PD05-03 MOLECULAR HALLMARKS OF MPMRI VISIBILITY IN PROSTATE CANCER
Notice bibliographique
Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,054 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».