Abstract 2965: Functionally informed genome-wide interaction analysis of nonsteroidal anti-inflammatory drugs on colorectal cancer risk
Notice bibliographique
Résumé
Abstract Background Regular use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal cancer (CRC). Genome-wide interaction analysis (GxE) has identified a few variants that may modify the effects of NSAIDs on CRC risk. However, limited statistical power remains a concern. Restricting analyses by using functional genomic information to aggregate variants into biologically relevant sets can reduce the number of tests, thereby increasing statistical power. We tested the interactions between variant models of gene expression and NSAIDs use on CRC risk. Methods Functional weights of each variant were estimated using PrediXcan based on jointly measured transcriptomes and genomes data of transverse colon tissues from the Genotype-Tissue Expression (GTEx) Project for all genes with sufficient heritability (≥1%). A mixed-effects model was used to assess the GxE effects in a gene among 9,917 incident CRC cases and 10,533 controls from 17 (nested) case-control studies from the Colon Cancer Family Registry (CCFR) and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). GxE analysis was done by modeling the interaction between predicted gene expression and NSAID use (fixed effects), and residual variant-specific GxE effects that are not accounted for (random effects). Regular user of aspirin and/or non-aspirin NSAIDs was compared to non-regular users. False discovery rate (FDR) was used to account for multiple comparison and FDR≤0.2 was considered genome-wide significant. Secondary analysis was performed on significant genes and duration of use among users. Results Regular use of aspirin was higher among controls (30.3%) compared to cases (24.3%). Among the 4,842 genes tested, SORD significantly modified the effect of regular use of aspirin on CRC risk (p-interaction = 1.45×10-5; FDR=0.07). The SORD gene encodes sorbitol dehydrogenase (SORD), which oxidizes sorbitol to fructose in the polyol pathway. Decreased SORD concentrations were previously observed to dramatically increase in colorectal adenoma cells when compared to normal mucosa cells, suggesting the involvement of dysregulated polyol metabolisms in colorectal tumorigenesis. However, the duration of aspirin use was not statistically significantly associated with SORD gene expression on CRC risk (p=0.149) among 5,560 aspirin users. No significant interactions were observed between genetically determined colon gene expression levels and any NSAID use or non-aspirin NSAID use at FDR<0.2. Conclusions Incorporating functional information, we discovered a novel gene that may interact with aspirin use to confer CRC risk. These findings provide preliminary support for new biological insights that could help understand the chemopreventive mechanisms of aspirin on CRC. We aim to replicate these findings in additional studies. Citation Format: Xiaoliang Wang, Yu-Ru Su, Andrew T. Chan, Stephanie Bien, Sonja I. Bernt, Hermann Brenner, Graham Casey, Jenny Chang-Claude, Steven J. Gallinger, Robert W. Haile, Tabitha A. Harrison, Michael Hoffmeister, Mark A. Jenkins, Amit Joshi, Yi Lin, Noralane M. Lindor, Loic Le Marchand, Hongmei Nan, Polly A. Newcomb, John D. Potter, Martha L. Slattery, Steve N. Thibodeau, Emily White, Li Hsu, Ulrike Peters. Functionally informed genome-wide interaction analysis of nonsteroidal anti-inflammatory drugs on colorectal cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2965.
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 | 0,006 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 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 ».