Abstract 904: Stimulation and expansion of oncogene-reactive tumor infiltrating T cells through combined Ad-HER2Δ16 vaccination and anti-PD1 enable anti-tumor responses against established HER2 BC
Bibliographic record
Abstract
Abstract Despite promising advances, overcoming immune suppression and driving productive immune responses in the tumor microenvironment remains a significant challenge. Using a spontaneous breast cancer model, we found that vaccination targeting HER2d16, a highly expressed driver of oncogenicity and HER2-therapeutic resistance, elicited significant anti-tumor responses. In contrast, vaccines targeting a non-driver tumor-specific antigen (GFP) or unique non-driver tumor neoepitopes had no impact on tumor occurrence or progression. While vaccine-induced HER2-specific CD8+ T cells were essential for responses, tumors treated therapeutically with a vaccine alone ultimately progressed. However, long-term tumor control and complete tumor regression was only achieved when vaccine was combined with immune-checkpoint blockade (anti-PD1). Single cell RNAsequencing of tumor-infiltrating T cells (TILs) revealed that while vaccination expanded CD8 T cells within the tumor, only the combination of vaccine with anti-PD1 therapy induced a tumor rejection activation signature that was identified in the expanded T cell clones. We go on to use the single cell data to clone and reexpress the TCRs from expanded TILs from vaccinated mice and show that they are HER2-reactive. This data conclusively demonstrates the efficacy of this vaccination strategy in expanding tumor rejection T cells and supports its further evaluation in an ongoing Phase II trial (NCT03632941). The workflow used to identify and clone expanded, tumor specific T cells has broad potential applications across tumor types and treatment platforms. Citation Format: Erika J. Crosby, Chaitanya Acharya, Christopher Rabiola, William J. Muller, Lewis A. Chodosh, Gloria Broadwater, Jonathan Shepherd, Daniel Hollern, Xiaping He, Charles M. Perou, Benjamin K. Ashby, Benjamin G. Vincent, Michael A. Morse, Herbert K. Lyerly, Zachary C. Hartman. Stimulation and expansion of oncogene-reactive tumor infiltrating T cells through combined Ad-HER2Δ16 vaccination and anti-PD1 enable anti-tumor responses against established HER2 BC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 904.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".