Abstract 2258: Genome-wide association studies of breast cancer prognosis
Bibliographic record
Abstract
Abstract Introduction: The prognosis and response to treatment in women with breast cancer varies considerably after taking into account clinic-pathological variables. Other factors such as host genotype are also likely to be important. We therefore investigated the role of germline genetic variation on survival after breast cancer using data from the Breast Cancer Association Consortium. Materials and methods: We included data from ten projects in which breast cancer case cohorts were genotyped using arrays providing genome-wide coverage of common variants. The majority of samples was genotyped using the Illumina custom iCOGS or OncoArray chips. Data were imputed using the 1000 Genome phase 3 as a reference panel using a two-stage procedure. We assessed the association between genotype and 10-year breast cancer specific survival for each SNP using Cox proportional hazards regression adjusted for principle components and stratified by country. Genotype data from 87,877 breast cancer cases, 63,170 ER-positive and 20,297 ER-negative, with 6,511 known breast cancer deaths were included in the analyses. Results: Preliminary analyses shows that in all invasive cases, ER-positive and ER-negative disease 123, 110, and 212 variants respectively, were associated with breast cancer-specific mortality at P<5x10-5. A single variant, rs1295683, was associated with higher mortality at genome-wide significance in all patients: HR=1.16 (95%CI:1.10-1.22); P=2.8x10-8 (risk allele frequency: 0.12). We identified four variants to be associated with mortality in ER-negative disease at P<5x10-8. The strongest association was for rs145963877: HR=1.32 (95%CI:1.20-1.46); P=2.3x10-8. No variant reached genome-wide significance for ER-positive disease; the most significant variant was chr7:41400313 with an HR=1.17 (95%CI:1.10-1.24); P=1.8x10-7. In addition, using OncoArray data, we provided independent validation of higher mortality of CHEK2 c.1100delC carriers compared with non-carriers (HR=1.56 (95%CI:1.22-2.01)), previously reported in Weischer et al JCO 2012. Using iCOGS data, we evaluated the association between mortality and the polygenic risk score (PRS) based on 77 known breast cancer susceptibility SNPs. A higher PRS was significantly associated with lower breast cancer-specific mortality: HR=0.87(95%CI:0.81-0.93) (per unit of PRS) with a similar association in ER-negative and ER-positive disease. The association was attenuated after adjusting for tumor grade. Conclusion: We have identified a novel set of germline genetic variants that are associated with breast cancer prognosis. The effect sizes are small for each of the variants and unlikely to be of immediate clinical relevance. However, understanding of the biology that underlies the associations may identify novel pathways and targets for therapy. Being at higher risk of breast cancer, as defined by the breast cancer susceptibility PRS, is not associated with an adverse prognosis. Citation Format: Marjanka K. Schmidt, Qi Guo, Thilo Dörk, Diana Eccles, Renske Keeman, Jacques Simard, Peter Kraft, Douglas F. Easton, Paul D. Pharoah, on behalf of the Breast Cancer Association Consortium. Genome-wide association studies of breast cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2258. doi:10.1158/1538-7445.AM2017-2258
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".