Abstract P5-03-07: Prevalence of Pathogenic Variants in Cancer Predisposition Genes in Women with Young Onset Breast Cancer
Notice bibliographique
Résumé
Abstract Introduction: Approximately 5% of breast cancers are diagnosed in women 40 years of age or younger. Known risk factors for young-onset breast cancer are few and can only account for a very small proportion of cases. In this study, we evaluated the contribution of mutations in 24 breast cancer predisposition genes in unselected Canadian women diagnosed with breast cancer at age 40 or younger. Methods: This study is a sub-study of the larger Reducing the bUrden of Breast cancer in Young women (RUBY) Study. In the RUBY study, women diagnosed with breast cancer at the age of 40 years or younger are recruited at the time of diagnosis from 33 centres across Canada. Participants in RUBY provided detailed demographic and clinical data, in addition to provision of serial biospecimens. Participants could elect to consent into the genetics substudy, and have genetic testing performed for pathogenic variants in 24 breast cancer predisposition genes, including ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, FAM175A, MLH1, MRE11, MSH2, MSH6, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, RECQL, STK11, TP53 and XRCC2. Sequencing was performed and all potentially pathogenic variants were confirmed with conventional Sanger sequencing. Pathogenic and likely pathogenic mutations were reported for all 24 genes. CanRisk scores for likelihood of having a pathogenic variant in 8 cancer predisposition genes (BRCA1 BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D, and BRIP1) were generated for each participant. Results: 714 women consented and genetic testing was performed on the blood samples provided as a component of the RUBY study. The mean age of the participants was 35.8 years (range 23-40 years), and the mean CanRisk score was 13.7 (range 2.3-98.0). Overall, 150 pathogenic mutations (21.0%) were detected in 147 women (three participants had mutations in two genes). The most common pathogenic variants detected were in BRCA1 (48), BRCA2 (40), CHEK2 (24), ATM (10), and PALB2 (9), representing 87.3% of all pathogenic variants identified. The mean CanRisk score was 28.8% (range 3.2-98.0%) for those identified with a pathogenic variant compared to 9.6% (range 1.0-88.9%) for those with a negative result (p < 0.0001). The prevalence of pathogenic variants was 32.9% for women age 20-30 years, 27.5% for 31-35 years, and 16.7% for 36-40 years. Conclusions: Twenty-one percent of women with breast cancer at age 40 or younger had a pathogenic variant in a breast-cancer predisposition gene. The great majority of these pathogenic variants were found in genes (BRCA1, BRCA2, CHEK2, PALB2) for which there are validated breast cancer treatment recommendations. All women with young-onset breast cancer should be offered germline genetic testing at the time of breast cancer diagnosis to make informed surgical and medical treatment decisions. Citation Format: Kelly Metcalfe, May Lynn Quan, Steven Narod, Ellen Warner, Christine Friedenreich, Nancy Baxter, Aletta J. Poll, Mohammad Akbari. Prevalence of Pathogenic Variants in Cancer Predisposition Genes in Women with Young Onset Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-03-07.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».