Abstract 3124: Prognostic biomarkers for pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) has the lowest 5-year-survival rate of common cancers (< 7%), but despite intensive research efforts over the past several decades, its dismal prognosis has barely improved. Recent genome-wide association studies (GWAS) have linked several genetic factors with pancreatic cancer, but there is generally poor overlap in the results from such studies, presumably due to population heterogeneity. In this study, we collected previously published pancreatic risk loci from multiple GWAS studies, and correlated these loci with clinical information gathered on a well-characterized cohort of 148 PDAC patients from the University Health Network in Toronto Canada, and several other North American hospitals, which have undergone whole-genome sequencing. Using this cohort, we attempted to identify prognostic genomic biomarkers by correlating the germline genomic alterations observed in our cohort to overall survival (OS). By understanding the mechanism of germline variants that alter OS, we hope to develop insights that will lead to improved detection and therapy for PDAC patients. Among the 67 published risk loci we tested using a multivariate Cox proportional hazard model, we found a strong positive association of the single nucleotide polymorphism rs4785367 (RefSNP alleles: C/T on forward strand; MAF = 0.474) with overall survival of PDAC donors: HR = 0.426; CI = 0.268 - 0.686; p-value = 0.00029. A more detailed analysis at the genotype level revealed that the presence of the homozygous minor allele has a stronger effect than either the heterozygous or homozygous major allele. The SNP falls within the intergenic region between the ZNF423 and TMEM188 genes, within the exon 2 of lncRNA RP11-305A4.3 and overlaps a CTCF regulatory domain. Preliminary gene expression analysis from RNA sequencing data on a subset of PDAC donors (n = 28) shows that patients carrying the minor allele have significant higher TMEM188 expression than of the major type (p-value = 0.012), suggesting that this allele may influence the course of PDAC via TMEM188 activity. A recent study linked the gene product to activation of NK cells, which in turns increases the defense mechanism against the pathogens, infections and transformed tumors. These findings suggest a possible molecular mechanism influencing the course of PDAC. We are also exploring the effect of the presence of the minor allele on the regulatory CTCF region, by applying an integrative pipeline for risk SNP analysis to pancreatic cancer. This will possibly detect the effect on the NANOG motif binding and/or on CTCF looping. In summary, the present study detected the rs4785367 as a prognostic biomarker for pancreatic cancer, with the novelty of increased TMEM188 gene expression being linked to the presence of the alternate allele in PDAC patients. Further investigations on this and on assessing the effect of the polymorphism on the regulatory CTCF feature are in progress. Citation Format: Cristina Baciu, Robert Grant, Hansen He, Musaddeque Ahmed, Robert E. Denroche, Lee Timms, Gun Ho Jang, Ayelet Borgida, Xihui Lin, Paul C. Boutros, Dianne Chadwich, Sheng-Ben Liang, Sagedeh Shahabi, Michael H.A. Roehrl, Sean Cleary, Julie M. Wilson, John D. McPherson, Lincoln Stein, Steven Gallinger. Prognostic biomarkers for pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3124.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".