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Enregistrement W1971981156 · doi:10.1158/1538-7445.panca2012-a15

Abstract A15: Exome sequencing identifies candidate tumor suppressor genes in familial pancreatic cancer.

2012· article· en· W1971981156 sur OpenAlexaffabout
Robert C. Grant, Timothy A. Beck, Lakshmi Muthuswamy, Ayelet Borgida, Spring Holter, Stefano Serra, John Peter McPherson, Steven Gallinger

Notice bibliographique

RevueGenetics · 2012
Typearticle
Langueen
DomaineMedicine
ThématiquePancreatic and Hepatic Oncology Research
Établissements canadiensUniversity Health NetworkPancreas Centre (Canada)Ontario Institute for Cancer ResearchUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésExome sequencingSuppressorCancerGenePancreatic cancerExomeGeneticsTumor suppressor geneCandidate geneCancer researchMedicineBiologyComputational biologyMutationCarcinogenesis

Résumé

récupéré en direct d'OpenAlex

Five to ten percent of pancreatic cancer (PC) clusters within families. Epidemiologic studies suggest that Familial Pancreas Cancer (FPC) is caused by highly penetrant germline mutations, but less than 20% of these genes have been identified. Characterizing the remaining genetic basis of FPC should improve outcomes through molecularly tailored prevention, screening and treatment protocols. Exome sequencing (ES) is a powerful new approach for detecting genetic causes of disease. However, the causal mutation is often indistinguishable from the hundreds of other rare nonsynonymous variants (RNV) detected by germline ES. In general, most familial cancer syndromes are caused by an inherited mutation that inactivates one allele of a tumor suppressor gene (TSG); the somatic inactivation of the second allele, i.e., loss of heterozygosity (LOH), initiates tumourigenesis. To increase the specificity of ES, we combined somatic and germline ES to identify candidate TSG (cTSG) in FPC with germline RNV and LOH. Patients with available snap frozen tumors were selected from the Ontario Pancreas Cancer Study. Histologic inspection of the snap frozen tumors determined their composition to be over 70% pancreatic ductal adenocarcinoma cells. DNA was extracted from snap frozen tumors and peripheral blood. Exomes were enriched using the Agilent ICGC or Illumina TruSeq kits, and sequenced with the Illumina Genome Analyzer IIx. Novoalign aligned sequencing reads, and SAMtools and Picard were used to process them further. The Genome Analysis Tool Kit detected variants. ANNOVAR and custom scripts annotated variants. We defined an RNV as: i) present in less than 0.1% of alleles in the 1000 Genomes Project March 2012 dataset, the NHLBI ESP5400 dataset, and dbSNP135; and ii) a missense, nonsense or splice site single-nucleotide variant, or an exonic indel. cTSG were defined as genes containing a germline heterozygous RNV and a second somatic intragenic RNV. Three FPC probands underwent somatic and germline ES. Over 90% of target bases were covered by at least 10 sequencing reads in all samples. FPC-1 was a 67-year-old male with PC whose maternal aunt and grandmother had PC. FPC-1 carried 1032 germline RNV and 11 somatic RNV. Only 2 somatic RNV were in genes with RNV in 24 PC sequenced by Jones et al. (Jones, Science, 2008). No KRAS or TP53 mutations were detected and no cTSG was identified. FPC-2 was a 55-year-old man with PC whose sister had PC at age 42 and mother also had PC. FPC2 carried 618 germline RNV and 18 somatic RNV. 5 of the somatic RNV were in genes mutated in Jones et al. This patient’s tumour has a KRAS and TP53 mutation. We identified a cTSG that may repress the mobilization of transposable elements. FPC-3 was a 20-yearold woman with PC whose maternal aunt had PC at 59. FPC-3 carried 418 germline RNV and 15 somatic RNV, 1 of which was in genes previously mutated in Jones et al. No mutations in KRAS or TP53 were detected. Here we identified a cTSG that is an ion channel strongly and selectively expressed in the pancreas. We are performing copy number analysis on blood and tumor samples to improve our sensitivity to detect LOH caused by large deletions. We are sequencing the coding region of the two cTSG we identified in 50 unrelated FPC probands to assess their contribution to FPC. These results will be ready for presentation at the AACR PC meeting. We have identified two cTSGs in FPC and characterized the somatic genetic landscape of three FPC, which seem to differ from sporadic PC. Recent studies suggest that FPC is genetically heterogeneous. Our new approach combining somatic and germline ES improves specificity, allowing the identification of causal germline mutations even when a gene is infrequently mutated in FPC. Citation Format: Robert C. Grant, Timothy Beck, Lakshmi Muthuswamy, Ayelet Borgida, Spring Holter, Stefano Serra, John McPherson, Steven Gallinger. Exome sequencing identifies candidate tumor suppressor genes in familial pancreatic cancer. [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 A15.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,112
Score d'incertitude au seuil0,680

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,055
Tête enseignante GPT0,357
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission2
Résumé présentoui

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