Abstract B18: Genomic analysis of pancreatic ductal adenocarcinoma.
Notice bibliographique
Résumé
Pancreatic cancer is the fifth leading cause of cancer deaths. Five-year survival rate is In our initial screen, whole-exome sequencing of 33 primary PDAC tumors and matched controls has been performed on the Illumina HiSeq 2000. Sequence alignment and variant calling have been performed using Novoalign and GATK, respectively. After manual review and validation on the Ion Torrent platform, we have identified 648 somatic mutations, 471 of which are non-silent mutations that impact 444 genes. Our results confirm several known mutations in PDAC such as KRAS, p53 and SMAD4. However, their mutation frequencies are lower than expected due to tumor cellularity. We have also screened for copy number alterations (CNAs) using Illumina Omni1-Quad BeadChip. Analysis was performed using Genome Studio, KSseg and PennCNV. In the 33 primary tumors, a median of 90 regions with copy number gain, copy number loss, or copy-neutral LOH have been detected per sample. Median genomic lengths are 19Mb and 20Mb in regions with copy number gain and loss, respectively. Annotation of the altered regions has identified 9152 protein-coding genes, miRNA and non-coding RNA that are altered in 4 or more tumors. To identify the pathways that contribute to PDAC, we have analyzed the genes with somatic mutations or CNAs by means of a functional interaction (FI) network. The FI network consists of curated pathways from Reactome and other databases and a high confidence set of functional interactions predicted by machine learning techniques. A PDAC-specific subnetwork is constructed by projecting the altered genes onto the FI network, and subsequently analyzed by a community clustering algorithm to identify network modules. These modules have been identified as KRAS, p53, TGFβ, Hedgehog, Integrin, Cadherin, Wnt, Rho GTPase and G-protein signaling pathways. While our effort in identifying driver mutations is ongoing, our initial screen has identified candidate genes that will be targeted for deep sequencing in all primary tumors. We will continue to perform whole-exome sequencing of other primary tumors along with xenografts derived from some of the primaries and cell lines derived from some of the xenografts. In addition, whole-genome sequencing of selected specimens is being performed to complement the exome data. The wealth of data will help to characterize the genomic abnormalities in PDAC. Citation Format: Christina K. Yung, Christine Ouellete, Lee Timms, Michelle Sam, Kimberly Begley, Thomas J. Hudson, John D. McPherson, Lincoln D. Stein, Timothy Beck, Lakshmi Muthuswamy, Richard De Borja, Carson Holt, Rob Denroche, Fouad Yousif, Zheng Zha, Niloofar Arshadi. Genomic analysis of pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr B18.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».