Expression of Small Noncoding RNAs in Urinary Exosomes Classifies Prostate Cancer into Indolent and Aggressive Disease

  • Wei-Lin Winnie Wang
  • Igor Sorokin
  • Ilija Aleksic
  • Hugh Fisher
  • Ronald P Kaufman
  • Andrew Winer
  • Brian McNeill
  • Raavi Gupta
  • Derya Tilki
  • Neil Fleshner
  • Laurence Klotz
  • A Gregory DiRienzo
  • Martin Tenniswood

Abstract

PURPOSE: This is the first report of the development and performance of a platform that interrogates small noncoding RNAs (sncRNA) isolated from urinary exosomes. The Sentinel™ PCa Test classifies patients with prostate cancer from subjects with no evidence of prostate cancer, the miR Sentinel CS Test stratifies patients with prostate cancer between those with low risk prostate cancer (Grade Group 1) from those with intermediate and high risk disease (Grade Group 2-5), and the miR Sentinel HG Test stratifies patients with prostate cancer between those with low and favorable intermediate risk prostate cancer (Grade Group 1 or 2) and those with high risk (Grade Group 3-5) disease.

MATERIALS AND METHODS: sncRNAs were extracted from urinary exosomes of 235 participants and interrogated on miR 4.0 microarrays. Using proprietary selection and classification algorithms, informative sncRNAs were selected to customize an interrogation OpenArray™ platform that forms the basis of the tests. The tests were validated using a case-control sample of 1,436 subjects.

RESULTS: The performance of the miR Sentinel PCa Test demonstrated a sensitivity of 94% and specificity of 92%. The Sentinel CS Test demonstrated a sensitivity of 93% and specificity of 90% for prediction of the presence of Grade Group 2 or greater cancer, and the Sentinel HG Test demonstrated a sensitivity of 94% and specificity of 96% for the prediction of the presence of Grade Group 3 or greater cancer.

CONCLUSIONS: The Sentinel PCa, CS and HG Tests demonstrated high levels of sensitivity and specificity, highlighting the utility of interrogation of urinary exosomal sncRNAs for noninvasively diagnosing and classifying prostate cancer with high precision.

Bibliografische Daten

OriginalspracheEnglisch
ISSN0022-5347
DOIs
StatusVeröffentlicht - 09.2020
Extern publiziertJa
PubMed 32191585