What Is Apoptosis and Why Is It Inhibited by the Most Important Tumor Suppressor (p53)?
Bibliographic record
Abstract
Anticancer strategies targeting the DNA damage response are largely centered on a number of false hypotheses. For example, engaging apoptosis in solid tumors is universally assumed to represent a tumor suppression response. But what is “apoptosis”, really? Time-lapse microscopy and other single-cell assays have revealed that engaging apoptosis in solid tumor cells is accompanied by anastasis, the homeostatic process of cell recovery from late stages of apoptosis, even after the formation of apoptotic bodies. Furthermore, apoptotic cells secrete a variety of prosurvival factors that contribute to overall tumor repopulation. Not surprisingly, numerous clinical studies reported since the 1990s have demonstrated that increased apoptosis in solid tumors is associated with cancer aggressiveness rather than representing a favorable clinical outcome. Another major false hypothesis pertains to the role of wild-type p53 in regulating apoptosis. Several recent articles addressing the challenges that have been encountered in implementing p53-based cancer therapies assume that p53 is pro-apoptotic. This assumption, which has become an almost indisputable fact, is shocking given that by mid-2000s it was already well established that p53 serves to inhibit apoptosis through upregulating ~40 anti-apoptotic proteins. The complexity of cancer cell response to therapeutic agents is discussed herein with a focus on the significance of p53-p21WAF1 signaling in suppressing the apoptosis–anastasis tumor repopulation pathway.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".