Functional HER1/HER2-Expressing Murine Tumor Models for Preclinical Evaluation of Targeted Therapies
Bibliographic record
Abstract
Background: HER1 and HER2 are critical receptors involved in tumorigenesis and the development of targeted therapies for various carcinomas. However, most antibodies and drugs currently in development do not recognize murine orthologs, which restricts their evaluation in immunocompetent models. Methods: We generated nine tumor models through the lentiviral transduction of murine prostate (RM1), lung (3LL-D122), and breast (4T1) carcinoma cell lines, subsequently validating them in immunocompetent BALB/c and C57BL/6 hosts. Receptor expression and functionality were characterized using flow cytometry, immunoblotting, proliferation assays, and therapeutic sensitivity testing. Results: Transduced cells exhibited stable membrane expression of HER1/HER2 and ligand-induced phosphorylation, confirming receptor functionality. In all three tumor models generated, the expression of HER1 and/or HER2 significantly enhanced cell proliferation compared to parental lines. Furthermore, treatment with specific monoclonal antibodies and the tyrosine kinase inhibitor markedly reduced the viability of cells expressing HER1 and/or HER2, without affecting negative controls. Conclusions: These models provide a robust and reproducible platform for the preclinical evaluation of HER1/HER2-targeted therapies in immunocompetent hosts. Although the current model relies on subcutaneous implantation and does not fully replicate the native tumor microenvironment, it represents a crucial first step toward the development of orthotopic and immunologically relevant models for translational cancer research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".