Revealing dynamic temporal regulatory networks driving cancer cell state plasticity with neural ODE-based optimal transport
Bibliographic record
Abstract
Abstract Despite advances in single-cell technologies and temporal sampling, the seamless connection of cell states over time and the inference of gene-gene relationships driving cancer cell state remains a formidable challenge. We present Cflows, a framework that combines neural ordinary differential equation network designed to unravel continuous cellular dynamics from timelapsed scRNAseq data with causality analysis to learn gene regulatory networks underlying cell state changes. When applied to a novel scRNAseq data from breast cancer in vitro models, we trace cell states back to their origins, allowing us to refine a marker profile for cancer stem cells (CSCs), and compute rich and complex gene-gene networks that drive pathogenic trajectories forward. Our comprehensive temporal regulatory networks reveal the dynamic transitions along the epithelial-to-mesenchymal (EMT) and the mesenchymal-to-epithelial (MET) trajectories. Newly, we identify estrogen-related receptor alpha (ESRRA) as a critical mediator of CSC plasticity and MET cell fate decisions. We extend Cflows to an in vivo xenograft model, demonstrating its potential for elucidating trajectories governing primary tumor metastasis to the lung. In summary, Cflows is an innovative algorithm that uncovers temporal molecular programs within dynamic cell systems from static single-cell data and has helped identify key drivers and transcriptional networks underlying cancer plasticity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".