Reprogramming of tumor and extracellular vesicular microRNA in relapsed ovarian cancer
Bibliographic record
Abstract
Majority of advanced-stage ovarian cancer patients, including those with epithelial ovariancancer (EOC), develop recurrent disease and acquisition of resistance to chemotherapy, leading to limited treatment options. Decrease in Let7b miRNA levels in clinical ovarian cancer has been associated with chemoresistance, increased proliferation, invasion, and relapse in EOC. Extracellular Vesicles (EV) mediated transfer of miRNA between cells in the tumor microenvironment have been studied and functional effects of EV-miRNA on the recipient cells have been established. Given the role of tumor miRNA and EV-miRNA in resistance development, modulation of tumor miRNA and EV miRNA content by delivering exogenous miRNA mimics is a promising strategy to achieve cellular reprogramming and improve anti-tumor efficacy In order to mimic the phenomenon of relapse seen in clinical ovarian cancer patients, we have established a murine EOC relapsed model by administering paclitaxel (PTX) and stopping therapy to allow for tumor regrowth. Global molecular profiling studies such as gene expression microRNA and protein profiling in the relapsed tumor showed significant downregulation of Let7b relative to untreated tumors. In this project, we have utilized a novel, biodegradable, and selfassembling delivery system comprising of hyaluronic acid polyethyleneimine conjugate (HAPEI), that can deliver Let7b miRNA mimic (HA-PEI-Let7b NPs) to tumor cells and achieve cellular programming both in vitro and in vivo. We demonstrate that a therapeutic combination of Let7b miRNA and PTX leads to significant improvement in anti-tumor efficacy in the relapsed model of EOC. We further demonstrate that the combination therapy is safe for repeated administration. In addition, using a coculture model of ovarian cancer we demonstrate that Let7b miRNA from transfected cells are further propagated by EV to non-transfected cells. We developed mathematical models and experimentally validated EV mediated Let7b transfer and these models enable us to quantitate the kinetic and predict the functional effects of EV-Let7b miRNA in non-transfected ovarian cancer cells. Taken together we have systematically performed studies to develop and understand the role of miRNA model in relapsed ovarian cancer and selected a clinically relevant miRNA (Let7b) to evaluate the synergistic combination HA-PEI-Let7b with paclitaxel to improve the antitumor efficacy. Our novel approach of cellular reprogramming of tumor cells using clinically 3 relevant miRNA mimic in combination with chemotherapy could enable more effective therapeutic outcomes for patients with advanced-stage relapsed EOC--Author's abstract
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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 | 0.000 |
| 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 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".