Abstract B070: Dissecting EV dynamics in the sequestration of doxorubicin from breast cancer cells
Bibliographic record
Abstract
Abstract Background: Extracellular vesicles (EVs) are one of the three main analytes sought for in liquid biopsy development. EVs are released by all cell types including cancer cells and play a crucial role in cell-cell communication. Breast cancer cells have been shown to utilize EVs as a mechanism to sequester chemotherapeutic agents, thereby conferring resistance to treatment. This study investigates the role of EVs in doxorubicin (DOX) resistance in MDA-MB-231 breast cancer cells. Method: MDA-MB-231 cells were exposed to increasing dose of anthracycline for 6 and 12 months. Resistance was confirmed by cytotoxicity assay and western blot. Resistant and parent cells were labeled for immunohistochemistry and their EVs were isolated via ultracentrifugation. EVs were analysed using nanoscale flow cytometry and sent for mass spectrophotometry. Result: Using nanoscale flow cytometry, we optimized a method to detect DOX-carrying EVs based on the intrinsic fluorescence of the drug. We found that DOX-resistant cells exhibited lower intracellular DOX accumulation and higher expression of the EV marker CD63 compared to their parental counterparts. Conclusion: These results suggest that chemoresistant breast cancer cells may alter their EV production machinery to evade the lethal effects of DOX. Understanding this mechanism could lead to novel strategies to overcome chemoresistance in breast cancer. Citation Format: Sina Halvaei, Jing Xu, Suganthi Chittaranjan, Sharon Gorski, Karla Williams. Dissecting EV dynamics in the sequestration of doxorubicin from breast cancer cells [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B070.
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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 | 0.001 |
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".