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 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".