Combinatorial cancer immunotherapy strategies with proapoptotic small-molecule IAP antagonists
Bibliographic record
Abstract
Members of the inhibitor of apoptosis (IAP) family control several critical aspects of innate immunity, cell death, and tumorigenesis. Small molecule antagonists that target specific IAP oncoproteins, primarily cIAP1 and cIAP2, but potentially also XIAP and Livin, modulate distinct immune signal transduction pathways that can lead to an increased sensitivity of tumors cells to cytokine-mediated apoptosis. These antagonists are based on the structure of an endogenous cellular IAP inhibitor called Smac. Smac is normally sequestered within the mitochondria and is released into the cytoplasm upon cell death stimuli, thereby overcoming the anti-apoptotic action of the IAPs. The therapeutic usefulness of recombinant tumoricidal cytokines to treat cancer patients is principally limited due to their unacceptable adverse side effects. Therefore, investigators have sought to develop alternative regimens that do not rely on exogenously delivered death ligands. These approaches include the stimulation of the immune system with oncolytic virus-based agents or Toll-like receptor agonists in combination with Smac mimetics. Similarly, preclinical combination immunotherapy studies reveal that recombinant interferon synergizes with Smac mimetics to kill cancer. This strategy opens up new therapeutic avenues for anti-cancer therapy by modulating specific immune-mediated death pathways employing unique dual-pronged combinatorial approaches.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".