Stimulation of Oncogene-Specific Tumor-Infiltrating T Cells through Combined Vaccine and αPD-1 Enable Sustained Antitumor Responses against Established HER2 Breast Cancer
Bibliographic record
Abstract
Abstract Purpose: Despite promising advances in breast cancer immunotherapy, augmenting T-cell infiltration has remained a significant challenge. Although neither individual vaccines nor immune checkpoint blockade (ICB) have had broad success as monotherapies, we hypothesized that targeted vaccination against an oncogenic driver in combination with ICB could direct and enable antitumor immunity in advanced cancers. Experimental Design: Our models of HER2+ breast cancer exhibit molecular signatures that are reflective of advanced human HER2+ breast cancer, with a small numbers of neoepitopes and elevated immunosuppressive markers. Using these, we vaccinated against the oncogenic HER2Δ16 isoform, a nondriver tumor-associated gene (GFP), and specific neoepitopes. We further tested the effect of vaccination or anti–PD-1, alone and in combination. Results: We found that only vaccination targeting HER2Δ16, a driver of oncogenicity and HER2-therapeutic resistance, could elicit significant antitumor responses, while vaccines targeting a nondriver tumor-specific antigen or tumor neoepitopes did not. Vaccine-induced HER2-specific CD8+ T cells were essential for responses, which were more effective early in tumor development. Long-term tumor control of advanced cancers occurred only when HER2Δ16 vaccination was combined with αPD-1. Single-cell RNA sequencing of tumor-infiltrating T cells revealed that while vaccination expanded CD8 T cells, only the combination of vaccine with αPD-1 induced functional gene expression signatures in those CD8 T cells. Furthermore, we show that expanded clones are HER2-reactive, conclusively demonstrating the efficacy of this vaccination strategy in targeting HER2. Conclusions: Combining oncogenic driver targeted vaccines with selective ICB offers a rational paradigm for precision immunotherapy, which we are clinically evaluating in a phase II trial (NCT03632941).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".