Abstract 2854: Genetic variation in stromal genes decorin and lumican and susceptibility to serous ovarian cancer
Bibliographic record
Abstract
Abstract Introduction: Alterations in components of the stromal tissue that underlie the epithelium can initiate, promote or inhibit epithelial tumorigenesis. Decorin (DCN) and lumican (LUM) are stromal genes with reduced expression in serous ovarian cancer. We hypothesized that single nucleotide polymorphisms (SNPs) in these genes may influence susceptibility to serous ovarian cancer. Methods: To test this hypothesis, we compared genotype frequencies of 11 tagSNPs in DCN and LUM between 397 Caucasian cases with primary serous ovarian cancer and 920 Caucasian controls in two studies conducted at Mayo Clinic and Duke University (discovery set). Associations were evaluated further in a third Caucasian population of 436 cases and 1,098 controls in Australia (replication set). TagSNPs were selected from unrelated Caucasian samples within HapMap based on the following criteria: (i) minor allele frequency ≥ 0.05, (ii) pairwise linkage disequilibrium (LD) of r2 ≥ 0.8, and (iii) location within, and 5kb upstream and downstream of, each gene region. Genotyping was performed using the Illumina GoldenGate™ assay and associations between SNPs and serous cancer were evaluated using unconditional logistic regression to estimate per allele odds ratios (OR) and 95% confidence intervals (95%CI). Untyped genotypes for DCN rs13312816 in the discovery set, and for DCN rs3138165 and LUM rs17018765 in the replication set, were imputed using the MACH software. Results: In the discovery set, we observed inverse associations between DCN rs3138165 (OR=0.7; 95%CI=0.5-1.0), DCN rs13312816 (OR=0.7; 95%CI=0.5-1.0), DCN rs516115 (OR=0.8; 95%CI=0.7-1.0) and LUM rs17018765 (OR=0.7; 95%CI=0.5-1.0) and risk of serous ovarian cancers (all Ptrend=0.06). Associations were confirmed in the replication set for DCN rs3138165 (OR=0.6; 95%CI=0.4-0.9; Ptrend=0.009), DCN rs13312816 (OR=0.7; 95%CI=0.5-0.9; Ptrend=0.01) and LUM rs17018765 (OR=0.6; 95%CI=0.4-0.9; Ptrend=0.008), but not for DCN rs516115 (Ptrend=0.20). To increase statistical power, data from the discovery and replication sets were combined (833 cases and 2,013 controls) following confirmation of no statistical heterogeneity in the ORs between study sites. In the combined sample, associations were strengthened for DCN rs3138165 (OR=0.7; 95%CI=0.5-0.9; Ptrend=0.002), DCN rs13312816 (OR=0.7; 95%CI=0.5-0.9; Ptrend=0.002), DCN rs516115 (OR=0.9; 95%CI=0.8-1.0; Ptrend=0.03) and LUM rs17018765 (OR=0.6; 95%CI=0.5-0.8; Ptrend=0.001). All four SNPs were in the same LD block on chromosome 12, suggesting that serous ovarian cancer susceptibility may be driven by a single, correlated variant in the region. Conclusion: The findings of this study suggest that genetic variation in DCN and LUM may influence susceptibility to serous ovarian cancer and contribute to the mounting evidence that supports the role of the stroma in epithelial ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2854.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".