2. Academic Validation
  3. CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning

  • Adv Sci (Weinh). 2026 Jun 16:e76177. doi: 10.1002/advs.76177.
Chengming Zhang 1,  Zexi Chen 2,  Yuanxiang Miao 3,  Zuolin Shen 4 5 6 7,  Deyu Cai 3,  Shijie Tang 8,  Yun Xue 8,  Weifeng Guo 9 10,  Hongbin Ji 8,  Jian Liu 4 5 6 7,  Kazuyuki Aihara 1,  Luonan Chen 11
Affiliations

Affiliations

  • 1 International Research Center For Neurointelligence, The University of Tokyo Institutes For Advanced Study, The University of Tokyo, Tokyo, Japan.
  • 2 Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • 3 School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
  • 4 Centre for Infection Immunity and Cancer (IIC) of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, China.
  • 5 Edinburgh Medical School: Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK.
  • 6 Biomedical and Health Translational Research Center of Zhejiang Province, Haining, China.
  • 7 Department of Thoracic Oncology, Hangzhou Cancer Hospital, Hangzhou, China.
  • 8 Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center For Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.
  • 9 School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.
  • 10 State Key Laboratory of Intelligent Agricultural Power Equipment, Zhengzhou University, Luoyang, China.
  • 11 School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai, China.
PMID: 42301228 DOI: 10.1002/advs.76177
Abstract

Understanding and controlling cell-state and phenotype transitions is central to biological discovery and therapeutic development, yet identifying true causal regulators from observational transcriptomic data remains challenging because of confounding and correlated signals. CauFinder is a framework that integrates causal disentanglement modeling with network control to prioritize causal regulators of state and phenotype transitions from observed data. By leveraging causal reasoning based on do-calculus and optimizing information-flow metrics, CauFinder separates putative causal factors from spurious associations, quantifies transition-relevant states, and nominates master regulators. Across simulations and multiple real-world datasets, CauFinder identifies regulators associated with diverse transitions, including differentiation, adenocarcinoma-to-squamous transdifferentiation, and shifts between drug-sensitive and drug-resistant states. In epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) resistance, CauFinder prioritizes DAAM1 as a previously unrecognized driver. Small interfering RNA (siRNA)-mediated knockdown of DAAM1 enhances sensitivity to osimertinib, providing functional support for this causal prediction. Overall, CauFinder enables actionable target nomination and testable hypotheses for intervening in disease-relevant state transitions using observational transcriptomic data.

Keywords

causal inference; cell‐state transition; disentangled representation; drug resistance; network control.

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