Characterization of extracellular vesicle-associated DNA and proteins derived from organotropic metastatic breast cancer cells
Bibliographic record
Abstract
BACKGROUND: While primary breast cancer (BC) is often effectively managed, metastasis remains the primary cause of BC-related fatalities. Gaps remain in our understanding of the mechanisms regulating cancer cell organotropism with predilection to specific organs. Unraveling mediators of site-specific metastasis could enhance early detection and enable more tailored interventions. Liquid biopsy represents an innovative approach in cancer involving the analysis of biological materials such as circulating tumor DNA and tumor-derived extracellular vesicles (EV) found in body fluids like blood or urine. This offers valuable insights for characterizing and monitoring tumor genomes to advance personalized medicine in metastatic cancers. METHODS: We performed in-depth analyses of EV cargo associated with BC metastasis using eight murine cell line models with distinct metastatic potentials and organotropism to the lung, the bone, the liver, and the brain. We characterized the secretome of these cells to identify unique biomarkers specific to metastatic sites. RESULTS: Small EVs isolated from all cell lines were quantified and validated for established EV markers. Tracking analysis and electron microscopy revealed EV secretion patterns that differed according to cell line. Cell-free (cf)DNA and EV-associated DNA (EV-DNA) were detected from all cell lines with varying concentrations. We detected a TP53 mutation in both EV-DNA and cfDNA. Mass spectrometry-based proteomics analyses identified 698 EV-associated proteins, which clustered according to metastatic site. This analysis highlighted both common EV signatures and proteins involved in cancer progression and organotropism unique to metastatic cell lines. Among these, 327 significantly differentially enriched proteins were quantified with high confidence levels across BC and metastatic BC cells. We found enrichment of specific integrin receptors in metastatic cancer EVs compared to EVs secreted from non-transformed epithelial cells and matched tumorigenic non-metastatic cells. Pathway analyses revealed that EVs derived from parental cancer cells display a cell adhesion signature and are enriched with proteins involved in cancer signaling pathways. CONCLUSION: Taken together, the characterization of EV cargo in a unique model of BC organotropism demonstrated that EV-DNA and EV proteomes were informative of normal and cancer states. This work could help to identify BC biomarkers associated with site-specific metastasis and new therapeutic targets.
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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 | 0.001 | 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".