Younger Age of Cancer Initiation Is Associated with Shorter Telomere Length in Li-Fraumeni Syndrome
Bibliographic record
Abstract
Li-Fraumeni syndrome (LFS) is a cancer predisposition syndrome frequently associated with germ line TP53 mutations. Unpredictable and disparate age of cancer onset is a major challenge in the management of LFS. Genetic modifiers, including the MDM2-SNP309 polymorphism, and genetic anticipation have been suggested as plausible explanations for young age of tumor onset, but the molecular mechanisms for these observations are unknown. We speculated that telomere attrition will increase genomic instability and cause earlier tumor onset in successive generations. We analyzed mean telomere length and MDM2-SNP309 polymorphism status in individuals from multiple LFS families and controls. A total of 45 peripheral blood lymphocyte samples were analyzed from 9 LFS families and 15 controls. High rate of MDM2-SNP309 was found in TP53 carriers (P = 0.0003). In children, telomere length was shorter in carriers affected with cancer than in nonaffected carriers and wild-type controls (P < 0.0001). The same pattern was seen in adults (P = 0.002). Within each family, telomere length was shorter in children with cancer than in their nonaffected siblings and their noncarrier parents. Telomere attrition between children and adults was faster in carriers than in controls. Our results support the role of MDM2-SNP309 as a genetic modifier in LFS. The novel finding of accelerated telomere attrition in LFS suggests that telomere length could explain earlier age of onset in successive generations of the same family with identical TP53/MDM2-SNP309 genotypes. Furthermore, telomere shortening could predict genetic anticipation observed in LFS and may serve as the first rational biological marker for clinical monitoring of these patients.
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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 | 0.002 |
| 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) | 0.003 | 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".