Abstract 1352: Epithelial-mesenchymal transition (EMT) gene variants influence epithelial ovarian cancer risk in women of European, African and Asian ancestry.
Bibliographic record
Abstract
Abstract Introduction The epithelial-mesenchymal transition (EMT) pathway contributes to epithelial ovarian cancer (EOC) progression. Previously we identified associations of single nucleotide polymorphisms (SNPs) in EMT-related genes and EOC risk (data not shown). To further investigate the role of EMT-related gene variants in EOC and to derive more reliable risk estimates, we evaluated associations for 795 SNPs from 278 EMT-related genes in a replication study of 43 studies within the Ovarian Cancer Association Consortium (OCAC). Methods The study population included 14,736 cases and 23,448 controls of European ancestry, 89 cases and 200 controls of African ancestry, and 249 cases and 1574 controls of Asian ancestry. The 795 SNPs were genotyped using an Illumina Infinium iSelect BeadChip as part of the Collaborative Oncological Gene-environment Study (COGS). For women of European ancestry, both invasive cancers combined and the four main histological subtypes (serous [n=8,372], endometroid [n=2,068], clear cell [n=1,025] and mucinous [n=943] were analyzed, while for women of African and Asian ancestries only the serous subtype was analyzed. SNP analyses were conducted using unconditional logistic regression under a log-additive model separately for women of each ancestry. All analyses were adjusted for study site and population substructure within each ancestry. Results For women of European ancestry, the strongest evidence of an association for invasive cancers combined was observed for IGF1R rs10794486 (OR=1.05, 95%CI=1.02-1.09, P=0.002), which was also associated with serous (P=0.019), but not the other histological subtypes. In the histological subtype-specific analysis, the most significant association for serous was observed at NRP2 rs3771044 (OR=1.09, 95%CI=1.03-1.15, P=0.00014), for endometroid at rs1770247 (13q31.3, OR=1.16, 95%CI=1.07-1.25, P=0.00028), for clear cell at rs4848300 (2q13, OR=0.84, 95%CI=0.76-0.93, P=0.00099) and for mucinous at SEMA4B rs8030039 (OR=1.09, 95%CI=1.01-1.17, P=0.00019). For women of African ancestry, the strongest association for serous histological subtype was observed at F9 rs6048 (OR=1.71, 95%CI=1.17-2.50, P=0.006). This SNP was not significant in women of European or Asian ancestry. The strongest association for women of Asian ancestry was IGF1R rs10794486 (OR=1.68, 95%CI=1.16-2.45, P=0.007), which was also significant in women of European, but not African, ancestry. Conclusion Findings from this large study provide additional evidence that variants in EMT-related genes may be associated with ovarian cancer risk and that EMT susceptibility loci may differ by ovarian cancer histological subtypes. Furthermore, the findings suggest that susceptibility locus for the serous subtype may be different for women of African ancestry. Future studies are warranted to confirm these findings. Citation Format: Ernest K. Amankwah, Jonathan Tyrer, Hui-Yi Lin, Ya-Yu Tsai, Zhihua Chen, Gang Han, Xiaotao Qu, Ellen Goode, Julie Cunninghan, Edward Iverson, Susan Ramus, Andrew Berchuck, Joellen Schildkraut, Alvaro Monteiro, Simon Gayther, Steven Narod, Paul Pharoah, Thomas A. Sellers, Catherine Phelan. Epithelial-mesenchymal transition (EMT) gene variants influence epithelial ovarian cancer risk in women of European, African and Asian ancestry. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1352. doi:10.1158/1538-7445.AM2013-1352
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".