Pharmacological targeting of MYC-regulated IRE1/XBP1 pathway suppresses MYC-driven breast cancer
Bibliographic record
Abstract
The unfolded protein response (UPR) is a cellular homeostatic mechanism that is activated in many human cancers and plays pivotal roles in tumor progression and therapy resistance. However, the molecular mechanisms for UPR activation and regulation in cancer cells remain elusive. Here, we show that oncogenic MYC regulates the inositol-requiring enzyme 1 (IRE1)/X-box binding protein 1 (XBP1) branch of the UPR in breast cancer via multiple mechanisms. We found that MYC directly controls IRE1 transcription by binding to its promoter and enhancer. Furthermore, MYC forms a transcriptional complex with XBP1, a target of IRE1, and enhances its transcriptional activity. Importantly, we demonstrate that XBP1 is a synthetic lethal partner of MYC. Silencing of XBP1 selectively blocked the growth of MYC-hyperactivated cells. Pharmacological inhibition of IRE1 RNase activity with small molecule inhibitor 8866 selectively restrained the MYC-overexpressing tumor growth in vivo in a cohort of preclinical patient-derived xenograft models and genetically engineered mouse models. Strikingly, 8866 substantially enhanced the efficacy of docetaxel chemotherapy, resulting in rapid regression of MYC-overexpressing tumors. Collectively, these data establish the synthetic lethal interaction of the IRE1/XBP1 pathway with MYC hyperactivation and provide a potential therapy for MYC-driven human breast cancers.
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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".