Abstract A006: Natural interception of drug resistance in breast cancer: Nanoencapsulation of Terfezia extracts enhances doxorubicin sensitivity in early-onset models
Bibliographic record
Abstract
Abstract Breast cancer in younger patients is rising at an alarming rate, emphasizing the urgent need for early interception strategies to delay relapse and improve survival outcomes. Resistance to anthracyclines such as doxorubicin remains a major challenge, as resistant cells rapidly exploit alternative pathways to evade therapy. In this context, natural compounds with pleiotropic activity offer a promising avenue to address these limitations. Extracts from desert truffles were evaluated both in their free form and after encapsulation in hydroxypropyl-β-cyclodextrin (HPβCD) nano-complexes, designed to enhance solubility and bioavailability. In vitro studies on drug-sensitive and doxorubicin-resistant breast cancer cells confirmed that both free and nanoformulated extracts exerted significant cytotoxicity, with nanoformulation further amplifying potency and stability. Co-treatment with doxorubicin restored chemosensitivity in resistant cells through opportunistic pathway modulation, resulting in synergistic anticancer activity. In vivo, administration of both free and encapsulated extracts reduced tumor progression, rebalanced endogenous antioxidant defenses, and, importantly, improved tolerability relative to doxorubicin alone. Alongside their anticancer potential, the formulations demonstrated a cardioprotective effect, mitigating one of the most limiting toxicities of anthracycline therapy. Collectively, these findings identify desert truffle formulations both free and nanoencapsulated as promising candidates for intercepting drug resistance in breast cancer. By enhancing the sensitivity of resistant tumors to conventional therapy, reducing systemic toxicity, and conferring cardioprotection, they highlight a novel natural strategy that could be integrated into precision oncology frameworks to address the unmet needs of early-diagnosed patients at high risk of relapse. Citation Format: Roua A. Nouh, Anwar A. Abdelnasser, Mohamed S. Sedeek, Moahmed A. Farag. Natural interception of drug resistance in breast cancer: Nanoencapsulation of Terfezia extracts enhances doxorubicin sensitivity in early-onset models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A006.
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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) | 0.002 | 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".