Targeted BiTE Expression by an Oncolytic Vector Augments Therapeutic Efficacy Against Solid Tumors
Bibliographic record
Abstract
Abstract Purpose: Immunotherapy with bispecific T-cell engagers has achieved striking success against hematologic malignancies, but efficacy against solid tumors has been limited. We hypothesized that oncolytic measles viruses encoding bispecific T-cell engagers (MV-BiTEs) represent a safe and effective treatment against solid tumors through local BiTE expression, direct tumor cell lysis and in situ tumor vaccination. Experimental Design: To test this hypothesis, we generated MV-BiTEs from the Edmonston B vaccine strain to target two model antigens. Replicative and oncolytic potential were assessed by infection and cell viability assays, respectively. Functionality of virus-derived BiTEs was tested in vitro by complementary binding and cytotoxicity assays. In vivo efficacy of MV-BiTE was investigated using both syngeneic and xenograft mouse models of solid cancers. Results: We verified secretion of functional BiTE antibodies by MV-BiTE-infected cells. Further, we demonstrated therapeutic efficacy of MV-BiTE against established tumors in fully immunocompetent mice. MV-BiTE efficacy was associated with increased intratumoral T-cell infiltration and induction of protective antitumor immunity. In addition, we showed therapeutic efficacy of MV-BiTE in xenograft models of patient-derived primary colorectal carcinoma spheroids with transfer of peripheral blood mononuclear cells. Conclusions: MV-BiTE treatment was effective in two distinct models of solid tumors without signs of toxicity. This provides strong evidence for therapeutic benefits of tumor-targeted BiTE expression by oncolytic MV. Thus, this study represents proof of concept for an effective strategy to treat solid tumors with BiTEs. Clin Cancer Res; 24(9); 2128–37. ©2018 AACR.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".