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Atlas of Computational Cell Reprogramming

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L1 · Network-informed inverse design heuristics P

CANDiT

Sinha S, Alcantara J, Perry K, Castillo V, Ondersma AK, Banerjee S, McLaren E, Espinoza CR, Taheri S, Vidales E, Tindle C, Adel A, Amirfakhri S, Sawires JR, Yang J, Bouvet M, Ghosh P

2025 · Cell reports. Medicine

Reactivating lineage commitment to differentiate, and hence eliminate, cancer stem cells (CSCs) remains a therapeutic challenge.

Abstract

From the original paper, Cell reports. Medicine · PubMed

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.

Summary

Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how CANDiT relates to the cross-modality inverse-design framework of the review.

Why this level

Level 1 because the method scores candidate interventions through static regulatory-network influence relative to a target GRN, without computing the post-intervention state P^S,u\hat P_{S,u}. Representation family is regulatory-network influence. Cited in §3.2 of the review. Editorial rationale pending review by the maintainer.

Classification

Level
L1
Representation
Regulatory-network influence
Modalities
P
Intervention
Transcription factors
Framework
Static network analysis

Software

Reproducibility
2 of 4
FAIR4RS
0 of 5

Last audited 2026-05-24

Citation

Sinha S et al. (2025). CANDiT: A machine learning framework for differentiation therapy in colorectal cancer., Cell reports. Medicine.

DOI: 10.1016/j.xcrm.2025.102421

PMID: 41118768

BibTeX
@article{candit2025,
  title  = {CANDiT: A machine learning framework for differentiation therapy in colorectal cancer.},
  author = {Sinha S et al.},
  year   = {2025},
  journal = {Cell reports. Medicine},
  pmid = {41118768},
  doi  = {10.1016/j.xcrm.2025.102421}
}