Abstract 1300: Genetic predictors of gene expression associated with risk of colorectal cancer
Bibliographic record
Abstract
Abstract To date, genome-wide association studies (GWAS) have reported common variants in over 50 loci with weak to moderate effects on CRC risk. These genetic factors in aggregate explain only a small fraction of familial risk of CRC. To aid in the discovery of novel CRC loci, we integrated large transcriptome data, including those generated in the Genotype-Tissue Expression (GTEx) Project in genetic association analyses of CRC. The computational method, PrediXcan, was used to predict transcript levels in relevant tissues and perform gene-level association tests with CRC. Prediction models were developed using whole blood transcriptomes (n=922) from the depression genes and networks (DGN), as well as colon transcriptomes (transverse n=169 and sigmoid n=124) from GTEx datasets, along with high-density genotyping data from the same subjects. Genetically determined expression levels were tested for association with CRC in 12,186 cases and 14,718 controls from GECCO-CCFR and suggestive associations (false discovery rate = 0.2) were evaluated in 7,481 cases and 17,796 controls from the Asia Colorectal Cancer Consortium (ACCC) and 22,974 cases and 14,392 controls from the Colorectal Transdisciplinary (CORECT) study. We attempted to replicate novel associations for eight genes and found statistically significant associations with CXCR1 (OR=1.21 (1.10-1.33), p-value=7.8x10-5) and CXCR2 (OR=1.24 (1.11-1.38), p-value=9.9x10- 5). We also recovered previous associations at six known GWAS loci, thereby providing additional support for putative target genes. CXCR1 and CXCR2 are therapeutic targets for the anticancer agent Reparixin, which is currently being investigated in a stage II clinical trial for triple negative breast cancer. As such, these findings provide preliminary support for new molecular targets that could potentially repurpose a putative cancer therapeutic. These findings highlight the utility integrating transcriptome data for novel discovery and biological insight of risk loci. Citation Format: Stephanie A. Bien, Xingyi Guo, Yu-Ru Su, Tabitha A. Harrison, Conghui Qu, Yingchang Lu, Jiron Long, Sai Chen, Andrew T. Chan, David V. Conti, Hyun M. Kang, Michael Hoffmeister, Thomas J. Hudson, Mark A. Jenkins, Loic Le Marchand, Polly A. Newcomb, Martha L. Slattery, Emily White, Goncalo R. Abeçasis, Stephen B. Gruber, Deborah A. Nickerson, Stephanie L. Schmit, Graham Casey, Li Hsu, Wei Zheng, Ulrike Peters, GECCO-CCFR-AAAC-CORECT. Genetic predictors of gene expression associated with risk of colorectal cancer [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 1300. doi:10.1158/1538-7445.AM2017-1300
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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".