ORIGINAL PAPER The inhibitors of apoptosis (IAPs) as cancer targets
Bibliographic record
Abstract
Abstract Apoptosis has been accepted as a fundamental component in the pathogenesis of cancer, in addition to other human diseases including neurodegeneration, coronary disease and diabetes. The origin of cancer involves deregulated cellular proliferation and the suppression of apoptotic processes, ultimately leading to tumor establishment and growth. Several lines of evidence point toward the IAP family of proteins playing a role in oncogenesis, via their effective suppression of apoptosis. The central mechanisms of IAP apoptotic suppression appear to be through direct caspase and pro-caspase inhibition (primarily caspase 3 and 7) and modulation of, and by, the transcription factor NF-kappaB. Thus, when the IAPs are over-expressed or over-active, as is the case in many cancers, cells are no longer able to die in a physiologically programmed fashion and become increasingly resistant to standard chemo- and radiation therapies. To date several approaches have been taken to target and eliminate IAP function in an attempt to re-establish sensitivity, reduce toxicity, and improve efficacy of cancer treatment. In this review, we address IAP proteins as therapeutic targets for the treatment of cancer and emphasize the importance of novel therapeutic approaches for cancer therapy. Novel This work was supported by funds from the Canadian Institutes of Health Research (CIHR) (to R.G.K). Allison M. Hunter—recipient of
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".