Abstract 1185: Phosphorylation of the MYC oncoprotein disrupts the direct interaction of MYC with the ASF1A histone chaperone
Bibliographic record
Abstract
Despite the profound role of deregulated MYC activity as a potent cancer driver, targeting MYC directly for the development of effective anti-cancer agents has not yet been fruitful. An alternate strategy is to disrupt the interaction of MYC with key partner proteins critical to MYC-driven oncogenesis. To this end, we took a triangulated strategy. First, we identified hundreds of direct and indirect MYC protein-protein interactions within cells using MYC-BioID mass spectrometry. Next to distinguish MYC-protein interactors that are functionally important for MYC oncogenic activity we performed a genome-wide CRISPR knock-out screen using an isogenic pair of normal and MYC-driven breast cancer cells. Finally, we distinguished key control points of MYC oncogenic activity and then identified protein that bound directly at these sites. This triangulated strategy has been highly fruitful. In this study, we focused on MYC phosphorylation as a key control point, as 20% of MYC is composed of residues with the potential to be phosphorylated, including Serine (S), Threonine, and Tyrosine. We have identified two regulatory phosphorylation sites, S71 and S81, that control MYC oncogenesis. Mutating each individual residue to alanine had no effect, however mutating both residues to alanine, resulted in a MYC protein that is gain-of-function (GoF) for MYC-driven transformation. To investigate the mechanism by which this GoF mutation results in potentiated oncogenic activity, we identified MYC protein-protein interactors controlled by MYC phosphorylation at these sites using our proximity-based labeling technique, BioID. We validated that the interaction of MYC with the histone chaperone ASF1A significantly increases with this MYC GoF protein within cells. The interaction is disrupted in response to MYC phosphorylation at these residues following Jun kinase activation under stressful conditions. This reinforces phosphorylation functions as a switch mechanism to inhibit MYC activity by disrupting MYC-ASF1A interaction. ASF1A binds to all variants of histone H3, functioning as an intermediary between several complexes responsible for the deposition of H3 variants in differing contexts associated with chromatin remodeling complexes, such as CAF-1 (RBBP4/CHAF1A/CHAF1B) and HIRA/UBN1/CABIN1. Interestingly, many of these complex components were hits in our CRISPR screen, further reinforcing functional relevance. Using NMR spectroscopy and biolayer interferometry, we have shown that MYC interacts directly with ASF1A. Moreover, this interaction of MYC-ASF1A is robustly abrogated by phosphomimetic mutations of either S71 or S81. Thus, we propose a model whereby phosphorylation of MYC at S71 and S81 blocks association with ASF1A. Due to its key role in replication and transcription, disrupting the MYC-ASF1A interaction has potential as a novel target for the development of anti-MYC therapeutics. Citation Format: Linda Z. Penn, Alannah MacDonald, Tristan Kenney, Adelaide Mitchell, Brian Raught, Cheryl Arrowsmith. Phosphorylation of the MYC oncoprotein disrupts the direct interaction of MYC with the ASF1A histone chaperone [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1185.
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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".