Abstract 2174: Identification of genetic factors contributing to development of common cancers through tissue-specific protein interaction analysis
Bibliographic record
Abstract
Abstract In the genetic study of diseases, pathway based analysis captures more of the variability in risk than single-variant analysis, and also provides a meaningful framework for organizing and interpreting findings. Here, we augment the biologic insights provided by pathway based analysis of GWAS results by incorporating tissue-specific protein interactions. From large GWAS meta-analyses of lung cancer (12,160 cases/16,838 controls), breast cancer (15,748 cases/18,084 controls), and prostate cancer (14,160 cases/12,724 controls), we determined the tissue-specific interactomes of proteins expressed from genes containing independently associated variants. The pathways with statistical overrepresentation of proteins in each network were evaluated across the three cancers. Our results show that pathways implicated in the development of all three cancers or two out of the three cancers tend to be broad, essential cellular processes required for growth and survival. The most significant examples include the nerve growth factor (P = 1.65 × 10^-47), epidermal growth factor (P = 1.22 × 10^-37), and stem cell factor/Kit (P = 4.47 × 10^-35) signaling cascades. However, within these shared pathways, the proteins encoded by genes with risk-conferring variants generally differ from cancer to cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as leptin signaling in breast cancer (P = 1.24 × 10^-6) and platelet sensitization by low-density lipoprotein in prostate cancer (P = 3.80 × 10^-6). Taken together, we demonstrate a new method of pathway based analysis following GWAS that considers tissue-specific protein interactions. For cancers of the lung, breast, and prostate, we validate known susceptibility pathways and identify previously unexplored ones, as well as characterize each cancer by its unique pathways. Citation Format: David C. Qian, Jinyoung Byun, Younghun Han, David J. Hunter, Brian E. Henderson, Rosalind Eeles, Christopher A. Haiman, Douglas F. Easton, Rayjean J. Hung, Christopher I. Amos. Identification of genetic factors contributing to development of common cancers through tissue-specific protein interaction analysis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2174. doi:10.1158/1538-7445.AM2015-2174
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".