Epithelial Cell Organization Suppresses Myc Function by Attenuating Myc Expression
Bibliographic record
Abstract
c-Myc is an oncogene transcription factor that causes cancer in many settings, including solid tumors that arise in the context of organized tissue structures. Given that disruption of tissue architecture frequently occurs in cancer, there is considerable interest in how cell organization impacts oncogene function. A previous report found that organization of mammary epithelial cells into defined 3-dimensional structures renders them insensitive to the effects of retrovirus-mediated overexpression of Myc, leading to the notion that organization tempers the sensitivity of individual cells to Myc activity. In this article, we report that epithelial cell organization does not profoundly alter Myc activity but, instead, suppresses Myc by modulating its expression. We show that the morphogenesis of mammary epithelial cells into organized acinar structures in vitro is accompanied by widespread changes in gene expression patterns, including a substantial decrease in the expression of Myc. Concomitant with the decrease in endogenous Myc expression, we observe a decrease in transcription from retroviral vectors during morphogenesis and find that Myc transgene expression in acini is much lower than in unorganized cells. This decrease in Myc transgene activity is responsible for the apparent recalcitrance of organized cells to ectopic Myc, as adenovirus-mediated expression of Myc in organized structures potently induces apoptosis. These observations reveal that organization does not alter the inherent response of epithelial cells to Myc and suggest that other tumor suppression mechanisms, apart from structure, antagonize Myc in the development of solid tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".