Concomitant Targeting of Tumor Cells and Induction of T-cell Response Synergizes to Effectively Inhibit Trastuzumab-Resistant Breast Cancer
Bibliographic record
Abstract
Trastuzumab is an iconic rationally designed targeted therapy for HER2-positive breast cancers. However, the low response rate and development of resistance call for novel approaches for the treatment of patients. Here, we report that concurrent targeting of tumor cells and activation of T cells in the tumor microenvironment results in a synergistic inhibitory effect on tumor growth and overcomes resistance in two distinct PTEN loss-mediated trastuzumab-resistant mammary tumor mouse models. In vivo combination treatment with HER2/Neu antibody and Akt inhibitor triciribine effectively inhibited tumor growth in both models via inhibiting PI3K/AKT and mitogen-activated protein kinase signaling accompanied by increased T-cell infiltration in the tumor microenvironment. We showed that both CD8(+) and CD4(+) T cells were essential to the optimal antitumor effect of this combination treatment in an IFN-γ-dependent manner. Importantly, the antitumor activities of HER2/Neu antibody and triciribine combination treatment were further improved when coinhibitory receptor cytotoxic T-lymphocyte-associated antigen 4 was blocked to enhance the T-cell response. Our data indicate that multitargeted combinatorial therapies targeting tumor cells and concomitantly enhancing T-cell response in the tumor microenvironment could cooperate to exert maximal therapeutic activity, suggesting a promising clinical strategy for treating trastuzumab-resistant breast cancers and other advanced malignancies.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".