Abstract A27: Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDA) is a highly lethal malignancy with a 5-year survival rate of 6%, due in part to therapy resistance and late stage at diagnosis. A detailed understanding of the molecular alterations underlying PDA is required to uncover mechanisms of tumorigenesis and enable development of more effective therapies. As such, large-scale exome sequencing efforts have revealed genes (KRAS, TP53, CDKN2A, SMAD4) and pathways (Wnt/Notch, TGFβ, axon guidance, cell adhesion) important for PDA progression. However, the exome comprises less than 2% of the human genome. Whole-genome sequencing analyses have uncovered a somatic mutation rate of 1 mutation per megabase in PDA. Therefore, PDA tumors carry thousands of somatic mutations per genome, mostly located in noncoding regions, the vast majority of which are completely uncharacterized. While the contributions of coding mutations to tumorigenesis are relatively well known, few studies have detailed alterations in noncoding DNA. Here we describe a new computational pipeline, GECCO (Genomic Enrichment Computational Clustering Operation) to analyze somatic noncoding alterations in 308 pancreatic ductal adenocarcinomas (PDA) and systematically identify commonly mutated putative regulatory regions. We find that recurrent somatic noncoding regulatory mutations are present in PDA but uncommon near canonical PDA genes. Instead, using GECCO we find that the regulatory mutations are enriched in known PDA pathways, including axon guidance and cell adhesion, and novel processes including transcriptional regulation and homeobox genes. We identify mutations in specific protein binding sites that correlate with significant differential expression of proximal genes. Furthermore, to explore the effects of these candidate mutations on gene expression, we developed an expression modulation score that quantifies the strength of gene regulation imposed by each class of regulatory elements. Surprisingly, we find that the strongest regulatory elements are also the most frequently mutated, suggesting a selective advantage for mutations in these regions. Our analysis provides a detailed single-cancer view of noncoding alterations in tumorigenesis, identifies recurrent regulatory mutations as new candidates for diagnostic and prognostic markers, and suggests novel mechanisms for tumor genome evolution. Citation Format: Michael E. Feigin, Tyler Garvin, Peter Bailey, Nicola Waddell, David K. Chang, Shimin Shuai, Steven Gallinger, John D. McPherson, Sean M. Grimmond, Ekta Khurana, Lincoln D. Stein, Andrew V. Biankin, Michael C. Schatz, David A. Tuveson.{Authors}. Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr A27.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".