Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression
Bibliographic record
Abstract
Molecular programs that underlie precursor progression in multiple myeloma are incompletely understood. Here, we report a disease spectrum-spanning, single-cell analysis of the Vκ*MYC myeloma mouse model. Using samples obtained from mice with serologically undetectable disease, we identify malignant cells as early as 30 weeks of age and show that these tumours contain subclonal copy number variations that persist throughout progression. We detect intratumoural heterogeneity driven by transcriptional variability during active disease and show that subclonal expression programs are enriched at different times throughout early disease. We then show how one subclonal program related to GCN2 stress response is progressively activated during progression in myeloma patients. Finally, we use chemical and genetic perturbation of GCN2 in vitro to support this pathway as a therapeutic target in myeloma. These findings therefore present a model of precursor progression in Vκ*MYC mice, nominate an adaptive mechanism important for myeloma survival, and highlight the need for single-cell analyses to understand the biological underpinnings of disease progression.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".