Reply to Li and Colleagues
Notice bibliographique
Résumé
We thank Dr Li and colleagues (1) for their correspondence in which the following point related to our publication entitled “Heterozygous BRCA1 and BRCA2 and mismatch repair gene pathogenic variants in children and adolescents with cancer” is raised: The frequency of BRCA2 pathogenic variant (PV) carriers in the population (including controls) decreases with age, due to the primarily cancer related death of carriers. This factor leads to an overestimation of the childhood cancer risk of BRCA2 PV carriers in the presented analysis. This is a valid argument. However, the use of an extreme phenotype case–control design (eg, by using healthy elderly controls to search for cancer associated genes) has been successfully employed to maximize power (2). Nevertheless, our literature review and combined meta-analysis and case–control study has several methodological limitations acknowledged in the discussion. Therefore, the analysis represents an early step, and reported findings require independent validation using a more appropriate design (including matched controls as well as identical pipelines to call PVs in cases and controls). In addition to our findings that were at least in part confirmed in a validation cohort and a supplementary analysis, there is growing evidence from childhood cancer sequencing studies suggesting that PVs in genes like BRCA1 and BRCA2, as well as other adult-onset cancer predisposition genes, represent (low penetrance) cancer risk alleles in children and adolescents (3-10). Recent results from integrative somatic-germline mutational signature (SBS3/BRCAness) analyses (3) as well as functional analyses (7) support this notion. Despite several limitations, our findings that require confirmation employing a more sophisticated design agree with other studies providing growing evidence that suggests that variants in BRCA2 and other adult-onset cancer predisposition genes represent low-penetrance cancer risk alleles in children and adolescents. No new data were generated or analyzed for this response. Christian Kratz, MD (Conceptualization; Writing – original draft); Dmitrii Smirnov, MS (Writing – review & editing); Robert Autry, PhD (Writing – review & editing); Natalie Jäger, PhD (Writing – review & editing); Sebastian M. Waszak, PhD (Writing – review & editing); Anika Großhennig, PhD (Writing – review & editing); Riccardo Berutti, PhD (Writing – review & editing); Mareike Wendorff, PhD (Writing – review & editing); Pierre Hainaut, PhD (Writing – review & editing); Stefan M. Pfister, MD (Writing – review & editing); Holger Prokisch, PhD (Writing – review & editing); Tim Ripperger, MD, PhD (Writing – review & editing); David Malkin, MD (Writing – review & editing) CPK and SMP have been supported by the Deutsche Kinderkrebsstiftung (DKS2019.13) and Bundesministerium für Bildung und Forschung (BMBF) ADDRess (01GM1909A and 01GM1909E). RA is supported by the Everest Centre for Low-Grade Paediatric Brain Tumours (the Brain Tumour Charity, UK; GN-000382). SMW is supported by the Research Council of Norway (187615), the South-Eastern Norway Regional Health Authority, and the University of Oslo. TR has been supported by BMBF MyPred (01GM1911B). DM is supported by grants from the Canadian Institutes for Health Research (FDN-143234) and the Terry Fox Research Institute (TFRI #1081). DS and HP have been supported by BMBF (01GM1906B). None exist. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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 | 0,006 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,034 | 0,054 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,011 |
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 ».