A triple-drug nanotherapy to target breast cancer cells, cancer stem cells, and tumor vasculature
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, accounting for the majority of breast cancer-related death. Due to the lack of specific therapeutic targets, chemotherapeutic agents (e.g., paclitaxel) remain the mainstay of systemic treatment, but enrich a subpopulation of cells with tumor-initiating capacity and stem-like characteristics called cancer stem cells (CSCs); thus development of a new and effective strategy for TNBC treatment is an unmet medical need. Cancer nanomedicine has transformed the landscape of cancer drug development, allowing for a high therapeutic index. In this study, we developed a new therapy by co-encapsulating clinically approved drugs, such as paclitaxel, verteporfin, and combretastatin (CA4) in polymer-lipid hybrid nanoparticles (NPs) made of FDA-approved biomaterials. Verteporfin is a drug used in the treatment of macular degeneration and has recently been found to inhibit the Hippo/YAP (Yes-associated protein) pathway, which is known to promote the progression of breast cancer and the development of CSCs. CA4 is a vascular disrupting agent and has been tested in phase II/III of clinical trials. We found that our new three drug-NP not only effectively inhibited TNBC cell viability and cell migration, but also significantly diminished paclitaxel-induced and/or CA4-induced CSC enrichment in TNBC cells, partially through inhibiting the upregulated Hippo/YAP signaling. Combination of verteporfin and CA4 was also more effective in suppressing angiogenesis in an in vivo zebrafish model than single drug alone. The efficacy and application potential of our triple drug-NPs were further assessed by using clinically relevant patient-derived xenograft (PDX) models. Triple drug-NP effectively inhibited the viability of PDX organotypic slide cultures ex vivo and stopped the growth of PDX tumors in vivo. This study developed an approach capable of simultaneously inhibiting bulk cancer cells, CSCs, and angiogenesis.
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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 | 0.000 | 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 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".