Integrated Genomic, Transcriptomic, and RNA-Interference Analysis of Genes in Somatic Copy Number Gains in Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVES: This study used an integrated analysis of copy number, gene expression, and RNA interference screens for identification of putative driver genes harbored in somatic copy number gains in pancreatic ductal adenocarcinoma (PDAC). METHODS: Somatic copy number gain data on 60 PDAC genomes were extracted from public data sets to identify genomic loci that are recurrently gained. Array-based data from a panel of 29 human PDAC cell lines were used to quantify associations between copy number and gene expression for the set of genes found in somatic copy number gains. The most highly correlated genes were assessed in a compendium of pooled short hairpin RNA screens on 27 of the same human PDAC cell lines. RESULTS: A catalog of 710 protein-coding and 46 RNA genes mapping to 20 recurrently gained genomic loci were identified. The gene set was further refined through stringent integration of copy number, gene expression, and RNA interference screening data to uncover 34 candidate driver genes. CONCLUSIONS: Among the candidate genes from the integrative analysis, ECT2 was found to have significantly higher essentiality in specific PDAC cell lines with genomic gains at the 3q26.3 locus, which harbors this gene, suggesting that ECT2 may play an oncogenic role in the PDAC neoplastic process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".