Therapeutic inhibition of USP9x-mediated Notch signaling in triple-negative breast cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a breast cancer subtype that lacks targeted treatment options. The activation of the Notch developmental signaling pathway, which is a feature of TNBC, results in the secretion of proinflammatory cytokines and the recruitment of protumoral macrophages to the tumor microenvironment. While the Notch pathway is an obvious therapeutic target, its activity is ubiquitous, and predictably, anti-Notch therapies are burdened with significant on-target side effects. Previously, we discovered that, under conditions of cellular stress commonly found in the tumor microenvironment, the deubiquitinase USP9x forms a multiprotein complex with the pseudokinase tribbles homolog 3 (TRB3) that together activate the Notch pathway. Herein, we provide preclinical studies that support the potential of therapeutic USP9x inhibition to deactivate Notch. Using a murine TNBC model, we show that USP9x knockdown abrogates Notch activation, reducing the production of the proinflammatory cytokines, C-C motif chemokine ligand 2 (CCL2) and interleukin-1 beta (IL-1β). Concomitant with these molecular changes, a reduction in tumor inflammation, the augmentation of antitumor immune response, and the suppression of tumor growth were observed. The pharmacological inhibition of USP9x using G9, a partially selective, small-molecule USP9x inhibitor, reduced Notch activity, remodeled the tumor immune landscape, and reduced tumor growth without associated toxicity. Proving the role of Notch, the ectopic expression of the activated Notch1 intracellular domain rescued G9-induced effects. This work supports the potential of USP9x inhibition to target Notch in metabolically vulnerable tissues like TNBC, while sparing normal Notch-dependent tissues.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".