Abstract 4609: Risk loci in telomere structure and maintenance genes across five cancer types: GAME-ON Consortium
Notice bibliographique
Résumé
Abstract Telomeres are complex structures that cap chromosome ends, protecting them from degradation, double strand breaks and end-to-end fusions. Telomeres are maintained by the enzyme telomerase, which is made up of a reverse transcriptase encoded by TERT, and an RNA template encoded by TERC. The telomere structure itself is composed of proteins encoded by ACD, ACYP2, BICD1, DKC1, DCLRE1B, MPHOSPH6, NAF1, NOLA1, NOLA2, NOLA3, OBFC1, PIK3C3, POT1, RTEL1, TEP1, TERF1, TERF2, TERF21P, TINF2, TNKS/PINX1, TPP1 and ZNF208. Several single nucleotide polymorphisms (SNPs) in the TERT and adjoining CLPTM1L gene region are associated with multiple cancer types, and some are associated with both increased and decreased risks across different cancer types. We systematically characterized the patterns of association between variants in these 25 telomere structure and maintenance genes and risk across five cancer types in the Genetic Association and Mechanisms in Oncology (GAME-ON) consortium. We performed a subset-based meta-analysis (ASSET) of 209,367 directly measured and imputed SNPs, one megabase up- and downstream of these genes, across genome-wide association studies of colorectal (5,100 cases, 4,831 controls), lung (12,160 cases, 16,838 controls), breast (15,748 cases, 18,084 controls), ovarian (4,369 cases, 9,123 controls) and prostate (14,160 cases, 12,724 controls) cancers. Correlations (r2) between SNPs were examined in Haploview using the 1000 Genomes Project CEU population. A total of 87 TERT, 123 TERC and 26 DCLRE1B SNPs were associated with cancer risk at gene-level Bonferroni-corrected p-values of 4.2-7.8×10−6. Patterns of association were similar for prostate and colorectal cancers in DCLRE1B. Much stronger associations were observed in TERT and TERC, with 63 and 24 SNPs reaching genome-wide significance (p<5.0×10−8), respectively. Of these, 9 TERT and 9 TERC SNPs were correlated at r2<0.75. The most strongly associated SNPs in TERT (rs37004, p = 2.6×10−11) and TERC (rs76925190, p = 1.5×10−15) are in regions containing documented risk loci for lung and prostate cancers, respectively. TERT rs37004 (p = 1.2×10−13) and 6 of the other 9 SNPs in TERT were associated only with lung cancer risk. For the other 2 SNPs, there was a suggestion that patterns of risk were opposite for lung and prostate cancers. While TERC rs76925190 was strongly associated with prostate cancer risk (p = 5.4 x10−17), it was also suggestively associated with colorectal cancer risk (p<0.005). This pattern was similar for another 2 of the 9 SNPs, while for the rest, associations were limited to prostate cancer. The complex patterns of association in telomere structure and maintenance genes observed across cancer types may provide insight about the mechanisms through which telomere dysfunction in different tissues influences cancer risk. Citation Format: Sara Karami, Younghun Han, Fredrick R. Schumacher, Zsofia Kote-Jarai, Sara Lindstrom, John S. Witte, Iona Cheng, Shenying Fang, Jiali Han, Peter Kraft, Fengju Song, Rayjean J. Hung, James McKay, Stephen J. Chanock, Mala Pande, Angela Risch, Hongbing Shen, Christopher A. Haiman, Lisa Boardman, Cornelia M. Ulrich, Graham Casey, Ulrike Peters, Nilanjan Chatterjee, Brandon Pierce, Wei Zheng, Christopher I. Amos, Jennifer A. Doherty. Risk loci in telomere structure and maintenance genes across five cancer types: GAME-ON Consortium. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4609. doi:10.1158/1538-7445.AM2015-4609
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».