Vaccine-Induced Memory CD8+ T Cells Provide Clinical Benefit in HER2 Expressing Breast Cancer: A Mouse to Human Translational Study
Bibliographic record
Abstract
Abstract Purpose: Immune-based therapy for metastatic breast cancer has had limited success, particularly in molecular subtypes with low somatic mutations rates. Strategies to augment T-cell infiltration of tumors include vaccines targeting established oncogenic drivers such as the genomic amplification of HER2. We constructed a vaccine based on a novel alphaviral vector encoding a portion of HER2 (VRP-HER2). Patients and Methods: In preclinical studies, mice were immunized with VRP-HER2 before or after implantation of hHER2+ tumor cells and HER2-specific immune responses and antitumor function were evaluated. We tested VRP-HER2 in a phase I clinical trial where subjects with advanced HER2-overexpressing malignancies in cohort 1 received VRP-HER2 every 2 weeks for a total of 3 doses. In cohort 2, subjects received the same schedule concurrently with a HER2-targeted therapy. Results: Vaccination in preclinical models with VRP-HER2 induced HER2-specific T cells and antibodies while inhibiting tumor growth. VRP-HER2 was well tolerated in patients and vaccination induced HER2-specific T cells and antibodies. Although a phase I study, there was 1 partial response and 2 patients with continued stable disease. Median OS was 50.2 months in cohort 1 (n = 4) and 32.7 months in cohort 2 (n = 18). Perforin expression by memory CD8 T cells post-vaccination significantly correlated with improved PFS. Conclusions: VRP-HER2 increased HER2-specific memory CD8 T cells and had antitumor effects in preclinical and clinical studies. The expansion of HER2-specific memory CD8 T cells in vaccinated patients was significantly correlated with increased PFS. Subsequent studies will seek to enhance T-cell activity by combining with anti-PD-1.
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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 | 0.007 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".