Limiting surveillance in individuals with the Palestinian <i>TP53</i> p. R181C founder variant—is it too soon to draw conclusions?
Bibliographic record
Abstract
In 1969, Drs Li and Fraumeni published the first report of a severe autosomal dominant cancer predisposition syndrome, which subsequently acquired the moniker Li–Fraumeni syndrome, describing families in which children with rhabdomyosarcoma had an unusually high frequency of different cancer types among their siblings and first- and second-degree adult relatives.1 Some 30 years later, it was discovered that the Li–Fraumeni syndrome is most commonly caused by germline pathogenic or likely pathogenic variants in the TP53 tumor suppressor gene.2 Li–Fraumeni syndrome is associated with a likelihood of cancer onset reaching 40% by age 20 years and exceeding 90% by age 90 years, with more than 50% experiencing multiple tumors. The clinical definitions of Li–Fraumeni syndrome have evolved over the years, but the current revised Chompret criteria are still considered the most appropriate to guide decisions about TP53 testing.3 The most common Li–Fraumeni syndrome–associated cancers include adrenocortical carcinomas, soft tissue and bone sarcomas, brain tumors, and early-onset breast cancer; however, a diverse range of other tumors have also been described.3 Some cancers including adrenocortical carcinoma or choroid plexus carcinoma are particularly overrepresented in Li–Fraumeni syndrome in which at least 50% of patients carry germline TP53 pathogenic variants.3,4 When a diagnosis of Li–Fraumeni syndrome is made, guidelines recommend offering genetic counseling to the family and tumor surveillance starting at birth or on confirmation of the presence of a germline TP53 pathogenic variant. Surveillance for early tumor detection includes annual whole-body and dedicated brain magnetic resonance imaging and pelvic-abdominal ultrasounds with careful complete physical examination initially every 3 months with some consideration for extending this interval in older individuals.5,6 This protocol has been shown to reduce mortality in individuals with Li–Fraumeni syndrome.7 Since the first descriptions of Li–Fraumeni syndrome, a more diverse picture has emerged with subsequent attempts to outline more comprehensive phenotype-genotype correlations and account for variable phenotypic penetrance. To account for this among different families, the term hereditary germline TP53-related cancer predisposition syndrome6 including classic Li–Fraumeni syndrome and attenuated Li–Fraumeni syndrome has been proposed.8 The diversity in genotype-phenotype correlations is in part a result of finding TP53 variants more easily with the accelerated access to genome sequencing and gene panels used clinically. In the context of adult oncology, this is most notable for breast cancer gene panels on which inclusion of TP53 is now standard.9 The increased use of these gene panels in breast cancer patients might lead to the detection of a previously unknown TP53 variant found in a proband with early-onset breast cancer, with unaffected family members. This raises the question of whether all Li–Fraumeni syndrome individuals should be treated within the same one-size-fits-all surveillance protocol, whether the mode of ascertainment might affect or bias risk predictions for other family members, or whether it is possible to tailor surveillance depending on the specific variant and its phenotypic features.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".