Abstract 897: Association of inherited variants in HGF and ovarian cancer survival time
Bibliographic record
Abstract
Abstract Using data from patients with invasive epithelial ovarian cancer, we sought to examine whether overall survival was associated with common inherited variation in candidate genes and genomic regions including those involved in angiogenesis, mitosis, and regions of interest from genome-wide association and expression studies. We conducted a two-stage analysis based on Mayo Clinic patients with replication of peak results in the Cancer Genome Atlas (TCGA). Ovarian cancer patients (N=312) seen at the Mayo Clinic diagnosed from 1999 to 2006 with median follow-up of 3.7 years (range, 0.1 − 9.5 years) and 172 observed deaths were analyzed at 993 single nucleotide polymorphisms (SNPs) in 179 genes. For all patients and for serous patients only (N=192 with 129 observed deaths), Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each SNP, adjusted for other prognostic factors. Twenty-nine SNPs in ten genes revealed p < 0.01 among all cases or among serous cases only, including 21 independent SNP at r2=0.9; analyses within four genes (HGF, PLG, PRKACB, and DCTN5) yielded p < 0.001. The strongest result was in HGF at rs1800793 (all cases: HR 1.7, 95% CI 1.3 − 2.2, p-trend 1.9 E-5; serous cases: HR 1.6, 95% CI 1.2 − 2.2, p-trend 1.8 E-3). A correlated SNP rs2214825 also showed association (r2=0.76; all cases: HR 1.4, 95% CI 1.1 − 1.8, p-trend 3.3 E-3; serous cases: HR 1.4, 95% CI 1.0 − 1.9, p-trend 0.03), and PCA gene-level tests of the HGF locus were also significantly associated with survival (all cases, p=3.7 E-4; serous cases, p=0.01). Among all cases, differential survival was also associated with DCTN5 rs12447304 (HR 1.8, 95% CI 1.3-2.6; p=7.1 E-4), and among serous cases, with PLG rs783173 (HR 1.6, 95% CI 1.2 − 2.0, p = 8.1 E-4) and PRKACB rs1402694 (HR 1.7, 95% CI 1.3 − 2.2, p=3.2 E-4). We then examined SNPs in the most suggestive gene, HGF, using data from TCGA which had genotyped four overlapping HGF SNPs in 350 invasive cases (188 deaths). Covariate-adjusted TCGA analysis at rs2214825 revealed consistent results (HR 1.7, 95% CI 1.3-2.2, p=1.4 E-4) as did analyses of three other HGF SNPs (HR range 1.3 to 1.5, p range 3.4 E-3 to 0.049); correlation between SNP genotypes and HGF expression was also observed (p=1.4 E-3, rs2214825, probe 210997_at). While this is the first report of SNPs in HGF, it has long been known that the HGF signaling pathway, activated by HGF ligation of c-MET, plays a key role ovarian cancer cell growth, migration, and invasion. c-MET is overexpressed in 40%-60% of ovarian tumors, and high overexpression is associated with lower overall survival. In addition, HGF has been shown to enhance the response of ovarian cancer cells to low doses of paclitaxel and cisplatin by activating the intrinsic apoptotic pathway, leading to cancer cell death. These results now suggest that inherited differences in HGF among women with invasive disease may play a role in survival, holding potential clues for tailored avenues of treatment. 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 897.
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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) | 0.002 | 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".