PD05-03 MOLECULAR HALLMARKS OF MPMRI VISIBILITY IN PROSTATE CANCER
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology I (PD05)1 Apr 2019PD05-03 MOLECULAR HALLMARKS OF MPMRI VISIBILITY IN PROSTATE CANCER Taylor Y. Sadun*, Kathleen E. Houlahan, Amirali Salmasi, Aydin Pooli, Ely R. Felker, Steven S. Raman, Preeti Ahuja, Anthony E. Sisk, Paul C. Boutros, and Robert E. Reiter Taylor Y. Sadun*Taylor Y. Sadun* More articles by this author , Kathleen E. HoulahanKathleen E. Houlahan More articles by this author , Amirali SalmasiAmirali Salmasi More articles by this author , Aydin PooliAydin Pooli More articles by this author , Ely R. FelkerEly R. Felker More articles by this author , Steven S. RamanSteven S. Raman More articles by this author , Preeti AhujaPreeti Ahuja More articles by this author , Anthony E. SiskAnthony E. Sisk More articles by this author , Paul C. BoutrosPaul C. Boutros More articles by this author , and Robert E. ReiterRobert E. Reiter More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555063.11964.56AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Multiparametric MRI (mpMRI) has transformed prostate cancer (PCa) management by improving identification of clinically significant disease. However, ∼20% of primary prostate tumors are invisible to mpMRI. We hypothesize that differences in functional mpMRI visibility reflect fundamental molecular properties of a tumor. METHODS: We profiled the transcriptomic and copy number profile of 40 Gleason Grade Group 2 tumors treated by prostatectomy. Twenty tumors were mpMRI invisible (PI-RADSv2: 1-2), while 20 tumors were visible (PI-RADsv2: 5). RESULTS: Copy number aberrations (CNAs) and mRNA abundance were analyzed. Univariate analysis identified 102 transcripts differentially abundant between visible vs invisible tumors. Unexpectedly, non-coding transcripts comprised the majority of differentially abundant RNAs (57/102 transcripts). In particular, snoRNAs were significantly more likely to have elevated abundance in visible tumors (OR=4.4; FDR=1.6x10-3). Perhaps most provocatively, SCHLAP1, a lncRNA linked to PCa progression, was more abundant in visible tumors (log2FC=3.2, FDR=0.028; Figure 1A). Additionally, visible tumors harbored significantly more unstable genomes, quantified as the percentage of the genome altered via CNAs (PGA; P=0.036; log2FC=2.3; Figure 1B). Concordantly, intraductal carcinoma (IDC) and cribriform architecture (CA) were enriched in PI-RADSv2 5 tumors (OR = 7.0; P=0.031; Figure 1C). Finally, we quantified a synergy between hallmarks and found the odds of visibility to be 10-fold higher with co-occurrence of ≥2 hallmarks (OR=10; P=5.7x10-3; Figure 1D). Nimbosus hallmarks synergized with snoRNA levels to predict visibility with 87% accuracy, superior to the 60% accuracy of the clinical signature, suggesting elevated snoRNA abundance may be a novel hallmark of nimbotic tumors (AUC=0.87, 95% CI: 0.75-0.99; Figure 1E). CONCLUSIONS: This work points to a novel model for the origin of mpMRI visibility involving the co-occurrence of multiple aggressive hallmarks, reminiscent of nimbosus. These hallmarks include IDC/CA pathology, increased PGA and overexpression of key non-coding transcripts, such as SCHLAP1 and snoRNAs. This co-occurrence results in an aggressive tumor phenotype, with poor patient outcome. Source of Funding: UCLA SPORE In Prostate Cancer, NIH/NCI grant number P50 CA092131 Los Angeles, CA; Toronto, Canada; San Diego, CA; Los Angeles, CA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e80-e81 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Taylor Y. Sadun* More articles by this author Kathleen E. Houlahan More articles by this author Amirali Salmasi More articles by this author Aydin Pooli More articles by this author Ely R. Felker More articles by this author Steven S. Raman More articles by this author Preeti Ahuja More articles by this author Anthony E. Sisk More articles by this author Paul C. Boutros More articles by this author Robert E. Reiter More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.015 |
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".