Bibliographic record
Abstract
Despite the knowledge that the MYC protein family is a potent driver of multiple oncogenic malignancies, no targeted therapy against MYC exists for patient care. One major obstacle for conventional drug development is that MYC is an intrinsically disordered protein, that only upon binding to protein partners adapts a conformation. One of the recently identified MYC protein interactors is the Protein Phosphatase 1 (PP1)/PP1 Nuclear Targeting Subunit (PNUTS) complex. Inhibition of PP1 leads to the hyperphosphorylation and degradation of MYC, thus making this complex a potential vulnerability of MYC-driven cancers. As protein phosphatases are promiscuous enzymes with multiple substrates, we placed our focus for developing a MYC-specific inhibitor on the interaction of MYC with the regulatory protein PNUTS. To this end we took two approaches: i) characterizing the MYC-PNUTS interaction structurally; and ii) demonstrating that disruption of this interaction is detrimental to tumour growth.First, we identify the MYC-PNUTS interaction. PNUTS interacts directly through the PNUTS amino-terminal domain (PAD) with the MYC homology Box 0 (MB0) of MYC. The latter is a highly conserved region, shown to be important for MYC oncogenic activity. We also demonstrate that mutation of key residues on either protein disrupts this interaction and leads to elevated phosphorylation of MYC in human cells. Leveraging the MYC-PNUTS structural information, we designed a proof-of-principle study, in which we show that overexpression of the PAD provokes disruption of the MYC-PNUTS complex in human cells. Moreover, we find that disruption of this complex affects MYC-driven transcriptional programs, and alters breast cancer cell proliferation, transformation, and xenograft growth. Taken together, the work presented in this thesis lays the groundwork for developing novel, targeted therapeutics against the to date deemed ‘undruggable’ MYC oncoprotein. Considering the omnipresence of MYC dysregulation in human cancers, these findings hold the promise of ultimately developing a MYC-PNUTS inhibitor and benefiting many cancer patients.
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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) | 0.001 | 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".