Skip to main content
Top
Published in: BMC Medicine 1/2020

01-12-2020 | Prostate Cancer | Research article

Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up

Authors: Heather Johnson, Jinan Guo, Xuhui Zhang, Heqiu Zhang, Athanasios Simoulis, Alan H. B. Wu, Taolin Xia, Fei Li, Wanlong Tan, Allan Johnson, Nishtman Dizeyi, Per-Anders Abrahamsson, Lukas Kenner, Xiaoyan Feng, Chang Zou, Kefeng Xiao, Jenny L. Persson, Lingwu Chen

Published in: BMC Medicine | Issue 1/2020

Login to get access

Abstract

Background

Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies.

Methods

Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy.

Results

The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963–0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929–0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956–0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980–0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947–0.909) in the combination cohort (n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy.

Conclusions

The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.
Appendix
Available only for authorised users
Literature
1.
go back to reference Taitt HE. Global trends and prostate cancer: a review of incidence, detection, and mortality as influenced by race, ethnicity, and geographic location. Am J Mens Health. 2018;12(6):1807–23.CrossRef Taitt HE. Global trends and prostate cancer: a review of incidence, detection, and mortality as influenced by race, ethnicity, and geographic location. Am J Mens Health. 2018;12(6):1807–23.CrossRef
2.
go back to reference Tonry CL, Leacy E, Raso C, et al. The role of proteomics in biomarker development for improved patient diagnosis and clinical decision making in prostate cancer. Diagnostics (Basel). 2016;6(3):27.CrossRef Tonry CL, Leacy E, Raso C, et al. The role of proteomics in biomarker development for improved patient diagnosis and clinical decision making in prostate cancer. Diagnostics (Basel). 2016;6(3):27.CrossRef
3.
go back to reference Botchkina GI, Kim RH, Botchkina IL, Kirshenbaum A, Frischer Z, Adler HL. Noninvasive detection of prostate cancer by quantitative analysis of telomerase activity. Clin Cancer Res. 2005;11(9):3243–9.CrossRef Botchkina GI, Kim RH, Botchkina IL, Kirshenbaum A, Frischer Z, Adler HL. Noninvasive detection of prostate cancer by quantitative analysis of telomerase activity. Clin Cancer Res. 2005;11(9):3243–9.CrossRef
6.
go back to reference Raja N, Russell CM, George AK. Urinary markers aiding in the detection and risk stratification of prostate cancer. Transl Androl Urol. 2018;7(Suppl 4):S436–42.CrossRef Raja N, Russell CM, George AK. Urinary markers aiding in the detection and risk stratification of prostate cancer. Transl Androl Urol. 2018;7(Suppl 4):S436–42.CrossRef
7.
go back to reference Truong M, Yang B, Jarrard DF. Towards the detection of prostate cancer in urine: a critical analysis. J Urol. 2013;189(2):422–9.CrossRef Truong M, Yang B, Jarrard DF. Towards the detection of prostate cancer in urine: a critical analysis. J Urol. 2013;189(2):422–9.CrossRef
8.
go back to reference Matin F, Jeet V, Moya L, et al. A plasma biomarker panel of four microRNAs for the diagnosis of prostate cancer. Sci Rep. 2018;8(1):6653.CrossRef Matin F, Jeet V, Moya L, et al. A plasma biomarker panel of four microRNAs for the diagnosis of prostate cancer. Sci Rep. 2018;8(1):6653.CrossRef
9.
go back to reference Kelly RS, Heiden MV, Giovannucci EL, Mucci LA. Metabolomic biomarkers of prostate cancer: prediction, diagnosis, progression, prognosis and recurrence. Cancer Epidemiol Biomark Prev. 2016;25(6):887–906.CrossRef Kelly RS, Heiden MV, Giovannucci EL, Mucci LA. Metabolomic biomarkers of prostate cancer: prediction, diagnosis, progression, prognosis and recurrence. Cancer Epidemiol Biomark Prev. 2016;25(6):887–906.CrossRef
10.
go back to reference Fredsøe J, Rasmussen AKI, Thomsen AR, et al. Diagnostic and prognostic microRNA biomarkers for prostate cancer in cell-free urine. Eur Urol Focus. 2018;4(6):825–33.CrossRef Fredsøe J, Rasmussen AKI, Thomsen AR, et al. Diagnostic and prognostic microRNA biomarkers for prostate cancer in cell-free urine. Eur Urol Focus. 2018;4(6):825–33.CrossRef
