Abstract 1435: Phenotype:Genotype correlations of p53 mutation carriers: The 20-year Toronto experience
Notice bibliographique
Résumé
Abstract Introduction: Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with germline mutations of the p53 gene. LFS is associated with early age of onset of breast cancer in women, childhood sarcomas and other malignancies. Previous studies have suggested potential correlations between p53 mutation type and clinical cancer phenotypes. For example, missense mutations within the DNA binding loop were found to be associated with higher prevalence of brain tumors and breast cancer whereas missense mutations outside the DNA binding loops were associated with higher prevalence of adrenocortical carcinoma. Through our Cancer Genetics Program (CGP), we have ascertained and conducted p53 mutation analysis on LFS patients/families from 1992 to the present. This represents one of the largest collections in the world. Here we set out to collate and characterize potentially novel genotype:phenotype associations from this database. Methods: TissueMetrix (Artificial Intelligence in Medicine (AIM), Toronto) was used to develop the CGP database. Clincal-pathologic data including family pedigrees, age of tumor onset, tumor type (confirmed with pathology reports where available were collected. From 1992-1998, Sanger sequencing of p53 encompassing the DNA binding domain (exons 5-8) was performed. Since 1998, the entire coding region and additional 50-100 bases spanning intron/exon splice sites have been sequenced and since 2007, MLPA testing has been performed to look for allelic deletion/duplication. Results: p53 mutations have been identified in 110 individuals, of whom 61 (55%) are less than 18years of age. Missense mutations the most common sequence alteration (83/110= 75%). The most commonly observed mutation was the dominant-negative Arg248Gln substitution (n=12). Monoallelic complete or partial gene deletion was observed in 10 cases, several of whom exhibited a non-cancer, developmental delay phenotype. The most common cancer were brain tumors (n=21), of which 10 were choroid plexus carcinomas. ADCC, breast cancer and sarcomas were commonly observed; non-classical LFS tumors, including neuroblastoma, papillary carcinoma of the thyroid and Wilms tumor are also seen. Conclusion: Our program, being situated in a pediatric tertiary centre has an inherent ascertainment bias which likely explains the high frequency of young p53 mutation carriers, as well as a disproportionate prevalence of childhood cancers. Nevertheless, unique correlations emerge from this rich resource including that of p53 deletion and hypotonia/developmental delay, as well as de novo and novel non-coding mutations. These correlations provide the foundation on which to develop practical genetic screening and clinical surveillance approaches to at-risk children and young adults. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1435. doi:1538-7445.AM2012-1435
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».