Deep learning inference of universal dormancy pseudotime reveals the cellular targets of anti-cancer therapies
Bibliographic record
Abstract
Abstract Controlled exit from and re-entry into the cell cycle is essential for multi-cellular life, while aberrant quiescent and senescent cell states have been implicated in age-related diseases and cancer treatment evasion. Recent molecular and imaging studies suggest non-cycling cellular states exist along a continuum of deepening dormancy, whereby the probability of cell cycle re-entry decreases with distance from the restriction point. We trained a probabilistic deep-learning model that enables mapping of heterogeneous single cell transcriptomic datasets into an interpretable latent space that encodes a common “dormancy pseudotime”. We demonstrate that our model enables robust inference of active cell cycle states, and validate in diverse biological contexts that the inferred location along dormancy pseudotime represents a continuum from quiescence to durably arrested states. Applying dormancy pseudotime inference to pre- and post-treatment time points from patients undergoing anti-cancer treatment, we uncover new insights into the distinct tumour cell dormancy states targeted by immune checkpoint inhibitors and platinum-taxane chemotherapy. Given the ubiquity of single cell transcriptomics, we anticipate that dormancy pseudotime analysis will be widely applied to shed new light on the complex interplay between cycling and non-cycling cellular states in health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".