Abstract LB-117: Myc phosphorylation suppresses transformation through novel regulatory regions.
Bibliographic record
Abstract
Abstract Myc is a proto-oncogenic transcription factor, whose expression is deregulated in numerous human tumours and over-expression in animal models can cause tumorigenesis. Myc is important in cancer initiation and progression and therefore to develop successful approaches targeting Myc, a better understanding of both the mechanisms of Myc regulation and subsequent deregulation in cancer is important. Identification of Myc post-translational modifications (PTMs) that are essential for its oncogenic activity remains largely unknown. Therefore, detecting those key PTMs and there effect on gene expression would shed more light on the mechanisms of Myc dependent tumorigenesis. In our study, we screened a panel of Myc phosphorylation mutants in MCF10A cells for their ability to promote anchorage-independent colony growth and we identified two previously uncharacterized mutants that increased Myc-dependent transformation: S71A/S81A and 340Cl-A (T343A, S344A, S347A, and S348A). We further confirmed that gain-of-function serine and threonine mutants potentiated transformation in SH-EP neuroblastoma cells and three-dimensional MCF10A acini. Additionally, to investigate the mechanism of increased transformation and mutants' selectivity for gene transcription, we conducted genome-wide mRNA expression analysis of MCF10A acini and identified 112 genes regulated by all MYC alleles, and 158 genes regulated by all three transforming phosphorylation mutants but not wild-type MYC. We performed genome-wide ChIP in MCF10A cells, integrated this with gene transcription data, and showed that the majority of genes with altered expression also have MYC binding in their promoter regions, further suggesting that these gene expression changes are directly regulated by Myc. In conclusion, phosphorylation of S71/S81 and 340Cl amino acid residues negatively regulates Myc dependent transformation, suggesting that better understanding of Myc regulation through the phosphorylation of these residues may provide opportunities to therapeutically target Myc. Citation Format: Dharmendra Dingar, Amanda R. Wasylishen, Michelle Chan-Seng-Yue, Ling Huang, Christina Bros, William Tu, Natalie Meyer, Paul C. Boutros, Linda Z. Penn. Myc phosphorylation suppresses transformation through novel regulatory regions. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-117. doi:10.1158/1538-7445.AM2013-LB-117
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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) | 0.003 | 0.001 |
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".