Abstract 1166: <i>PTEN</i> loss and <i>ERG</i> over-expression as prognostic biomarkers in prostate cancer and identification of downstream biomarkers with potential therapeutic value
Bibliographic record
Abstract
Abstract Genomic deletion of the PTEN tumor suppressor gene and formation of the TMPRSS2:ERG gene fusion are the two most recurrent genomic aberrations in prostate cancer. The lipid phosphatase activity of PTEN negatively regulates the PI3Kinase-AKT signaling pathway, which controls numerous downstream targets such as cell cycle checkpoints, DNA damage repair with maintenance of chromosomal stability and integrity. ERG is a member of the ETS transcription factor family, whose members are implicated in numerous cellular processes including membrane remodelling, angiogenesis, differentiation, proliferation, and tumourigenesis. Emerging evidence suggests that formation of the fusion gene may promote prostatic tumourigenesis, progression, and invasive disease by elevating motility and invasiveness. The simultaneous manifestation of both PTEN loss and TMPRSS2:ERG fusion is associated with poor prognosis. This study is addressing pathways downstream of PTEN and effectors of ERG over-expression to identify additional biomarkers of prognostic and therapeutic potential. In silico genomic copy number analyses demonstrated that patient samples harboring a genomic deletion of PTEN have a greater number of genomic aberrations, in keeping with the model that loss of PTEN leads to heightened genomic instability. Mining of publically available gene expression datasets have been performed to further examine signalling pathway aberrations specific to each rearrangement. PTEN loss and ERG over-expression are being modeled in the histologically normal prostate epithelial cell line RWPE-1 using shRNA knockdown or transgene directed ectopic over-expression, respectively. Biochemical, proliferation, migration and invasion assays will be performed to determine if knockdown of PTEN leads to the expected AKT activation and increased proliferation; whereas over-expression of ERG in RWPE-1 cells correlates with heightened motility and invasive potential. Gene expression microarray profiling will also be carried out for each derivative RWPE-1 cell line using the Agilent SurePrint G3 Human Exon microarrays. This cell model system will be used to validate transcriptional changes associated with PTEN loss and ERG over-expression in prostate cancer gene expression datasets and identify potential novel downstream therapeutic targets and predictive biomarkers. 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 1166. doi:1538-7445.AM2012-1166
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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 | 0.001 |
| 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) | 0.002 | 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".