Abstract S1-02: Prognostic effects of gene mutation in estrogen receptor positive breast cancer
Notice bibliographique
Résumé
Abstract Background: Relationships between recurrent somatic mutations and outcome in estrogen receptor positive (ER+) breast cancer has 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 mutant allele frequencies greater than 0.1% in 1000 genomes or NHLBI exome datasets. Kaplan-Meier analysis and multivariable analysis (clinical features and intrinsic subtype by qPCR) was performed for breast-cancer-specific and relapse free survival. Results: A total of 638 samples met minimum quality controls of 80% targeted space covered at 20X or greater. On average each sample had 332M of aligned bases and a mean coverage of 134.3X. In total 7,159 variants were identified including 3,696 missense, 494 nonsense, and 1,047 frameshift insertions or deletions. Preliminary results indicate significant associations between mutation status and improved survival for PIK3CA, ARID1B, ERBB3, MAP3K1 and GATA3 or worse survival for PTEN, DDR1, TP53 and JAK2. Five Y537N/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 much as 1.9% of pre-treatment samples. Analysis will be presented regarding the use of relapse events to differentiate passenger from driver mutations. Conclusion. Multiple recurrently mutated genes have both positive and negative associations with prognosis in tamoxifen montherapy treated breast cancer populations. Associations with poor outcome suggest that PTEN, DDR1, and JAK2 are high priorities for pharmacological interventions. Table 1: Mutations and survival in ER+ breast cancerGeneP-ValueHazard RatioPTEN0.0022.11PIK3CA0.0070.69DDR10.0082.41ARID1B0.010.57ERBB30.020.36MAP3K10.010.5TP530.041.48GATA30.040.44JAK20.052.1 Citation Format: Obi L Griffith, Malachi Griffith, Jingqin Luo, Jasreet Hundall, Christopher A Miller, David E Larson, Robert Fulton, Richard K Wilson, Shuzhen Liu, Samuel Leung, Torsten O Nielsen, Elaine R Mardis, Matthew J Ellis. Prognostic effects of gene mutation in estrogen receptor positive breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S1-02.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 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 ».