Smac mimetics and oncolytic viruses synergize in driving anticancer T-cell responses through complementary mechanisms
Bibliographic record
Abstract
Abstract Second mitochondrial activator of caspase (Smac)-mimetic compounds and oncolytic viruses were developed to kill cancer cells directly. However, Smac-mimetic compound and oncolytic virus therapies also modulate host immune responses in ways we hypothesized would complement one another in promoting anticancer T-cell immunity. We show that Smac-mimetic compound and oncolytic virus therapies synergize in driving CD8+ T-cell responses toward tumors through distinct activities. Smac-mimetic compound treatment with LCL161 reinvigorates exhausted CD8+ T cells within immunosuppressed tumors by targeting tumor-associated macrophages for M1-like polarization. Oncolytic virus treatment with vesicular stomatitis virus (VSVΔM51) promotes CD8+ T-cell accumulation within tumors and CD8+ T-cell activation within the tumor-draining lymph node. When combined, LCL161 and VSVΔM51 therapy engenders CD8+ T-cell-mediated tumor control in several aggressive mouse models of cancer. Smac-mimetic compound and oncolytic virus therapies are both in clinical development and their combination therapy represents a promising approach for promoting anticancer T-cell immunity.
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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".