MYC Phosphorylation at Novel Regulatory Regions Suppresses Transforming Activity
Bibliographic record
Abstract
Despite its central role in human cancer, MYC deregulation is insufficient by itself to transform cells. Because inherent mechanisms of neoplastic control prevent precancerous lesions from becoming fully malignant, identifying transforming alleles of MYC that bypass such controls may provide fundamental insights into tumorigenesis. To date, the only activated allele of MYC known is T58A, the study of which led to identification of the tumor suppressor FBXW7 and its regulator USP28 as a novel therapeutic target. In this study, we screened a panel of MYC phosphorylation mutants for their ability to promote anchorage-independent colony growth of human MCF10A mammary epithelial cells, identifying S71A/S81A and T343A/S344A/S347A/S348A as more potent oncogenic mutants compared with wild-type (WT) MYC. The increased cell-transforming activity of these mutants was confirmed in SH-EP neuroblastoma cells and in three-dimensional MCF10A acini. Mechanistic investigations initiated by a genome-wide mRNA expression analysis of MCF10A acini identified 158 genes regulated by the mutant MYC alleles, compared with only 112 genes regulated by both WT and mutant alleles. Transcriptional gain-of-function was a common feature of the mutant alleles, with many additional genes uniquely dysregulated by individual mutant. Our work identifies novel sites of negative regulation in MYC and thus new sites for its therapeutic attack.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".