Abstract 3742: Gene-environment Interactions in esophageal adenocarcinoma risk: A case-only analysis
Bibliographic record
Abstract
Abstract The incidence of esophageal adenocarcinoma (EA) has increased approximately 500% in Western countries over the last four decades. The rapidly increasing incidence and sporadically development feature of EA suggest that gene-environment (G-E) interactions dominate the etiology. However, how G-E contributes to EA carcinogenesis is still poorly understood. We used a case-only approach to test the effect of G-E interactions in the occurrence of EA. 1330 functional and/or tagging SNPs selected from 354 cancer-associated genes were genotyped in 335 Caucasian EA cases. G-E (gastroesophageal reflux symptoms, GERD; BMI; and smoking) interactions were assessed by a two-step approach. First, we applied random forest (RF) to screen for important SNPs that had main effects as well as interaction effects. Second, we used case-only logistic regression (LR) models to estimate the G-E interaction effect, adjusting for covariates and false-discovery rate (FDR). RF analyses identified three sets of SNPs (94 SNPs-GERD, 38 SNPs-smoking, and 44 SNPs-BMI, respectively) that had the highest importance scores and lowest classification error rates. Further case-only LR analysis revealed that multiple interaction markers were significantly associated with EA risk: GERD*rs2237051 of EGF(OR = 1.39, P = 3.50E-07), GERD*rs2440 of XRCC5 (OR = 1,80, P = 0.0008), GERD*rs2237051*rs2440 (OR = 1.85, P = 0.004), smoking*rs10842514 of KRAS (OR = 0.85, P = 0.012), BMI*rs2305742 of IL12RB1 (OR = 0.84, P = 0.003), BMI*rs11568820 of VDR (OR = 0.97, P = 0.0005), BMI*rs11244142 of ABL1 (OR = 1.47, P = 2.09E-05), BMI*rs2800975 of XPA*rs743572 of CYP17A (OR = 3.62, P = 0.0009), and BMI*rs11244142*rs20547 of IL13 (OR = 1.89, P = 0.023). These results indicate that genetic variability may contribute to EA development through interactions with environmental factors. Our data also suggest that G-E interactions may be G- and E-specific in modulating the risk of EA. Supported by grants: NIH grants CA92824, CA74386, CA90578, and CA119650); Flight Attendant Medical Research Institute (FAMRI) grant 062459_YCSA; the Kevin Jackson Memorial Fund and Alan Brown Chair of Molecular Genomics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3742. doi:10.1158/1538-7445.AM2011-3742
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".