Nanoparticles Loaded with Wnt and YAP/Mevalonate Inhibitors in Combination with Paclitaxel Stop the Growth of TNBC Patient‐Derived Xenografts and Diminish Tumorigenesis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) accounts for the majority of breast cancer‐related deaths and remains the hardest breast cancer to treat due to the lack of specific therapeutic targets. While chemotherapy is the mainstay of systemic treatment for TNBC, it is associated with chemotherapy‐induced cancer stem cells (CSCs) and tumor regeneration. Here, it is found that Wnt and YAP target genes that have been closely associated with CSCs are highly expressed in TNBC patient tumors and negatively correlated with patient survival. Therefore, a nanotherapeutic strategy is employed, using nanomaterials that are approved by the FDA, and two co‐delivery nanoparticle platforms (NPs) are developed to target TNBC. These NPs contain Wnt inhibitor PRI‐724 (in clinical trials) and YAP/mevalonate inhibitor simvastatin (FDA‐approved). Toward clinical translation, nanotherapeutic efficacy is assessed in clinically relevant patient‐derived xenograft (PDX) models. These NPs in combination with the chemotherapeutic drug paclitaxel effectively halt the growth of both paclitaxel‐resistant and paclitaxel‐sensitive PDX tumors, and diminish the paclitaxel‐induced CSC enrichment around two to fourfold. Importantly, NPs also decrease the paclitaxel‐enhanced PDX tumorigenesis after secondary transplantation. Together, this study demonstrates the efficacy of two NP platforms using clinically translatable TNBC PDX models, suggesting their application potential for the treatment of TNBC.
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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 | 0.000 | 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 | 0.000 |
| 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 it