Abstract A10: The myc post-translational landscape: How novel gain-of-function mutants are revealing new stability and functional regulatory systems
Bibliographic record
Abstract
Abstract The c-MYC (MYC) oncogene plays an important role in tumorigenesis and is implicated in >50% of all human cancers. Deregulation of MYC can occur through abnormally high expression levels, but also through oncogenic lesions in upstream signaling cascades. The study of these signaling pathways have provided an alternative approach for the development of MYC-targeted therapeutics. For example, the study of post-translational modifications (PTMs) of MYC, such as P-T58 and the T58A gain-of-function mutant, identified FBXW7 as a tumor suppressor and the deubiquitinating enzyme USP28 as a therapeutic target. We considered that MYC is highly modified post-translationally and that unknown mechanistic pathways may be modifying residues, in addition to T58, in order to control MYC stability and/or function. These undiscovered pathways may therefore provide additional opportunities for the development of MYC-targeted therapeutics. These considerations led to recent work in the Penn lab that uncovered clusters of negatively regulating residues of MYC function. These residues include S71/S81, a cluster of residues referred to as MYC-4 (T343, S344, S347 and S348) and a cluster of 6 lysine residues (6K) at the C-terminal end of MYC (K298, K317, K323, K326, K341 and K355). These negatively regulating residues were characterized using alanine (S71/S81 and 340 cluster) and arginine (C-terminal lysines) substitution mutants in our established transformation assays. The S71/S81A and MYC-4A mutants scored with having gain-of-function activity in comparison to wild-type MYC in multiple transformation assays including growth in soft agar and the disruption of regular acini formation using a normal, immortalized MCF10A cell line. In addition, these mutants were shown to regulate additional genes compared to wild-type MYC using genome-wide mRNA expression analysis of MCF10A acini, suggesting that these MYC proteins have gained additional transcriptional targets. Additionally, substitution of the C-terminal lysine residues with arginine (6KR) also revealed gain-of-function activity. 6KR expressing MCF10A and SH-EP cells had increased anchorage-independent growth compared to cells expressing wild-type MYC and was also more potent in promoting xenograft tumor growth of Rat1A and SH-EP cells. Interestingly, all three mutants do not have extended half-lives as seen with T58A, suggesting that functional activity and not stability is contributing to these transformative phenotypes. The above mutants reveal that each of S71/S81, MYC-4 and C-terminal 6K residues are critically important for the negative regulation of MYC-induced transformation. To further explore these regions of MYC, we used mass spectrometry to identify post-translational modifications that occurred on MYC in growing cells. These data confirm phosphorylation events on S71/81 as well as at MYC-4A. Strikingly, three modifications were directly observed on three of the six lysine residues; acetylation of lysine 323, ubiquitylation of lysine 355 and SUMOylation of lysine 326. The importance of these modifications and the roles that these modifications have in regulating MYC activity are currently under investigation using our established transformation assays. I now aim to understand the contribution of single or multiple modifications within the indicated clusters and how these modifications modulate MYC activity. Citation Format: Corey Lourenco, Amanda Wasylishen, Michelle Chan-Seng-Yue, Christina Bros, Dharmendra Dingar, William Tu, Manpreet Kalkat, Pak-Kei Chan, Peter Mullen, Brian Raught, Paul Boutros, Linda Penn. The myc post-translational landscape: How novel gain-of-function mutants are revealing new stability and functional regulatory systems. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr A10.
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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 | 0.002 | 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) | 0.000 | 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".