Abstract 4917: PRKCE and other genetic networks in osteosarcoma metastasis
Bibliographic record
Abstract
Abstract Osteosarcoma (OS) is the most common malignant bone cancer with approximately 1, 000 new cases reported annually in North America. Unfortunately OS has a poor 5-year survival rate (∼60%), and this is attributable largely to its propensity towards pulmonary metastasis: roughly a fifth (20%) of newly diagnosed cases are accompanied by macroscopic pulmonary metastases, and it is estimated that four fifths (80%) of all cases have undetectable microscopic metastases at diagnosis. The aim of the present study was to identify and characterize genetic networks that contribute to the metastasis of OS. Gene-expression profiling was performed on a cohort of 63 primary OS tumor samples: 46 with undetectable metastasis at diagnosis (‘localized’), and 17 that had detectable pulmonary metastasis at time of diagnosis (‘metastatic’). The relative abundance of transcripts from approximately 9, 000 genes was measured. This information was overlain on the Pathway Commons human interactome, and two independent methods of supervised network analysis were performed. Those networks with significantly differential gene expression, or ‘activity’ (Chuang et al. Mol. Sys. Biol. 2007), and those networks with significantly differential internal gene correlation, or ‘organization’ (Taylor et al. Nature Biotech. 2009) were discovered. Networks were assigned to biological processes using Gene Ontology Slim Generic. Real-time PCR was performed in vitro and in vivo to corroborate and validate the initial findings. The supervised network analysis has elucidated the 43 most significantly differentially activated, and 11 most significantly differentially organized networks in metastatic OS. One candidate differentially activated network, comprising RASGRP3, PRKCE and GNB2, was selected for follow-up. In vitro analysis in a panel of paired OS cell lines has corroborated the findings of the profiling screen: RASGRP3 was found to be under-expressed in some highly metastatic sub-lines, and PRKCE was found to be over-expressed in some highly metastatic sub-lines. Independent validation of PRKCE expression in the original tumor cohort has confirmed the increased expression in metastatic samples. A less stringent inspection of the network results reveals global trends in network aberrations: differentially activated networks are commonly found in transport, translation and protein modification processes, while dis-organized networks are commonly found in metabolism, signaling, transcription and organization processes. The study here described has revealed significant network aberrations in metastatic OS primary tumours. Additionally, several high-confidence networks have been identified for detailed follow-up. Subsequent in vitro and in vivo expression analysis has confirmed that one network is differentially expressed in metastatic OS. Knockdown assays are ongoing to demonstrate the impact of this network on OS metastatic progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4917. doi:10.1158/1538-7445.AM2011-4917
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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 | 0.000 |
| 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) | 0.006 | 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".