Abstract 1686: Identification of potential prostate cancer biomarkers associated with TMPRSS2-ERG fusion and PTEN deletions
Bibliographic record
Abstract
Abstract Background: ERG gene rearrangements and PTEN genomic deletions have been shown to be associated with specific molecular subtype of prostate cancer (PCA). It has been also suggested that tumors showing those two genetic aberrations are associated with worse prognosis compared to tumors with none of those genetic aberrations. In this study, we investigated gene expression differences between those two classes of tumors to identify potential genetic targets and pathways Material and Methods: 6144 informative genes belonging to 59 HRPCA samples were previously studied using the DASL platform. We used Singular Value Decomposition (SVD), as an effective method for analyzing gene expression data based on their ERG and PTEN status. We used the normal prostate cell line RWPE-1 as well as different prostate cancer cell lines (LNCaP, VCaP, DU145 and PC-3) to validate the results of microarray expression profiling. We screened for the expression of the different proteins by means of real time PCR and western blot studies. Results: MMP1 and LEF1 protein expression was much higher in VCaP cells and almost absent in RWPE-1 cells while MEIS2, CHD5 and Syntenin were expressed differently between cell lines and were not specifically in VCaP which is only cell line that harbor the ERG fusion. Conclusion: In this preliminary study we show that MPP1 and LEF-1 might be associated with the TMPRSS2-ERG fusion since their expression was much higher in VCaP cell line compared to other cell lines. However, further studies are still needed to confirm these results in TMPRSS2-ERG knockdown VCaP cells. 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 1686. doi:1538-7445.AM2012-1686
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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 | 0.000 | 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".