Abstract 693: Development of a protein-based functional assay for the determination of BRCA1 carrier status
Notice bibliographique
Résumé
Abstract While most breast cancers are sporadic, 5-10% are hereditary and attributable to the presence of a mutation in the breast cancer associated gene 1 (BRCA1). The BRCA1 gene encodes a tumour suppressor with several well described roles in maintaining genomic integrity, such as cell cycle checkpoint control and DNA repair. BRCA1 is also known to play a role in the differentiation of breast epithelial cells. Given the high risk of cancer development associated with BRCA1 mutation carriers, it is important that they be identified early and accurately. Previous work in our lab developed a novel functional assay to predict BRCA1 status based on gene expression profiles. That study analyzed EBV-transformed lymphocyte cell lines (LCLs) of BRCA1 mutation carriers (BRCA1+/−) and controls (BRCA1+/+) and found that lymphocytes from BRCA1 carriers could be distinguished with high fidelity (∼90% accuracy) from individuals with two wild type copies of the gene. Interestingly, it was found that many of the genes used to distinguish BRCA1 mutation carriers were also markers of differentiation in blood cells. We are now using this information to develop a flow cytometric protein-based biomarker assay. To do this, our transcriptome microarray data was analyzed to identify genes with strong edictability of BRCA1 status, including genes with known roles in lymphocyte differentiation such as TBX21 and CXCR3. These genes were then used to model the assay in-silico using a training set of data comprised of 53 LCLs. The model was designed by selecting genes both up- and down-regulated in BRCA1 carriers, placing them in pairs and looking at the ratio of their expression values. This model was subsequently validated using a test data set comprising 16 LCLs. Overall accuracy of the model was 94%, correctly predicting 54/59 LCLs. Based on in-silico modelling, we chose six antibodies to develop the assay. To identify other candidate proteins, LCL lysates were analyzed by mass spectrometry (MS). The results identified several other candidate proteins for assay development including IgG1, IgD, BCR and MX1. Interestingly, as was the case in our transcriptome analysis, these MS identified proteins also suggested that BRCA1 mutation carriers displayed defects in cellular differentiation. This was indicated by the higher expression of surface markers in responding to less differentiated B-lymphocytes in comparison with controls. Ultimately, the assay will be applied to fresh patient blood samples with the expectation that it will become an assay that can be implemented in a clinical setting to provide accurate identification of BRCA1 mutation carriers and aid in disease management. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 693. doi:1538-7445.AM2012-693
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».