Multiomic analysis of clonal development reveals new regulators of leukemic cell growth
Bibliographic record
Abstract
Mechanisms driving cell growth during clonal evolution in leukemia are not fully understood. We focused on epigenomic regulation of this process by analyzing the changes of chromatin marks and gene expression in independent leukemic clones evolving towards increased growth. The evolved subclones lost their growth differential ex vivo but restored it upon secondary transplantation, suggesting molecular memory of their evolutionary stage. Genome-wide, clonal evolution was associated with clone-specific gradual modulation of chromatin states and expression levels, with a surprising preferential trend of reversing the prior changes observed at the early leukemic stage. We leveraged clonal specificity of these modulation patterns to focus on the core gene set of potential growth regulators with consistent changes of expression and chromatin marks that were maintained in vivo and ex vivo in both independent clones. We selected three of these genes as candidates (Irx5 and Plag1 as growth suppressors and Smad1 as a driver) and validated their predicted growth effects by overexpression in leukemic subclones. AML patient data confirmed IRX5 and SMAD1 as markers of AML status in patients, suggesting that multiomic analysis of clonal evolution in a mouse model is a valuable predictive approach relevant to human AML.
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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 | 0.000 | 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 | 0.000 |
| 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".