Abstract 1853: Identification and characterization of genes in regions of recurrent genomic gain as putative therapeutic targets in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related mortality in the United States. PDAC presents with the worst prognosis of all solid tumors and the 5-year survival rate for patients with advanced PDAC is only 2%. Current chemotherapies fail to attenuate the aggressiveness of this disease and as such, novel therapeutic strategies are needed. The purpose of our study is to identify putative therapeutic targets in PDAC and characterize the role of a target gene in tumor progression. Towards this aim, we sought to identify genes which are recurrently gained or amplified in pancreatic tumors. If increased copy of specific genes confers neoplastic properties, selective targeting of such genes may have therapeutic implications. We obtained publically available copy number alteration data on 60 PDAC genomes characterized in four independent PDAC studies. Using bioinformatics and computational approaches, we identified 20 genomic loci that are gained in at least one sample in three of the four PDAC datasets. Our integrated analysis of the genes mapping to these 20 loci results in a catalogue of 710 protein-coding genes and 46 miRNA genes, which are candidate targets for further analysis. In order to delineate appropriate biological models for functional validation of these genes in PDAC, we obtained SNP array-based copy number data using the Illumina OmniExpress platform and gene expression analysis from Illumina HT-12 BeadChip arrays from 30 human PDAC cell lines. We identified genes from our candidate gene list in which copy number alteration and gene expression are correlated as computed by a Spearman rank correlation coefficient, α. This gene set was enriched for genes with high correlation between copy number and expression in comparison to simulated gene sets (p = 0.007). Our data suggest that Epithelial cell-transforming sequence 2 oncogene (ECT2) on 3q26.3 is a strong candidate for functional validation. This gene encodes a Rho-specific guanine exchange factor involved in various cellular processes including regulation of G1-to-S phase transition in cell-cycle progression. To validate our in silico findings, we have designed experiments to investigate the role of ECT2 amplification in PDAC, using the 30 human PDAC cell lines we have genetically analyzed. This will be accomplished through differential inhibition of the ECT2 protein, using RNAi and small molcules, in cell lines in which ECT2 is amplified, compared to appropriate control lines. In conclusion, we have identified a set of candidate target genes in PDAC and are currently validating the role of one of these targets, ECT2, in PDAC tumor progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1853. doi:1538-7445.AM2012-1853
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".