Abstract 244: Pathway analysis of OncoArray data identifies biological pathways involved in lung cancer development
Bibliographic record
Abstract
Abstract Background:Genome-wide association studies (GWAS) have identified susceptible loci associated with lung cancer development. However, these variants only account for a small proportion of lung cancer heritability. With the aim to identify the missing heritability, we propose to conduct pathway analysis to test the joint effects of rare and common variants using the OncoArray data. By grouping SNPs into genes and pathways, we may shed light into the underlying mechanisms for lung cancer development and identify novel candidate genes and pathways.Methods:We applied sequence kernel association test (SKAT) to the OncoArray data of the Transdisciplinary Research of Cancer in lung of the International Lung Cancer Consortium (TRICL-ILCCO) that includes 34,432 individuals (19,028 cases and 15,404 controls) across over twenty lung cancer studies. A total of 403 KEGG and Biocarta pathways containing 5,555 genes and 58,717 SNPs were included in the study. As a score-based variance-component test, SKAT calculated p values for each pathway set by fitting the null model containing only age, gender, smoking, and first three PCAs. Results:KEGG neuroactive ligand receptor interaction (p=1.18×10-4, FDR=0.0285) and KEGG pancreatic cancer pathways (p=1.41×10-4, FDR=0.0285) were significantly associated with lung cancer. Gene-based analyses found that the most significant genes on the KEGG neuroactive ligand receptor interaction pathway to be CHRNA5 (p=2.33×10-8, FDR=0.0003), CHRNA3 (p=2.85×10-7, FDR=0.0019), and CHRNB4 (p=7.49×10-7, FDR=0.0034), while the most significant genes for KEGG pancreatic cancer pathway to be BRCA2 (p=2.23×10-5, FDR=0.0505). Stratified analyses highlighted five pathways (KEGG intestinal immune network for IGA production, KEGG leishmania infection, KEGG bladder cancer, KEGG pancreatic cancer, and KEGG axon guidance) for squamous cell carcinoma and one pathway (Biocarta ACH pathway) for adenocarcinoma with FWER<0.05. Noteworthy, the most significant gene on ACH pathway for adenocarcinoma was TERT gene, whereas the top two significant pathways for squamous carcinoma shared the most significant HLA-DQA1 gene, along with 11 other HLA genes at 6p21.Conclusion:The results suggest that the underlying pathways differ by cancer cell types and further research should be conducted to investigate the effects of immune pathways and HLA genes on squamous cell carcinoma etiology. Citation Format: Zhihui Wang, Ruyang Zhang, Li Su, Rayjean Hung, Christopher Amos, David C. Christiani. Pathway analysis of OncoArray data identifies biological pathways involved in lung cancer development [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 244.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".