Large-scale multiomic analysis identifies non-coding somatic driver mutations and nominates <i>ZFP36L2</i> as a driver gene for pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Background The identification and characterisation of somatic cancer driver mutations in the non-coding genome remains challenging. Objective To broadly characterise non-coding driver mutations for pancreatic ductal adenocarcinoma (PDAC). Design Using mutation calls from whole-genome sequence data in PDACs and genome-scale maps of accessible gene regulatory regions in normal-derived and tumour-derived pancreatic samples, we analysed enrichment of non-coding mutations in gene regulatory regions relevant to normal-derived and tumour-derived pancreatic contexts. Functional follow-up of potential driver mutations was performed using chromatin interaction analyses, massively parallel reporter assays (MPRA) and targeted analysis of selected non-coding somatic mutations (NCSMs). Results We first created genome-scale maps of accessible chromatin regions (ACRs) and histone modification marks (HMMs) in pancreatic cell lines and purified pancreatic acinar and duct cells. Integration with whole-genome mutation calls from 506 PDACs revealed 314 ACRs/HMMs significantly enriched with 3614 NCSMs. Chromatin interaction analysis identified 416 potential target genes and MPRA revealed 178 NCSMs impacting reporter activity (19.45% of those tested). Targeted luciferase validation confirmed negative effects on gene regulatory activity for NCSMs near ZFP36L2 and CDKN2A . For the former, CRISPR interference identified ZFP36L2 as a target gene (16.0–24.0% reduced expression, p=0.023–0.0047), and growth inhibition after overexpression of ZFP36L2 (4.1–14.1-fold reduction, p=6.0×10 –4 − 3.2×10 –3 ) implicates a possible tumour suppressor function. Conclusion Our integrative approach provides a catalogue of potential non-coding driver mutations and nominates ZFP36L2 as a novel PDAC driver gene with a likely tumour suppressor function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".