Analysis of Segmental Duplications, Mouse Genome Synteny and Recurrent Cancer-Associated Amplicons in Human Chromosome 6p21–p12
Bibliographic record
Abstract
It has been proposed that regions of microhomology in the human genome could facilitate genomic rearrangements, copy number transitions, and rapid genomic change during tumor progression. To investigate this idea, this study examines the role of repetitive sequence elements, and corresponding syntenic mouse genomic features, in targeting cancer-associated genomic instability of specific regions of the human genome. Automated database-mining algorithms designed to search for frequent copy number transitions and genomic breakpoints were applied to 2 publicly-available online databases and revealed that 6p21-p12 is one of the regions of the human genome most frequently involved in tumor-specific alterations. In these analyses, 6p21-p12 exhibited the highest frequency of genomic amplification in osteosarcomas. Analysis of repetitive elements in regions of homology between human chromosome 6p and the syntenic regions of the mouse genome revealed a strong association between the location of segmental duplications greater than 5 kilobase-pairs and the position of discontinuities at the end of the syntenic region. The presence of clusters of segmental duplications flanking these syntenic regions also correlated with a high frequency of amplification and genomic alteration. Collectively, the experimental findings, in silico analyses, and comparative genomic studies presented here suggest that segmental duplications may facilitate cancer-associated copy number transitions and rearrangements at chromosome 6p21-p12. This process may involve homology-dependent DNA recombination and/or repair, which may also contribute towards the overall plasticity of the human genome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".