Excessive genomic DNA copy number variation in the Li–Fraumeni cancer predisposition syndrome
Bibliographic record
Abstract
DNA copy number variations (CNVs) are a significant and ubiquitous source of inherited human genetic variation. However, the importance of CNVs to cancer susceptibility and tumor progression has not yet been explored. Li-Fraumeni syndrome (LFS) is an autosomal dominantly inherited disorder characterized by a strikingly increased risk of early-onset breast cancer, sarcomas, brain tumors and other neoplasms in individuals harboring germline TP53 mutations. Known genetic determinants of LFS do not fully explain the variable clinical phenotype in affected family members. As part of a wider study of CNVs and cancer, we conducted a genome-wide profile of germline CNVs in LFS families. Here, by examining DNA from a large healthy population and an LFS cohort using high-density oligonucleotide arrays, we show that the number of CNVs per genome is well conserved in the healthy population, but strikingly enriched in these cancer-prone individuals. We found a highly significant increase in CNVs among carriers of germline TP53 mutations with a familial cancer history. Furthermore, we identified a remarkable number of genomic regions in which known cancer-related genes coincide with CNVs, in both LFS families and healthy individuals. Germline CNVs may provide a foundation that enables the more dramatic chromosomal changes characteristic of TP53-related tumors to be established. Our results suggest that screening families predisposed to cancer for CNVs may identify individuals with an abnormally high number of these events.
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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 | 0.002 | 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) | 0.002 | 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".