Limiting surveillance in individuals with the Palestinian <i>TP53</i> p. R181C founder variant—is it too soon to draw conclusions?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».