P755: The impact of gene-specific guidelines on variant reassessment: Perspectives from a hereditary cancer clinic
Notice bibliographique
Résumé
Introduction: The prevalence of variants of uncertain significance (VUS) leads to significant challenges for clinical management.Stringent classification criteria for variant interpretation set by the American College of Medical Genetics and Genomics (ACMG) rely on genetic population databases among other criteria.However, genetic population databases contain a disproportionately large amount of data from individuals of European ancestry compared to non-European ancestry.Our preliminary analysis aims (i) to manually interpret VUS identified in high-penetrance breast cancer genes (BRCA1, BRCA2, PALB2) among individuals of an underrepresented ancestry utilizing ACMG guidelines and (ii) to explore reclassification potential with hypothetical increased representation in population databases.Methods: We gathered a list of 1648 patients who underwent hereditary cancer germline testing at the UCSF Genomic Medicine Laboratory (GML) from January 2024-October 2024 and filtered to reveal 48 individuals with results that include a VUS in either BRCA1, BRCA2, or PALB2.Retrospective chart review was completed to obtain self-reported ancestry of the individual, indicating whether the patient has an underrepresented ancestry, only a represented (European) ancestry, or if they are of mixed ancestry.From this review, we further analyzed variants identified in individuals of non-European and mixed ancestries alike to examine a more diverse representation of the challenges in variant interpretation.Comparative analysis was performed on each distinct group.Population frequencies and computational data were obtained through gnomAD, PolyPhen-2, Mutation Taster, and CADD.Functional and segregation data were identified through literature search in PubMed, LitVar2, and Google Scholar.ACMG guideline criteria points were systematically applied for each variant without the use of automated tools revealing a classification.Hypothetical strong evidence for benign impact (BS1) was then applied to determine if this would alter classification.Results: Of the 48 individuals with a VUS in a high-penetrance breast cancer gene, 22 were individuals with an underrepresented ancestry (46%; 13 with underrepresented-only ancestry, and 9 with mixed ancestry).Independent evaluation of ACMG variant interpretation criterion for the 22 variants show 0 variants meet criteria for reclassification.Clearly defined criteria for classifying variants are categorized into strong, moderate, and supporting evidence of pathogenicity or benign impact.Population data analysis shows 1 variant meeting moderate evidence for pathogenicity (PM2) with no presence in population databases and the other 20 variants have a low allele frequency in gnomAD and do not meet PM2 due to the autosomal dominant association of the genes.Computational evidence suggesting impact on the gene or gene product was reviewed.This reflected 6 variants with multiple lines of evidence supporting no consequence (BP4), 7 variants with multiple evidence lines that suggest deleterious effects (PP3), and 9 variants with conflicting evidence.Functional studies were available for 2 variants, which support no damaging effect on the protein or gene function (BS3).Segregation data was not available for any of the studied variants.By considering hypothetical BS1 criteria, 9 (41%) of the 22 variants meet classification criteria for a downgrade to either benign or likely benign.Conclusion: This study highlights the obstacle of interpreting VUS and addresses barriers of inequitable population data for underrepresented ancestries.ACMG classification inherently relies on population data from overrepresented groups.An inability to apply population frequency classification criteria (PM2, BS1) due to the disparity of genetic information available for underrepresented ancestries reflects and calls for increased efforts in variant interpretation to ensure access to informative genetic test results across all populations.
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 | 0,046 | 0,104 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,022 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».