cIAP1/2 Antagonism Induces Antigen-Specific T Cell–Dependent Immunity
Bibliographic record
Abstract
Checkpoint blockade immunotherapy has failed in pancreatic cancer and other poorly responsive tumor types in part due to inadequate T cell priming. Naive T cells can receive costimulation not only via CD28 but also through TNF superfamily receptors that signal via NF-κB. Antagonists of the ubiquitin ligases cellular inhibitor of apoptosis protein (cIAP)1/2, also called second mitochondria-derived activator of caspases (SMAC) mimetics, induce degradation of cIAP1/2 proteins, allowing for the accumulation of NIK and constitutive, ligand-independent activation of alternate NF-κB signaling that mimics costimulation in T cells. In tumor cells, cIAP1/2 antagonists can increase TNF production and TNF-mediated apoptosis; however, pancreatic cancer cells are resistant to cytokine-mediated apoptosis, even in the presence of cIAP1/2 antagonism. Dendritic cell activation is enhanced by cIAP1/2 antagonism in vitro, and intratumoral dendritic cells show higher expression of MHC class II in tumors from cIAP1/2 antagonism-treated mice. In this study, we use in vivo mouse models of syngeneic pancreatic cancer that generate endogenous T cell responses ranging from moderate to poor. Across multiple models, cIAP1/2 antagonism has pleiotropic beneficial effects on antitumor immunity, including direct effects on tumor-specific T cells leading to overall increased activation, increased control of tumor growth in vivo, synergy with multiple immunotherapy modalities, and immunologic memory. In contrast to checkpoint blockade, cIAP1/2 antagonism does not increase intratumoral T cell frequencies. Furthermore, we confirm our previous findings that even poorly immunogenic tumors with a paucity of T cells can experience T cell-dependent antitumor immunity, and we provide transcriptional clues into how these rare T cells coordinate downstream immune responses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".