11.
go back to reference Jamaspishvili T, Kral M, Khomeriki I, et al. Urine markers in monitoring for prostate cancer. Prostate Cancer Prostatic Dis. 2010;13(1):12–9.CrossRef Jamaspishvili T, Kral M, Khomeriki I, et al. Urine markers in monitoring for prostate cancer. Prostate Cancer Prostatic Dis. 2010;13(1):12–9.CrossRef
12.
go back to reference Carrion DM, Gómez Rivas J, Álvarez-Maestro M, Martínez-Piñeiro L. Biomarkers in prostate cancer management. Is there something new? Arch Esp Urol. 2019;72(2):105–15.PubMed Carrion DM, Gómez Rivas J, Álvarez-Maestro M, Martínez-Piñeiro L. Biomarkers in prostate cancer management. Is there something new? Arch Esp Urol. 2019;72(2):105–15.PubMed
13.
go back to reference Fuessel S, Wirth MP. New markers in prostate cancer: genomics. Arch Esp Urol. 2019;72(2):116–25.PubMed Fuessel S, Wirth MP. New markers in prostate cancer: genomics. Arch Esp Urol. 2019;72(2):116–25.PubMed
14.
go back to reference Vickers A, Carlsson SV. Toward responsible, informed decision making for prostate cancer treatment decisions. J Clin Oncol. 2019;30:JCO1900989. Vickers A, Carlsson SV. Toward responsible, informed decision making for prostate cancer treatment decisions. J Clin Oncol. 2019;30:JCO1900989.
15.
go back to reference Boyd LK, Mao X, Lu Y. The complexity of prostate cancer: genomic alterations and heterogeneity. Nature Reviews Urol. 2012;9:652–64.CrossRef Boyd LK, Mao X, Lu Y. The complexity of prostate cancer: genomic alterations and heterogeneity. Nature Reviews Urol. 2012;9:652–64.CrossRef
16.
go back to reference Wang Y, Xia XQ, Jia Z, Sawyers A, et al. In silico estimates of tissue components in surgical samples based on expression profiling data. Cancer Res. 2010;70(16):6448–55.CrossRef Wang Y, Xia XQ, Jia Z, Sawyers A, et al. In silico estimates of tissue components in surgical samples based on expression profiling data. Cancer Res. 2010;70(16):6448–55.CrossRef
17.
go back to reference Jia Z, Wang Y, Sawyers A, Yao H, et al. Diagnosis of prostate cancer using differentially expressed genes in stroma. Cancer Res. 2011;71(7):2476–87.CrossRef Jia Z, Wang Y, Sawyers A, Yao H, et al. Diagnosis of prostate cancer using differentially expressed genes in stroma. Cancer Res. 2011;71(7):2476–87.CrossRef
18.
go back to reference Kroneis T, Kroneis E, Andersson D, Dolatabadi S, Ståhlberg A. Global preamplification simplifies targeted mRNA quantification. Sci Rep. 2017;7:45219.CrossRef Kroneis T, Kroneis E, Andersson D, Dolatabadi S, Ståhlberg A. Global preamplification simplifies targeted mRNA quantification. Sci Rep. 2017;7:45219.CrossRef
19.
go back to reference Xiao K, Guo J, Zhang X, et al. Use of two gene panels for prostate cancer diagnosis and patient risk stratification. Tumour Biol. 2016;37(8):10115–22.CrossRef Xiao K, Guo J, Zhang X, et al. Use of two gene panels for prostate cancer diagnosis and patient risk stratification. Tumour Biol. 2016;37(8):10115–22.CrossRef
20.
go back to reference Guo J, Yang J, Zhang X, et al. A panel of biomarkers for diagnosis of prostate cancer using urine samples. Anticancer Res. 2018;38(3):1471–7.PubMed Guo J, Yang J, Zhang X, et al. A panel of biomarkers for diagnosis of prostate cancer using urine samples. Anticancer Res. 2018;38(3):1471–7.PubMed
21.
go back to reference Breiman L. Random forests. Machine Learning. 45(1):5–32. Breiman L. Random forests. Machine Learning. 45(1):5–32.
22.
go back to reference Zhao S, Yu J, Wang L. Machine learning based prediction of brain metastasis of patients with IIIA-N2 lung adenocarcinoma by a three-miRNA signature. Transl Oncol. 2018;11(1):157–67.CrossRef Zhao S, Yu J, Wang L. Machine learning based prediction of brain metastasis of patients with IIIA-N2 lung adenocarcinoma by a three-miRNA signature. Transl Oncol. 2018;11(1):157–67.CrossRef
23.
go back to reference Humphrey PA, Moch H, Cubilla AL, Ulbright TM, Reuter VE. The 2016 WHO classification of tumours of the urinary system and male genital organs-part B: prostate and bladder tumours. Eur Urol. 2016;70(1):106–19.CrossRef Humphrey PA, Moch H, Cubilla AL, Ulbright TM, Reuter VE. The 2016 WHO classification of tumours of the urinary system and male genital organs-part B: prostate and bladder tumours. Eur Urol. 2016;70(1):106–19.CrossRef
