MECHANISMS OF ENDOCRINOLOGY: Cell cycle regulation in adrenocortical carcinoma
Bibliographic record
Abstract
Adrenocortical carcinomas (ACCs) are rather rare endocrine tumors that often have a poor prognosis. The reduced survival rate associated with these tumors is due to their aggressive biological behavior, combined with the scarcity of effective treatment options that are currently available. The recent identification of the genomic alterations present in ACC have provided further molecular mechanisms to develop consistent strategies for the diagnosis, prevention of progression and treatment of advanced ACCs. Taken together, molecular and genomic advances could be leading the way to develop personalized medicine in ACCs similarly to similar developments in lung or breast cancers. In this review, we focused our attention to systematically compile and summarize the alterations in the cell cycle regulation that were described so far in ACC as they are known to play a crucial role in cell differentiation and growth. We have divided the analysis according to the major transition phases of the cell cycle, G1 to S and G2 to M. We have analyzed the most extensively studied checkpoints: the p53/Rb1 pathway, CDC2/cyclin B and topoisomerases (TOPs). We reached the conclusion that the most important alterations having a potential application in clinical practice are the ones related to p53/Rb1 and TOP 2. We also present a brief description of on-going clinical trials based on molecular alterations in ACC. The drugs have targeted the insulin-like growth factor receptor 1, TOP 2, polo-like kinase1, cyclin-dependent kinase inhibitors, p53 reactivation and CDC25.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".