCs C_LIO_LITherapy induces crypt differentiation and CSC-specific cytotoxicity C_LIO_LICDX2-low state predicts therapeutic response; restoration improves prognosis C_LIO_LITherapy dismantles stemness via reactivation of stress polarity signaling C_LI