Inhibition of Oncogenic Epidermal Growth Factor Receptor Kinase Triggers Release of Exosome-like Extracellular Vesicles and Impacts Their Phosphoprotein and DNA Content
Bibliographic record
Abstract
Cancer cells emit extracellular vesicles (EVs) containing unique molecular signatures. Here, we report that the oncogenic EGF receptor (EGFR) and its inhibitors reprogram phosphoproteomes and cargo of tumor cell-derived EVs. Thus, phosphorylated EGFR (P-EGFR) and several other receptor tyrosine kinases can be detected in EVs purified from plasma of tumor-bearing mice and from conditioned media of cultured cancer cells. Treatment of EGFR-driven tumor cells with second generation EGFR kinase inhibitors (EKIs), including CI-1033 and PF-00299804 but not with anti-EGFR antibody (Cetuximab) or etoposide, triggers a burst in emission of exosome-like EVs containing EGFR, P-EGFR, and genomic DNA (exo-gDNA). The EV release can be attenuated by treatment with inhibitors of exosome biogenesis (GW4869) and caspase pathways (ZVAD). The content of P-EGFR isoforms (Tyr-845, Tyr-1068, and Tyr-1173), ERK, and AKT varies between cells and their corresponding EVs and as a function of EKI treatment. Immunocapture experiments reveal the presence of EGFR and exo-gDNA within the same EV population following EKI treatment. These findings suggest that targeted agents may induce cancer cells to change the EV emission profiles reflective of drug-related therapeutic stress. We suggest that EV-based assays may serve as companion diagnostics for targeted anticancer agents.
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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) | 0.001 | 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".