TRIM28-dependent developmental heterogeneity determines cancer susceptibility through distinct epigenetic states
Bibliographic record
Abstract
Mutations in cancer risk genes increase susceptibility, but not all carriers develop cancer. Indeed, while DNA mutations are necessary drivers of cancer, only a small subset of mutated cells go on to cause the disease. To date, the mechanisms underlying individual cancer susceptibility remain unclear. Here, we took advantage of a unique mouse model of intrinsic developmental heterogeneity (Trim28+/D9) to investigate whether early-life epigenetic variation influences cancer susceptibility later in life. We found that heterozygosity of Trim28 is sufficient to generate two distinct early-life epigenetic states associated with differing cancer susceptibility. These developmentally primed states exhibit differential methylation patterns at typically silenced heterochromatin, detectable as early as 10 days of age. The differentially methylated loci are enriched for genes with known oncogenic potential, frequently mutated in human cancers and correlated with poor prognosis. This study provides genetic evidence that intrinsic developmental heterogeneity can prime individual, lifelong cancer susceptibility. Panzeri et al. use a Trim28+/D9 mouse model with intrinsic developmental heterogeneity to show that ‘heavy’ and ‘light’ developmental morphs exhibit different timing, type and severity of cancer, linked to a relevant DNA hypomethylation signature.
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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 | 0.001 |
| 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".