Nanoparticle-Mediated mRNA Delivery to Triple-Negative Breast Cancer (TNBC) Patient-Derived Xenograft (PDX) Tumors
Why this work is in the frame
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Bibliographic record
Abstract
mRNA-based therapies can overcome several challenges faced by traditional therapies in treating a variety of diseases by selectively modulating genes and proteins without genomic integration. However, due to mRNA's poor stability and inherent limitations, nanoparticle (NP) platforms have been developed to deliver functional mRNA into cells. In cancer treatment, mRNA technology has multiple applications, such as restoration of tumor suppressors and activating antitumor immunity. Most of these applications have been evaluated using simple cell-line-based tumor models, which failed to represent the complexity, heterogeneity, and 3D architecture of patient tumors. This discrepancy has led to inconsistencies and failures in clinical translation. Compared to cell line models, patient-derived xenograft (PDX) models more accurately represent patient tumors and are better suitable for modeling. Therefore, for the first time, this study employed two different TNBC PDX tumors to examine the effects of the mRNA-NPs. mRNA-NPs are developed using EGFP-mRNA as a model and studied in TNBC cell lines, ex vivo TNBC PDX organotypic slice cultures, and in vivo TNBC PDX tumors. Our findings show that NPs can effectively accumulate in tumors after intravenous administration, protecting and delivering mRNA to PDX tumors with different genetic and chemosensitivity backgrounds. These studies offer more clinically relevant modeling systems for mRNA nanotherapies in cancer applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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