Abstract C25: The 16p13.3 genomic gain in prostate cancer: A role for PDK1 in disease progression
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is a leading cause of cancer death and distinguishing life threatening tumors from indolent ones is a major challenge. The identification and characterization of genomic alterations associated with advanced disease may lead to the development of new markers of progression and more efficient therapeutic approaches. Array-CGH data have shown that gain of chromosome 16p13.3 to be associated with lymph node (LN) metastases of PCa, but this region remained uncharacterized. Our goal was to establish the prognostic value of 16p13.3 gain, and identify the cancer relevant genes residing within this region. In this study, we performed Fluorescence In Situ Hybridization (FISH) to detect the copy number gain of chromosome 16p13.3 in 75 PCa samples including 10 lymph node (LN) metastases and their matched primary tumors, 9 transurethral resections of prostate (TURP) tissue samples of castration-resistant prostate cancer (CRPC), and 46 additional primary PCa specimens with clinicopathological parameters. We detected the gain in 5/10 LN metastases and 3/5 matched primary tumors, 3/9 CRPC samples, and 9/46 (20 %) primary tumors. In the latter set of samples, the 16p13.3 alteration was associated with high Gleason score (P=0.002) and elevated pre operative prostate specific antigen (PSA) levels (P=0.047). The levels of 16p13.3 gain were higher in LN metastasis and CRPC specimens compared to primary PCa (P>0.05). Chromosome mapping revealed a focal gain that spans PDPK1 encoding the 3-Phosphoinositide-dependent protein kinase-1 (PDK1). RNA interference-mediated knock down of PDK1 in three different PCa cell lines reduced cell motility without affecting growth and re-expressing PDK1 rescued motility (P>0.05). Our results support that the 16p13.3 gain is relevant to PCa progression and may represent an early marker of metastasis, since retrieved in primary PCa which is sampled by biopsies at time of diagnosis. PDK1 is implicated in PCa cell motility, a critical step for progression to metastasis. These findings provide further rationale for considering PDK1 as a target for cancer therapies and the development of new specific inhibitors of PDK1. Citation Format: Khalil Choucair, Fadi Brimo, Isabela W Cunha, Armen Aprikian, Martin Gleave, Jacques Lapointe, Karl-Philippe Guérard, Joshua Ejdelman, Simone Chevalier, Maisa Yoshimoto, Eleonora Scarlata, Ladan Fazli, Kanishka Sircar, Jeremy A. Squire. The 16p13.3 genomic gain in prostate cancer: A role for PDK1 in disease progression [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr C25.
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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 | 0.000 | 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) | 0.006 | 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".