Clonal and non-clonal chromosome aberrations and genome variation and aberration
Bibliographic record
Abstract
The theoretical view that genome aberrations rather than gene mutations cause a majority of cancers has gained increasing support from recent experimental data. Genetic aberration at the chromosome level is a key aspect of genome aberration and the systematic definition of chromosomal aberrations with their impact on genome variation and cancer genome evolution is of great importance. However, traditionally, efforts have focused on recurrent clonal chromosome aberrations (CCAs). The significance of stochastic non-clonal chromosome aberrations (NCCAs) is discussed in this paper with emphasis on the simple types of NCCAs that have until recently been considered "non-significant background". Comparison of various subtypes of transitional and late-stage CCAs with simple and complex types of NCCAs has uncovered a dynamic relationship among NCCAs, CCAs, overall genomic instability, and karyotypic evolution, as well as the stochastic nature of cancer evolution. Here, we review concepts and methodologies to measure NCCAs and discuss the possible causative mechanism and consequences of NCCAs. This study raises challenging questions regarding the concept of cancer evolution driven by stochastic chromosomal aberration mediated genome irregularities that could have repercussions reaching far beyond cancer and organismal genomes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".