A Bispecific γδ T-cell Engager Targeting EGFR Activates a Potent Vγ9Vδ2 T cell–Mediated Immune Response against EGFR-Expressing Tumors
Bibliographic record
Abstract
Vγ9Vδ2 T cells are effector cells with proven antitumor efficacy against a broad range of cancers. This study aimed to assess the antitumor activity and safety of a bispecific antibody directing Vγ9Vδ2 T cells to EGFR-expressing tumors. An EGFR-Vδ2 bispecific T-cell engager (bsTCE) was generated, and its capacity to activate Vγ9Vδ2 T cells and trigger antitumor activity was tested in multiple in vitro, in vivo, and ex vivo models. Studies to explore safety were conducted using cross-reactive surrogate engagers in nonhuman primates (NHP). We found that Vγ9Vδ2 T cells from peripheral blood and tumor specimens of patients with EGFR+ cancers had a distinct immune checkpoint expression profile characterized by low levels of PD-1, LAG-3, and TIM-3. Vγ9Vδ2 T cells could be activated by EGFR-Vδ2 bsTCEs to mediate lysis of various EGFR+ patient-derived tumor samples, and substantial tumor growth inhibition and improved survival were observed in in vivo xenograft mouse models using peripheral blood mononuclear cells (PBMC) as effector cells. EGFR-Vδ2 bsTCEs exerted preferential activity toward EGFR+ tumor cells and induced downstream activation of CD4+ and CD8+ T cells and natural killer (NK) cells without concomitant activation of suppressive regulatory T cells observed with EGFR-CD3 bsTCEs. Administration of fully cross-reactive and half-life extended surrogate engagers to NHPs did not trigger signals in the safety parameters that were assessed. Considering the effector and immune-activating properties of Vγ9Vδ2 T cells, the preclinical efficacy data and acceptable safety profile reported here provide a solid basis for testing EGFR-Vδ2 bsTCEs in patients with EGFR+ malignancies.
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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) | 0.001 | 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 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".