Syngeneic mouse model of human HER2+ metastatic breast cancer for the evaluation of trastuzumab emtansine combined with oncolytic rhabdovirus
Bibliographic record
Abstract
Background: Established mouse models of HER2+ cancer are based on the over-expression of rodent Neu/Erbb2 homologues, which are incompatible with human HER2 (huHER2) targeted therapeutics. Additionally, the use of immune-deficient xenograft or transgenic models precludes assessment of native anti-tumour immune responses. These hurdles have been a challenge for our understanding of the immune mechanisms behind huHER2-targeting immunotherapies. Methods: To assess the immune impacts of our huHER2-targeted combination strategy, we generated a syngeneic mouse model of huHER2+ breast cancer, using a truncated form of huHER2, HER2T. Following validation of this model, we next treated tumour-bearing with our immunotherapy strategy: oncolytic vesicular stomatitis virus (VSVΔ51) with clinically approved antibody-drug conjugate targeting huHER2, trastuzumab emtansine (T-DM1). We assessed efficacy through tumour control, survival, and immune analyses. Results: The generated truncated HER2T construct was non-immunogenic in wildtype BALB/c mice upon expression in murine mammary carcinoma 4T1.2 cells. Treatment of 4T1.2-HER2T tumours with VSVΔ51+T-DM1 yielded robust curative efficacy compared to controls, and broad immunologic memory. Interrogation of anti-tumour immunity revealed tumour infiltration by CD4+ T cells, and activation of B, NK, and dendritic cell responses, as well as tumour-reactive serum IgG. Conclusions: setting. We further demonstrated that HER2T can be implemented in multiple other syngeneic tumour models, including but not limited to colorectal and ovarian models. These data also suggest that the HER2T platform may be used to assess a range of surface-HER2T targeting approaches, such as CAR-T, T-cell engagers, antibodies, or even retargeted oncolytic viruses.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".