Abstract 974: Characterization of the novel lncRNA, PCAT14, clinically associated with metastatic prostate cancer
Bibliographic record
Abstract
Abstract Each year, over 180,000 men are diagnosed with prostate cancer in the United States. Advances in research have established a molecular stratification of prostate cancer disease improving screening and treatment options. However, some patients lack these genetic aberrations, indicating that prostate tumors may harbor disease-associated noncoding RNAs that further characterize molecular subtypes. Long non-coding RNAs (lncRNAs) are largely unexplored and are emerging as a new aspect of cancer biology through advances in sequencing technologies. To discover novel transcripts, and overcome the shortcomings of relying on incomplete or inaccurate annotations, we focused on an approach using a genome-wide annotation-independent method to identify regions of differential expression named SWORD (Sliding WindOw Region Discovery tool). We applied SWORD to recently generated data from aggressive prostate tumors and adjacent normal tissue. We discovered ten novel lncRNAs including the previously annotated Prostate Cancer Associated Transcript-14 (PCAT14). PCAT14 was consistently altered in an integrative analysis performed across three patient cohorts consisting of primary tumors with matched control transcriptome sequencing and Affymetrix gene expression including metastatic tumors. Utilizing the clinical data associated with the Affymetrix cohort, PCAT14 significantly associated with both high (9) and low (6) Gleason scores. Interestingly, PCAT14 is highly upregulated in primary tumors relative to control tissue and its expression is downregulated in metastatic tumors relative to primary tumors. These data suggest that PCAT14 expression promotes a metastatic phenotype. Therefore, we assessed PCAT14 expression within a cohort of 1008 radical prostatectomy specimens from three independent patient cohorts across institutes. We found that patients with high versus low PCAT14 expression showed significantly different rates of distant metastasis free survival, biochemical recurrence free survival, prostate cancer specific survival, and overall survival. Moreover, PCAT14 was implicated with protein-coding genes involved in biological processes promoting aggressive disease. In vitro experiments in prostate cancer cell lines further supported the clinical data associating PCAT14 with aggressive disease. Overall, we discovered that PCAT14 is broadly deregulated, promotes aggressive oncogenic phenotypes, and is significantly prognostic for multiple clinical endpoints supporting its significance for predicting metastatic disease. Due to its tissue-specific expression PCAT14 may serve as a valuable biomarker to define a subgroup of high-grade prostate carcinomas and improve disease management and patient prognosis. Citation Format: Nicole M. White, George Zhao, Jin Zhang, Elias Davicioni, Christopher A. Maher. Characterization of the novel lncRNA, PCAT14, clinically associated with metastatic prostate cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 974.
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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) | 0.001 | 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 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".