Development of a Whole-urine, Multiplexed, Next-generation RNA-sequencing Assay for Early Detection of Aggressive Prostate Cancer

Andi K Cani; Kevin Hu; Chia-Jen Liu; Javed Siddiqui; Yingye Zheng; Sumin Han; Srinivas Nallandhighal; Daniel H Hovelson; Lanbo Xiao; Trinh Pham; Nicholas W Eyrich; Heng Zheng; Randy Vince Jr; Jeffrey J Tosoian; Ganesh S Palapattu; Todd M Morgan; John T Wei; Aaron M Udager; Arul M Chinnaiyan; Scott A Tomlins; Simpa S Salami

doi:10.1016/j.euo.2021.03.002

Development of a Whole-urine, Multiplexed, Next-generation RNA-sequencing Assay for Early Detection of Aggressive Prostate Cancer

Eur Urol Oncol. 2022 Aug;5(4):430-439. doi: 10.1016/j.euo.2021.03.002. Epub 2021 Mar 31.

Authors

Andi K Cani¹, Kevin Hu², Chia-Jen Liu³, Javed Siddiqui⁴, Yingye Zheng⁵, Sumin Han⁴, Srinivas Nallandhighal⁶, Daniel H Hovelson⁷, Lanbo Xiao³, Trinh Pham⁶, Nicholas W Eyrich⁶, Heng Zheng³, Randy Vince Jr⁶, Jeffrey J Tosoian⁸, Ganesh S Palapattu⁹, Todd M Morgan⁹, John T Wei¹⁰, Aaron M Udager³, Arul M Chinnaiyan¹¹, Scott A Tomlins¹², Simpa S Salami¹³

Affiliations

¹ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Molecular and Cellular Pathology Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
² Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
³ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
⁴ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
⁵ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
⁶ Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
⁷ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
⁸ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
⁹ Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁰ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
¹¹ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Molecular and Cellular Pathology Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
¹² Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Molecular and Cellular Pathology Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: tomlinss@med.umich.edu.
¹³ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: simpa@med.umich.edu.

PMID: 33812851
DOI: 10.1016/j.euo.2021.03.002

Abstract

Background: Despite biomarker development advances, early detection of aggressive prostate cancer (PCa) remains challenging. We previously developed a clinical-grade urine test (Michigan Prostate Score [MiPS]) for individualized aggressive PCa risk prediction. MiPS combines serum prostate-specific antigen (PSA), the TMPRSS2:ERG (T2:ERG) gene fusion, and PCA3 lncRNA in whole urine after digital rectal examination (DRE).

Objective: To improve on MiPS with a novel next-generation sequencing (NGS) multibiomarker urine assay for early detection of aggressive PCa.

Design, setting, and participants: Preclinical development and validation of a post-DRE urine RNA NGS assay (Urine Prostate Seq [UPSeq]) assessing 84 PCa transcriptomic biomarkers, including T2:ERG, PCA3, additional PCa fusions/isoforms, mRNAs, lncRNAs, and expressed mutations. Our UPSeq model was trained on 73 patients and validated on a held-out set of 36 patients representing the spectrum of disease (benign to grade group [GG] 5 PCa).

Outcome measurements and statistical analysis: The area under the receiver operating characteristic curve (AUC) of UPSeq was compared with PSA, MiPS, and other existing models/biomarkers for predicting GG ≥3 PCa.

Results and limitations: UPSeq demonstrated high analytical accuracy and concordance with MiPS, and was able to detect expressed germline HOXB13 and somatic SPOP mutations. In an extreme design cohort (n = 109; benign/GG 1 vs GG ≥3 PCa, stratified to exclude GG 2 cancer in order to capture signal difference between extreme ends of disease), UPSeq showed differential expression for T2:ERG.T1E4 (1.2 vs 78.8 median normalized reads, p < 0.00001) and PCA3 (1024 vs 2521, p = 0.02), additional T2:ERG splice isoforms, and other candidate biomarkers. Using machine learning, we developed a 15-transcript model on the training set (n = 73) that outperformed serum PSA and sequencing-derived MiPS in predicting GG ≥3 PCa in the held-out validation set (n = 36; AUC 0.82 vs 0.69 and 0.69, respectively).

Conclusions: These results support the potential utility of our novel urine-based RNA NGS assay to supplement PSA for improved early detection of aggressive PCa.

Patient summary: We have developed a new urine-based test for the detection of aggressive prostate cancer, which promises improvement upon current biomarker tests.

Keywords: Algorithm; Biomarkers; Early detection; Machine learning; Mutations; Next-generation sequencing; Prostate cancer; RNA; Transcriptome; Urine detection.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Antigens, Neoplasm / genetics
Antigens, Neoplasm / urine
Biomarkers, Tumor
Early Detection of Cancer
High-Throughput Nucleotide Sequencing
Humans
Male
Nuclear Proteins / genetics
Prostate*
Prostate-Specific Antigen
Prostatic Neoplasms* / diagnosis
Prostatic Neoplasms* / genetics
RNA / urine
Repressor Proteins / genetics

Substances

Antigens, Neoplasm
Biomarkers, Tumor
Nuclear Proteins
Repressor Proteins
SPOP protein, human
RNA
Prostate-Specific Antigen

Grants and funding

T32 GM113900/GM/NIGMS NIH HHS/United States