Abstract PR01: Functional characterization of breast cancer using pooled lentivirus shRNA screens
Notice bibliographique
Résumé
Abstract Breast cancer is the most common invasive malignancy and the second leading cause of cancer death in U.S. women. Early detection and improved therapy have led to >85% 5-year survival, but still, half of currently affected women will succumb from metastatic disease. This poor outcome reflects our incomplete knowledge of essential genes driving tumor proliferation for each subtype. From genomic data alone, it can be difficult to access which genetic alterations drive pathogenesis because most of these are functionally irrelevant “passenger” mutations. Even if an oncogene or tumor suppressor is identified, these often are not amenable to targeted therapy. However, unanticipated gene/pathway dependencies can arise as a consequence of these genetic abnormalities in cancer cells (“synthetic lethality”). The recent development of lentiviral-based shRNA libraries enables genome-wide screening of cultured cancer cells in a pooled format, facilitating the identification of genes necessary for cancer cell proliferation and survival in cell culture as well as potential synthetic lethal interactions. The overall objectives of this project were to identify subtype-specific targets and synthetic lethal interactions for common mutations in human breast cancer using genome-wide shRNA screens, as well as to compare “functional genomic” and genomic classification schemes. We screened a panel of > 75 breast cancer lines using an 80,000 lentiviral shRNA library targeting 16,000 genes in a pooled format. We identified several classes of gene “dropouts,” including general essential genes, which are required for survival or growth in more than 70% of all cell lines, irrespective of subtype. Using a newly developed scoring algorithm that allows for precise measurement of statistical significance between two groups, we also identified several “subtype-specific” genes, whose essentiality is restricted to a defined subtype. These include well-known HER2 subtype-specific genes, ERBB2, ERBB3, and TFAP2C and luminal subtype-specific gene FOXA1, SPDEF, GATA3, ESR1, and CCDN1 as well as newly identified BRD4 and CHD4. These two genes were further validated as luminal-specific and results to explain their luminal subtype-specificity will be presented. In addition, the unprecedented number of lines allows the identification of synthetic lethal interaction with common breast cancer somatic mutation such as PIK3CA and PTEN. Finally, integration of gene expression, copy number variation, and functional screening results identified potential biomarkers with common genetic changes and functional drivers. Overall, our study represents an extensive functional genetic survey of four major breast cancer subtypes, reveals complexities between genomic and functional genomic results, and uncovers several unexpected gene dependencies and potential novel therapeutic target for each subtype. This abstract is also presented as Poster A22. Citation Format: Richard Marcotte, Azin Sayad, Maliha Haider, Kevin Brown, Troy Ketela, Jason Moffat, Benjamin G. Neel. Functional characterization of breast cancer using pooled lentivirus shRNA screens. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR01.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 ».