Polyclonal antibody-induced downregulation of HER1/EGFR and HER2 surpasses the effect of combinations of specific registered antibodies
Bibliographic record
Abstract
Antitumor therapies targeting HER1/EGFR and HER2, such as monoclonal antibodies (MAbs) and tyrosine-kinase inhibitors (TKIs), have demonstrated a significant clinical benefit, but the emergence of resistance limits long-term efficacy. While secondary HER1 mutations confer tolerance to TKI, compensatory upregulation of HER2 drives resistance to anti-HER1 MAbs, which identifies MAb combinations targeting both receptors as an attractive therapeutic strategy. Nevertheless, toxicity hampers the clinical validation of this approach. Alternatively, cancer vaccines may induce antibodies directed against several antigens with less concern about induced toxicity.Polyclonal antibodies (PAbs) targeting HER1 and HER2 were induced in mice or rabbits through immunization. Recognition of different epitopes on targets by PAbs was validated by phage-display technology. Receptor downregulation was evaluated by flow cytometry, immunofluorescence, and Western blot. MTT assays assessed cytotoxicity, while the antitumor effect of PAbs was assayed in nude mice.PAbs promoted degradation of HER1 and HER2 regarding clinical MAbs or their combinations. As a result, inhibition of cytotoxicity on tumor cell lines was improved, even in the presence of oncogenic mutations in HER1, as well as in cetuximab-insensitive cells. Accordingly, the antitumor effect of vaccination-induced PAbs was observed in lung tumor lines representative of sensitivity or resistance to HER1 targeting therapies.Immunization against HER1 and HER2 receptors offers an alternative to passive administration of combinations of MAbs, since vaccination-induced PAbs promote the downregulation of both receptors and they have a higher impact on the survival of tumor cells.
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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 | 0.001 |
| 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".