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Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport

Tong, A.; Kuchroo, M.; Gupta, S.; Venkat, A.; Perez San Juan, B.; Rangel, L.; Zhu, B.; Lock, J. G.; Chaffer, C.; Krishnaswamy, S.

2023-03-29 cancer biology
10.1101/2023.03.28.534644 bioRxiv
Show abstract

While single-cell technologies provide snapshots of tumor states, building continuous trajectories and uncovering causative gene regulatory networks remains a significant challenge. We present Cflows, an AI framework that combines neural ODE networks with Granger causality to infer continuous cell state transitions and gene regulatory interactions from static scRNA-seq data. In a new 5-time point dataset capturing tumorsphere development over 30 days, Cflows reconstructs two types of trajectories leading to tumorsphere formation or apoptosis. Trajectory-based cell-of-origin analysis delineated a novel cancer stem cell profile characterized by CD44hiEPCAM+CAV1+, and uncovered a cell cycle-dependent enrichment of tumorsphere-initiating potential in G2/M or S-phase cells. Cflows uncovers ESRRA as a crucial causal driver of the tumor-forming gene regulatory network. Indeed, ESRRA inhibition significantly reduces tumor growth and metastasis in vivo. Cflows offers a powerful framework for uncovering cellular transitions and dynamic regulatory networks from static single-cell data.

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