Abstract A1-06: Recurrent mutations of hormone-positive breast cancer and association with outcome
Notice bibliographique
Résumé
Abstract Background: Relationships between recurrent somatic mutations and outcome in estrogen receptor positive (ER+) breast cancer have not been extensively studied as the original discovery efforts were from either heterogeneously treated patients or follow up was too brief. Targeted massively parallel sequencing (MPS) analysis was therefore conducted on DNA extracted from archived formalin-fixed breast primaries from a cohort of over 600 patents from British Columbia treated with five years of adjuvant tamoxifen monotherapy and followed for over 10 years (Nielsen et al CCR 16:5222, 2010). Methods: Genes were selected for targeted sequencing by meta-analysis of five large-scale breast cancer sequencing studies and manual review of breast cancer literature. In total, 83 genes were identified and 3286 probes were designed to tile across all known exons. Minimum starting input DNA was 50ng (mean=189.1ng). Illumina sequencing libraries were constructed, indexed, pooled, and enriched for target sequences by hybrid-capture followed by paired-end 100bp reads. The Genome Modeling System was used to perform single-tumor somatic variant prediction. Variant calls were filtered to include only targeted regions and exclude variants with global minor allele frequencies greater than 0.1% in unmatched non-tumor samples from 1000 genomes, NHLBI exome, and TCGA datasets. Kaplan-Meier univariate and multivariate survival analyses (including mutation status, clinical features and intrinsic subtype by qPCR) were performed for breast-cancer-specific and relapse free survival. Results: A total of 625 samples met minimum quality controls of 80% targeted space covered at 20X or greater. On average, each sample had 332M aligned bases and a mean coverage of 134.3X. In total, 3,628 variants were identified including 2,066 missense, 188 nonsense, and 298 frame shift insertions or deletions. Novel hot spots for recurrent mutation were identified in several genes including a splice site mutation in CBFB. Results indicate significant associations between mutation status and improved survival for MAP3K1, ERBB3, ARID1B, PIK3CA and SMG1 or worse survival for DDR1, NF1, FOXC1 and TP53. Six Y537S/C, two E380Q and 5 potentially novel ligand-binding-domain mutations were identified in ESR1. Such mutations were recently reported to be associated with resistance to hormone therapy but were discovered here in as many as 2.1% of pre-treatment samples. Analysis will be presented regarding the use of relapse events to differentiate passenger from driver events. Conclusion: Multiple recurrently mutated genes have both positive and negative associations with prognosis in tamoxifen monotherapy treated breast cancer populations. Associations with poor outcome suggest that DDR1, NF1 and FOXC1 are high priorities for pharmacological interventions. Citation Format: Obi L. Griffith, Malachi Griffith, Nicholas C. Spies, Jingqin Luo, Jasreet Hundal, Christopher A. Miller, David E. Larson, Robert Fulton, Shuzhen Liu, Samuel Leung, Richard K. Wilson, Torsten O. Nielsen, Elaine R. Mardis, Matthew J. Ellis. Recurrent mutations of hormone-positive breast cancer and association with outcome. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-06.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».