A Bispecific γδ T-cell Engager Targeting EGFR Activates a Potent Vγ9Vδ2 T cell–Mediated Immune Response against EGFR-Expressing Tumors
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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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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it