European urology oncology · 2021
Development of a Whole-urine, Multiplexed, Next-generation RNA-sequencing Assay for Early Detection of Aggressive Prostate Cancer.
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Cani Andi K, Hu Kevin, Liu Chia-Jen, Siddiqui Javed, Zheng Yingye, Han Sumin, Nallandhighal Srinivas, Hovelson Daniel H, Xiao Lanbo, Pham Trinh, Eyrich Nicholas W, Zheng Heng, Vince Randy, Tosoian Jeffrey J, Palapattu Ganesh S, Morgan Todd M, Wei John T, Udager Aaron M, Chinnaiyan Arul M, Tomlins Scott A, Salami Simpa S
Аннотация
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.
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.