Abstract 3222: Evaluation of associations between circulating proteins and breast cancer risk using genetic variants
Bibliographic record
Abstract
Abstract Breast cancer is the most commonly diagnosed cancer in women in many countries. Several circulating protein biomarkers have been identified in relation to breast cancer risk. However, previous studies either had small sample sizes or employed low-throughput techniques. To search for novel biomarkers, we utilized genetic variants as instruments and evaluated over 1,400 proteins in relation to breast cancer risk using data from the Breast Cancer Association Consortium (BCAC). We extracted beta coefficients from reported protein quantitative trait loci (pQTL) derived from genome-wide association studies of circulating proteins. Summary statistics of these pQTL variants associated with breast cancer risk were obtained from 122,977 cases and 105,974 controls of European descent in the BCAC. Associations of genetically predicted protein levels with breast cancer risk were evaluated using the inverse-variance weighted method. For proteins with a significant association, expression levels of the corresponding gene were predicted using genotyping and transcriptomic data from the Genotype-Tissue Expression project and then evaluated for their associations with breast cancer risk. We identified 56 protein biomarkers showing a significant association with breast cancer risk after accounting for multiple comparisons (false discovery rate < 0.05). Among them, levels of 32 proteins were influenced by variants close to a newly reported breast cancer susceptibility locus (9q34.2, ABO). Inverse associations of breast cancer risk were found with membrane proteins such as insulin receptor, insulin-like growth factor receptor 1, hepatocyte growth factor receptor, neurogenic locus notch homolog protein 1, and vascular endothelial growth factor receptor 2, with odds ratios ranging from 0.82 to 0.97 per unit increase in genetic risk scores (p-values ranging from 6.53x10-4 to 3.28x10-8). Genetically predicted expression of five genes, CPNE1, CTSF, TFPI, SCG3, and QSOX2, was found to be associated with breast cancer risk at p <0.05 in the same direction as the associations observed for the corresponding proteins. Results from our study suggest that multiple membrane proteins related to insulin resistance may be linked to breast cancer risk through genetic variants at 9q34.2. Citation Format: Xiang Shu, Jiandong Bao, Lang Wu, Jirong Long, Xingyi Guo, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang, Joe Dennis, Jacques Simard, Douglas F. Easton. Evaluation of associations between circulating proteins and breast cancer risk using genetic variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3222.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".