Abstract B27: Investigation of small GTPase genes in epithelial ovarian cancer.
Bibliographic record
Abstract
Abstract Background: Epithelial Ovarian Cancer (EOC) is a lethal gynecologic malignancy and the fifth cause of cancer mortality in women in the US. Normal ovarian physiology is intricately connected to tightly regulated small GTP binding proteins of the Ras superfamily (Ras, Rac, Rho, Rab, Arf, and Ran) which regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. These proteins function in a highly coordinated manner through signaling cascades and feedback loops within and among the small GTPase subfamilies. We hypothesized that single nucleotide polymorphisms (SNPs) in small GTPase genes are associated with epithelial ovarian cancer (EOC) risk and aberrant expression of these genes correlates with tumor characteristics including overall survival (OS). Methods: In a discovery set of 7931 EOC cases and 9206 controls we investigated 9,356 SNPs from 112 genes, 657 of which showed associations up to the significance level of p<0.05. We genotyped 407 of the most significant SNPs from 112 genes in a combined dataset (discovery and replication) which consisted of 18,736 EOC cases and 23,448 controls (of European ancestry) from 43 studies in the Ovarian Cancer Association Consortium (OCAC), using an Illumina Infinium iSelect BeadChip as part of the Collaborative Oncological Gene-environment Study (COGS). Odds ratios and 95% confidence intervals were calculated using unconditional logistic regression under log-additive models. A False Discovery Rate (FDR) q<0.2 was applied. Pearson's correlation tests were performed on the expression of genes with tumor characteristics and OS in 561 ovarian tumors: serous (n=464); endometrioid (n=52); clear cell carcinoma (n=28) and mucinous (n=17) subtypes. Results: The most significantly associated SNP associations with EOC were: AKAP6 rs1955513, (OR=0.9, p=3.3x10-4) in all invasive; ARGHEF10L rs10788679, (OR=1.05, p=2.6x10-4) in serous; RAB31 rs1166373, (OR=1.25, p=4.0x10-3) in endometrioid; KRAS rs4963872, (OR=1.35, P=4.5x10-4) in mucinous EOC and TNIK rs6780532, (OR=0.88, p=7.7x10-3) in clear cell carcinoma. Increased expression of AKAP6 and TNIK was marginally correlated with advanced EOC stage (p<0.05) while high KRAS expression was correlated with OS (HR=1.18, p=0.038). Conclusions: Genetic variation in the small GTPase genes appears to be associated with ovarian cancer risk and aberrant expression of these genes correlates with tumor characteristics and OS. Citation Format: Hui-Yi Lin, Yin Xiong, Jonathan Tyrer, Douglas C. Marchion, Alvaro NA Monteiro, Andrew Berchuck, Joellen M. Schildkraut, Ellen L. Goode, Susan J. Ramus, Simon A. Gayther, Paul DP Pharoah, Steven A. Narod, Thomas A. Sellers, Catherine M. Phelan. Investigation of small GTPase genes in epithelial ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr B27.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".