Abstract 2466: Detection of extracellular vesicle-associated DNA in mouse metastatic breast cancer cells
Bibliographic record
Abstract
Abstract Background: Breast cancer is the second most common type of cancer worldwide, and while primary disease is often well controlled, metastatic breast cancer (MBC) is responsible for the majority of deaths. MBC cells can metastasize to distant sites including the liver, lung, brain and bone, and the mechanisms underlying this organotropism remain largely unknown. MBC has high genetic heterogeneity, and studies have reported mutations in oncogenic pathways such as TP53, PIK3CA and ESR1. Liquid biopsy has been used to detect extracellular vesicle-associated DNA (EV-DNA), bypassing the limits of tissue biopsies often invasive and difficult to obtain. EVs are nanoparticles of diverse sizes released in the extracellular environment by all types of cells and are known to carry cargos including EV associated proteins, lipids, RNA and DNA with the potential to interact with surrounding cells and be explored as markers of disease progression. The goal of our study was to detect EV associated oncogenes derived from MBC cell lines. EV-DNA can potentially be incorporated into cancer diagnosis and prognosis in the clinical setting. Methods: In this study, we isolated EVs from 4T1 mouse mammary tumor model with primary cells and 4T1 induced liver-metastatic variants (2776, 2792 cells). We isolated and characterized the EVs using nanoparticle tracking analysis (NTA) and dynamic light scattering (DLS) for particle size and concentration, as well as western blot (WB) for EV markers. EV-DNA was isolated using the Gentra puregene blood kit (Qiagen) and quantified using a Qubit fluorometer, and oncogenic mutations assessed using droplet digital polymerase chain reaction (ddPCR) to detect mutant PIK3CA H1047R, TP53 P31T, and ESR1 D538G. Results: EVs isolated from primary and liver metastatic MBC cell lines were positive for EV markers synthenin-1 and CD63. NTA showed higher concentrations of EVs from liver-metastatic variants cell lines (2776: 1.11x1011 particles/ml, mean size 109.2 nm; 2792: 1.37x1011 particles/ml, mean size 122.7 nm) compared to primary tumor cells (4T1: 5.83x1010 particles/ml; mean size: 120.3 nm). These results suggest an increased EV emission from metastatic variants cell lines compared to parental cells. Interestingly, significantly higher EV-DNA concentration was found in liver-metastatic cells (2776: 2.85 ng/μl; 2792: 1.48 ng/μl) than in parental cells (0.23 ng/μl). Conclusions: These findings suggest circulating oncogenic EV-DNA may be a useful biomarker to monitor MBC patients throughout disease progression. For future investigation of EV enriched material, we will assess the mutant copies of oncogenes TP53 P31T, PIK3CA H1047R and ESR1 D538G associated with the EVs of parental cells and metastatic variants. Extensive studies in vitro and in vivo are required to validate and clarify definitive roles of EVs in tumor invasion and to further our understanding of the role of EV cargo in spreading malignancies. Citation Format: Amélie Nadeau, Kyle Dickinson, Thupten Tsering, Emilie Solymoss, Sébastien Tabariès, Peter Siegel, Julia Burnier. Detection of extracellular vesicle-associated DNA in mouse metastatic breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2466.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".