Abstract B18: Genomic analysis of pancreatic ductal adenocarcinoma.
Bibliographic record
Abstract
Pancreatic cancer is the fifth leading cause of cancer deaths. Five-year survival rate is In our initial screen, whole-exome sequencing of 33 primary PDAC tumors and matched controls has been performed on the Illumina HiSeq 2000. Sequence alignment and variant calling have been performed using Novoalign and GATK, respectively. After manual review and validation on the Ion Torrent platform, we have identified 648 somatic mutations, 471 of which are non-silent mutations that impact 444 genes. Our results confirm several known mutations in PDAC such as KRAS, p53 and SMAD4. However, their mutation frequencies are lower than expected due to tumor cellularity. We have also screened for copy number alterations (CNAs) using Illumina Omni1-Quad BeadChip. Analysis was performed using Genome Studio, KSseg and PennCNV. In the 33 primary tumors, a median of 90 regions with copy number gain, copy number loss, or copy-neutral LOH have been detected per sample. Median genomic lengths are 19Mb and 20Mb in regions with copy number gain and loss, respectively. Annotation of the altered regions has identified 9152 protein-coding genes, miRNA and non-coding RNA that are altered in 4 or more tumors. To identify the pathways that contribute to PDAC, we have analyzed the genes with somatic mutations or CNAs by means of a functional interaction (FI) network. The FI network consists of curated pathways from Reactome and other databases and a high confidence set of functional interactions predicted by machine learning techniques. A PDAC-specific subnetwork is constructed by projecting the altered genes onto the FI network, and subsequently analyzed by a community clustering algorithm to identify network modules. These modules have been identified as KRAS, p53, TGFβ, Hedgehog, Integrin, Cadherin, Wnt, Rho GTPase and G-protein signaling pathways. While our effort in identifying driver mutations is ongoing, our initial screen has identified candidate genes that will be targeted for deep sequencing in all primary tumors. We will continue to perform whole-exome sequencing of other primary tumors along with xenografts derived from some of the primaries and cell lines derived from some of the xenografts. In addition, whole-genome sequencing of selected specimens is being performed to complement the exome data. The wealth of data will help to characterize the genomic abnormalities in PDAC. Citation Format: Christina K. Yung, Christine Ouellete, Lee Timms, Michelle Sam, Kimberly Begley, Thomas J. Hudson, John D. McPherson, Lincoln D. Stein, Timothy Beck, Lakshmi Muthuswamy, Richard De Borja, Carson Holt, Rob Denroche, Fouad Yousif, Zheng Zha, Niloofar Arshadi. Genomic analysis of pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr B18.
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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 | 0.000 | 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 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".