Abstract 2787: Exon sequencing of candidate genes for early onset ER negative breast cancer risk reveals novel gene-level associations
Notice bibliographique
Résumé
Abstract Purpose: To test whether genes located in GWAS-identified regions contain exonic variants that show novel gene-level associations with early-onset ER negative breast cancer risk. Background: Only a small portion of the expected genetic contribution to breast cancer risk has been identified by examining the effect of common variants using single-variant analyses. In contrast to single-variant analyses, sequencing studies combined with gene-based tests evaluate the collective effect of all variants in a gene. In regions identified as associated with disease in GWAS, sequencing combined with gene-based tests can potentially provide evidence whether exonic variation contributes to disease risk independently of the common variants already. However, these studies have not yet been widely implemented in breast cancer research. Methods: We selected 19 genes in 11 genomic regions in which previous GWA studies found associations between common variants and breast cancer. We sequenced the exons of each of these genes in 210 cases (women diagnosed with breast cancer before age 45 who did not carry a known BRCA-1 or BRCA-2 mutation and whose tumors were ER-), and 169 age-matched controls. We analyzed these exons using the variance-based SKAT-O test to determine if exonic variation in any of these 19 genes was associated with risk of developing breast cancer. To examine whether any association was driven by the known GWAS variant, we repeated the analysis, controlling for the common variant that single-variant association tests indicated was most strongly associated with breast cancer. To examine whether the association was driven by putative “functional” variants, we also repeated both analyses, including only non-synonymous variants that alter the amino acid sequence. Results: Exonic variants in three genes were collectively associated with breast cancer risk in our population of early-onset cases: ZMIZ1 (p = 2.07•10-4), FGF3 (p = 1.30•10-3), and ANKLE1 (p = 2.80•10-5). In all of these three genes, the associations persisted or even strengthened after adjusting for the top variant identified by GWAS in the region (ZMIZ1 p = 2.41•10-5; FGF3 p = 1.41•10-3; ANKLE1 p = 7.63•10-6). Functional variants in these three genes (restricted only to non-synonomous variants) also were collectively associated with risk (ZMIZ1 p = 2.65•10-5; FGF3 p = 4.52•10-5; ANKLE1 p = 6.52•10-5). These associations are similar after adjusting for the strongest single variant association identified by GWAS in the region (ZMIZ1 p = 5.10•10-4; FGF3 p = 7.74•10-5; ANKLE1 p = 9.42•10-5). Conclusions: Our results are consistent with the hypothesis that the exons of genomic regions identified through GWAS contain additional variants that contribute to ER- breast cancer risk. These results, that need to be independently validated in a larger study, contribute to our understanding of the genetic determinants that influence the risk of ER- early onset breast cancer. Citation Format: Molly Scannell Bryan, Muhammad G. Kibirya, Irene Andrulis, Jenny Chang-Claude, Habibul Ahsan, Brandon Pierce. Exon sequencing of candidate genes for early onset ER negative breast cancer risk reveals novel gene-level associations. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2787. doi:10.1158/1538-7445.AM2015-2787
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».