Abstract P6-14-03: Genome wide association study (GWAS) to identify variants conferring ramucirumab-associated hypertension in the ROSE/TRIO-012 breast cancer trial
Bibliographic record
Abstract
Abstract Background: In a candidate single nucleotide polymorphism (SNP) association study, we previously identified VEGFR-1 and VEGFR-2 SNPs strongly associated with treatment emergent hypertension (HT) in the ramucirumab (RAM) and docetaxel (Doc) arm in the ROSE/TRIO-012 study, a double-blinded multinational phase III trial that randomized 1,144 patients with advanced breast cancer to receive first-line Doc in combination with RAM or placebo (Mackey et al, JCO Jan 10, 2015:141-148; Mackey et al, JCO, Volume 33, Issue 15_suppl, May 20, 2015: 547). However, candidate SNP studies limit the number of genes for interrogation and a more comprehensive genome wide search may identify critical variants associated with the phenotype of HT. Preliminary analysis indicated that patients experiencing HT with RAM showed better overall survival (Mackey JR, et al (2015). Reply to H. Lee, et al. JCO; in press). These observations provide the potential to identify those patients with genetic variants for predisposition to RAM-associated HT to inform therapeutic decisions. Methods: Genotyping of samples is underway using Affymetrix SNP 6.0 arrays. Genotype data will be filtered for deviations from Hardy Weinberg Equilibrium and minor allele frequency of >0.05. Study subjects (n=792) provided ethics-committee approved prospective consent for this genetic study of whom 478 subjects were allocated RAM + Doc arm. Toxicity grades 0-1 (n= 394 controls; low toxicity) vs. grade >2 (n= 84 cases, high toxicity) is our binary outcome. Dominant genotypic model is assumed. Chi-square test, FDR and/or 10000 permutation tests will be employed (Golden Helix-SVS v8.3) and p<0.05 considered statistically significant. Population stratification will be identified (EIGENSTRAT) and association statistics will be corrected using Eigenvectors along with age as covariates. Fine mapping of loci showing significant associations will be attempted using imputation tools. Results and conclusions: We expect up to 700,000 SNPs to be retained after filtering based on our previous breast cancer GWAS analyses (Damaraju et al. Cancer Research (suppl); Vol 70 (24), page 258s, 2010 and Sehrawat et al Hum Genet. 2011 Oct;130(4):529-37) and 30,000 SNPs to show significance at a nominal p-value (0.05); these will be analysed for regions of high linkage disequilibrium to narrow down potential loci showing association with HT to serve as candidate markers in further independent validation studies. Cumulative dose to adverse events will be considered in the analysis. Identified loci will be interrogated for potential genes in the flanking regions with biological relevance based on pathway analysis. Identified variants from candidate SNP and GWAS may allow developing predictive tools to enable stratification of patients for therapies. The analysis is expected to be completed by mid- November, 2015. Citation Format: Mackey JR, Lipatov O, Martín M, Webster M, Hegg R, Verma S, Ramos-Vázquez M, Fresco R, Thireau F, Houé V, Press MF, Kumaran M, Damaraju S. Genome wide association study (GWAS) to identify variants conferring ramucirumab-associated hypertension in the ROSE/TRIO-012 breast cancer trial. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-14-03.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".