Chromosomal Instability in Acute Myeloid Leukemia
Bibliographic record
Abstract
Chromosomal instability (CIN), the increasing rate in which cells acquire new chromosomal alterations, is one of the hallmarks of cancer. Many studies highlighted CIN as an important mechanism in the origin, progression, and relapse of acute myeloid leukemia (AML). The ambivalent feature of CIN as a cancer-promoting or cancer-suppressing mechanism might explain the prognostic variability. The latter, however, is described in very few studies. This review highlights the important CIN mechanisms in AML, showing that CIN signatures can occur largely in all the three major AML types (de novo AML, secondary-AML, and therapy-related-AML). CIN features in AML could also be age-related and reflect the heterogeneity of the disease. Although most of these abnormalities show an adverse prognostic value, they also offer a strong new perspective on personalized therapy approaches, which goes beyond assessing CIN in vitro in patient tumor samples to predict prognosis. Current and emerging AML therapies are exploring CIN to improve AML treatment, which includes blocking CIN or increasing CIN beyond the limit threshold to induce cell death. We argue that the characterization of CIN features, not included yet in the routine diagnostic of AML patients, might provide a better stratification of patients and be extended to a more personalized therapeutic approach.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".