Apoptosis and cancer: mutations within caspase genes
Bibliographic record
Abstract
The inactivation of programmed cell death has profound effects not only on the development but also on the overall integrity of multicellular organisms. Beside developmental abnormalities, it may lead to tumorigenesis, autoimmunity, and other serious health problems. Deregulated apoptosis may also be the leading cause of cancer therapy chemoresistance. Caspase family of cysteinyl-proteases plays the key role in the initiation and execution of programmed cell death. This review gives an overview of the role of caspases, their natural modulators like IAPs, FLIPs, and Smac/Diablo in apoptosis and upon inactivation, and also in cancer development. Besides describing the basic mechanisms governing programmed cell death, a large part of this review is dedicated to previous studies that were focused on screening tumours for mutations within caspase genes as well as their regulators. The last part of this review discusses several emerging treatments that involve modulation of caspases and their regulators. Thus, we also highlight caspase cascade modulating experimental anticancer drugs like cFLIP-antagonist CDDO-Me; cIAP1 antagonists OSU-03012 and ME-BS; and XIAP small molecule antagonists 1396-11, 1396-12, 1396-28, triptolide, AEG35156, survivin/Hsp90 antagonist shephedrin, and some of the direct activators of procaspase-3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.003 | 0.002 |
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".