Transcriptomic Profiling Reveals Novel Candidate Genes and Signalling Programs in Breast Cancer Quiescence and Dormancy
Bibliographic record
Abstract
Metastatic recurrence, the major cause of breast cancer mortality, is driven by reactivation of dormant disseminated tumour cells that are defined by mitotic quiescence and chemoresistance. The molecular mechanisms underpinning mitotic quiescence in cancer are poorly understood, severely limiting the development of novel therapies for removal of residual, metastasis-initiating tumour cells. Here, we present a molecular portrait of the quiescent breast cancer cell transcriptome across the four main breast cancer sub-types (luminal, HER2-enriched, basal-like and claudin-low) and identify a novel quiescence-associated 22-gene signature using an established lipophilic-dye (Vybrant® DiD) retention model and whole-transcriptomic profiling (mRNA-Seq). Using functional association network analysis, we elucidate the molecular interactors of these signature genes. We then go on to demonstrate that our novel 22-gene signature strongly correlates with low tumoural proliferative activity, and with dormant disease and late metastatic recurrence (≥5 years after primary tumour diagnosis) in metastatic breast cancer in multiple clinical cohorts. These genes may govern the formation and persistence of disseminated tumour cell populations responsible for breast cancer recurrence, and therefore represent prospective novel candidates to inform future development of therapeutic strategies to target disseminated tumour cells in breast cancer, eliminate minimal residual disease and prevent metastatic recurrence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".