BiTE-secreting T cells rationally combine with PD-1 blockade and vaccine boosting to reshape antitumor immunity in ovarian cancer
Bibliographic record
Abstract
Despite some clinical success, ovarian cancer (OC) patients rarely achieve durable benefit from current immunotherapies, suggesting a need for strategies that improve OC immune recognition. We previously reported that engineered T cells secreting folate receptor alpha (FRα)-targeted bispecific T cell engagers (FR-B T cells) elicit robust antitumor responses in OC, in part by engaging endogenous T cells. Here, we use clinical OC specimens and preclinical OC to evaluate FR-B T cells combined with PD-1 blockade. Assessing the tumor microenvironment during acute and prolonged FR-B T cell + anti-PD-1 responses revealed broad immune cell engagement/reorganization. Early CD8+ T cell-driven responses and myeloid cell influx were followed by accumulation of CXCL13-producing macrophages, activated B cells, and effector memory CD4+ T cells with durable response, hallmarks that were diminished with progressive disease. Resistant OC (characterized by FRα loss and metabolic reprogramming) emerged at disease relapse, suggesting a need to target additional vulnerabilities to extend responses. As FR-B T cells promoted epitope spreading beyond FRα, we employed a booster vaccine to enhance antitumor immunity, improving OC control. Our findings point to rationally combining FR-B T cells with PD-1 blockade in OC and an opportunity to apply personalized cancer vaccines to limit OC relapse.
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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".