Abstract 3133: Identification and functional validation of novel genetically-linked breast cancer targets through pooled gain-of-function screening.
Notice bibliographique
Résumé
Abstract Breast cancer is one of the most common cancer types, with greater than 450,000 deaths reported per year worldwide. Through genome wide sequencing efforts, multiple genetic alterations have been identified, including mutations and amplifications in genes such as v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (ERBB2), GATA binding protein 3 (GATA3), phosphatidylinositol 3-kinase alpha catalytic subunit (PIK3CA) as well as novel genomic rearrangements such as the recently identified MAGI3-AKT3 fusion. Now that breast cancer can be characterized to an unprecedented level, one of the key challenges remaining is to identify and distinguish critical ‘driver’ events responsible for tumor progression, from neutral ‘passenger’ lesions. In order to achieve this, we utilized high resolution aCGH analysis of 50 purified breast cancer samples (made up of Her2+, estrogen receptor positive (ER+) and triple negative tumors with variable responses to SOC regimens), in combination with a Gain-of-Function transformation screen to identify and validate novel breast targets. 158 genomic regions were found to be recurrently amplified, consisting of 759 genes in total. The top 32 focally amplified genes, along with 12 cancer-relevant mutant alleles were prioritized and a library generated utilizing the pTRIPZ-tetracycline regulated inducible lentiviral vector system. These 44 genes were subsequently combined into 16 different target pools (5-13 targets per pool, co-expressing genes that were co-amplified in the same clinical specimen) and evaluated for their ability to transform immortalized breast epithelial MCF10A cells (both wild-type and p53 -/- cells). Through this screening approach, p21-activated kinase 1 (PAK1) was identified, whose kinase activity was required to robustly transform MCF10A cells through regulating multiple signalling pathways including MAPK. Several other putative oncogenes were also identified and will be presented here, including the glycosyltransferse asparagine-linked glycosylation 8 (ALG8). Interestingly, PAK1 and ALG8 are co-amplified in both breast (8%) and ovarian cancers (11%). Our target validation studies have suggested that ALG8 can support PAK1-induced transformation, as dramatic suppression of soft-agar colony growth was seen in co-amplified breast cancer cell lines upon combined siRNA treatment to both targets. Thus, this combined high resolution aCGH profiling and functional screening approach has enabled the successful identification of novel oncogenic targets in breast cancer. Citation Format: Krishna Vasudevan, Axel Hernandez, Zhongwu Lai, Yonghong Xiao, Nin Guan, Carolyn Hardy, Robert Godin, Christopher Denz, Minwei Ye, Elizabeth Lenkiewicz, Stephanie Savage, Michael T. Barrett, Donna Prunkard, Peter Rabinovitch, Mark Basik, Ewa Przybytkowski, Kevin Webster, Michael Zinda, Emma-Louise Jenkins. Identification and functional validation of novel genetically-linked breast cancer targets through pooled gain-of-function screening. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3133. doi:10.1158/1538-7445.AM2013-3133
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».