Abstract 30: Cross-cancer cross-tissue transcriptome-wide association study (TWAS) of 11 cancers identifies 56 novel genes
Bibliographic record
Abstract
Abstract Although cancer is a heterogeneous disease, there are shared hallmark mechanisms across multiple tumor types. Because of this, identifying genes associated with multiple cancer types has the potential to shed light on general oncogenic mechanisms. Conversely, integrating evidence for genetic association across multiple cancers could identify novel genes missed in single-cancer analyses. Transcriptome Wide Association Studies (TWAS) have been successful in identifying genes associated with individual cancers. TWAS test whether genetically-predicted tissue-specific gene expression levels are associated with cancer risk. Although cross-cancer genome-wide association studies (GWAS) analyses have been performed previously, no cross-cancer TWAS has been conducted to date. Here, we implement a pipeline to perform cross-cancer, cross-tissue TWAS analysis. We use newly-developed multi-trait TWAS test statistics to integrate the TWAS results for association between 11 separated cancers and predicted gene expression in each of 43 GTEx tissues; these include a "sum" test and a "variance components" test, analogous to fixed- and random-effects meta-analyses. We then integrated the results across different tissues using the Aggregated Cauchy Association Test (ACAT), a novel powerful and robust test for combining association results under general correlation patterns. A total of 403 genes were significantly associated with at least one cancer type using predicted gene expression for at least one tissue (p<0.05/899,338, Bonferroni adjusted for the total number of gene-cancer-tissue combinations tested); 96 additional genes were identified when combining test results across cancers (p<0.05/81,758, adjusting for the total number of gene-tissue combinations tested); and 35 additional genes when further combining test results across tissue (p<0.05/12,001, adjusting for the number of genes tested). Among these significant genes, 70 were not near previously-published GWAS index variants (>250 kb distant). 14 of the 70 novel genes were identified from the single cancer single tissue test; an additional 43 were identified with the cross-cancer test; and another 13 were identified when further combining the results across tissues. The newly identified genes include RBBP8, which regulates cell proliferation and modulates BRCA1-mediated DNA repair, and TP53BP1, which is involved in double-strand break repair. Other newly identified genes are involved in chromatin structure, tumorigenesis, apoptosis, transcriptional regulation, DNA repair, immune system, oxidative damage and cell cycle, proliferation, progression, shape, structure, and migration. Citation Format: Helian Feng, Arunabha Majumdar, Bogdan Pasaniuc, Hongjie Chen, Sara Lindström, BCAC, OCAC, PRACTICAL, Jeroen Huyghe, Stephanie L. Schmit, Tracy A. O'Mara, Deborah J. Thompson, Stuart MacGregor, Paul Brennan, James McKay, Richard S. Houlston, Beatrice S. Melin, Christopher Amos, Anne E. Cus, Mark M. Iles, Siddhartha Kar, Paul Pharoah, Rayjean J. Hung, Peter Kraft. Cross-cancer cross-tissue transcriptome-wide association study (TWAS) of 11 cancers identifies 56 novel genes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 30.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".