Abstract 2998: Co-targeting bulk tumor and CSCs in clinically translatable TNBC patient-derived xenografts via combination nanotherapy
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) accounts disproportionally for the majority of breast cancer related deaths throughout the world. This is largely attributed to lack of a specific therapy capable of targeting both bulk tumor mass and cancer stem cells (CSC) as well as appropriate tumor animal models to accurately evaluate treatment efficacy for the clinical translation. Thus, development of effective and clinically translatable targeted therapies for TNBC is an unmet medical need. In this report, we developed a hybrid nanoparticles-based co-delivery platform containing both paclitaxel and verteporfin (PV-NPs) to target TNBC patient-derived xenograft (PDX) tumor and CSCs. MRI and IVIS imaging were performed on mice containing PDX tumors to assess tumor vascularity and selective accumulation of NPs. NF-kB, Wnt and YAP activities were measured by reporter assays. Mice bearing TNBC PDX tumor were treated with PV-NPs and controls; and tumors progression and CSC subpopulations were analyzed. Our MRI imaging studies indicated high vascularization of PDX tumors. IVIS imaging showed selective accumulation of NPs in PDX tumors. In comparison to control NPs and free-drug combination, PV-NPs significantly retarded tumor growth of TNBC PDX. PV-NPs simultaneously repressed NF-kB, Wnt and YAP that have been shown to be crucial for cancer growth, CSC development and tumorigenesis. In conclusion, NPs containing two clinically used drugs concurrently inhibited NF-kB, Wnt and YAP pathways and exhibited synergic effects on killing TNBC bulk tumor and CSCs. This combination nanotherapy evaluated with a PDX model may lead to an effective treatment of TNBC patients.Note: This abstract was not presented at the meeting.Citation Format: Andrew Sulaiman, Sara El-Sahli, Sarah McGarry, Lisheng Wang, Suresh Gadde. Co-targeting bulk tumor and CSCs in clinically translatable TNBC patient-derived xenografts via combination nanotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2998.
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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".