24.
go back to reference Loeb S, Carter HB, Berndt SI, Ricker W, Schaeffer EM. Complications after prostate biopsy: data from SEER-Medicare. J Urol. 2011;186(5):1830–4.CrossRef Loeb S, Carter HB, Berndt SI, Ricker W, Schaeffer EM. Complications after prostate biopsy: data from SEER-Medicare. J Urol. 2011;186(5):1830–4.CrossRef
25.
go back to reference Song C, Chen H, Song C. Research status and progress of the RNA or protein biomarkers for prostate cancer. Onco Targets Ther. 2019;12:2123–36.CrossRef Song C, Chen H, Song C. Research status and progress of the RNA or protein biomarkers for prostate cancer. Onco Targets Ther. 2019;12:2123–36.CrossRef
27.
go back to reference Tomlins SA, Day JR, Lonigro RJ, et al. Urine TMPRSS2:ERG plus PCA3 for individualized prostate cancer risk assessment. Eur Urol. 2016;70(1):45–53.CrossRef Tomlins SA, Day JR, Lonigro RJ, et al. Urine TMPRSS2:ERG plus PCA3 for individualized prostate cancer risk assessment. Eur Urol. 2016;70(1):45–53.CrossRef
28.
go back to reference Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics. 2006;62(1):221–9.CrossRef Pepe MS, Cai T, Longton G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics. 2006;62(1):221–9.CrossRef
29.
go back to reference Miftakhova R, Hedblom A, Semenas J, et al. Cyclin A1 and P450 aromatase promote homing and growth of stem-like prostate cancer cells to bone marrow. Cancer Res. 2016;76:2453–64.CrossRef Miftakhova R, Hedblom A, Semenas J, et al. Cyclin A1 and P450 aromatase promote homing and growth of stem-like prostate cancer cells to bone marrow. Cancer Res. 2016;76:2453–64.CrossRef
30.
go back to reference Galbraith MD, Bender H, Espinosa JM. Therapeutic targeting of transcriptional cyclin-dependent kinases. Transcription. 2019;10(2):118–36.CrossRef Galbraith MD, Bender H, Espinosa JM. Therapeutic targeting of transcriptional cyclin-dependent kinases. Transcription. 2019;10(2):118–36.CrossRef
31.
go back to reference Luo D, Ren H, Zhang W, Xian H, Lian K, Liu H. Clinicopathological and prognostic value of hypoxia-inducible factor-1α in patients with bone tumor: a systematic review and meta-analysis. J Orthop Surg Res. 2019;14(1):56.CrossRef Luo D, Ren H, Zhang W, Xian H, Lian K, Liu H. Clinicopathological and prognostic value of hypoxia-inducible factor-1α in patients with bone tumor: a systematic review and meta-analysis. J Orthop Surg Res. 2019;14(1):56.CrossRef
32.
go back to reference Wang K, Peng HL, Li LK. Prognostic value of vascular endothelial growth factor expression in patients with prostate cancer: a systematic review with meta-analysis. Asian Pac J Cancer Prev. 2012;13(11):5665–9.CrossRef Wang K, Peng HL, Li LK. Prognostic value of vascular endothelial growth factor expression in patients with prostate cancer: a systematic review with meta-analysis. Asian Pac J Cancer Prev. 2012;13(11):5665–9.CrossRef
33.
go back to reference Semenas J, Hedblom A, Miftakhova RR, et al. The role of PI3K/AKT-related PIP5K1α and the discovery of its selective inhibitor for treatment of advanced prostate cancer. Proc Natl Acad Sci U S A. 2014;111(35):E3689–98.CrossRef Semenas J, Hedblom A, Miftakhova RR, et al. The role of PI3K/AKT-related PIP5K1α and the discovery of its selective inhibitor for treatment of advanced prostate cancer. Proc Natl Acad Sci U S A. 2014;111(35):E3689–98.CrossRef
34.
go back to reference Jetten AM, Suter U. The peripheral myelin protein 22 and epithelial membrane protein family. Prog Nucleic Acid Res Mol Biol. 2000;64:97–129.CrossRef Jetten AM, Suter U. The peripheral myelin protein 22 and epithelial membrane protein family. Prog Nucleic Acid Res Mol Biol. 2000;64:97–129.CrossRef
Metadata
Title
Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up
Authors
Heather Johnson
Jinan Guo
Xuhui Zhang
Heqiu Zhang
Athanasios Simoulis
Alan H. B. Wu
Taolin Xia
Fei Li
Wanlong Tan
Allan Johnson
Nishtman Dizeyi
Per-Anders Abrahamsson
Lukas Kenner
Xiaoyan Feng
Chang Zou
Kefeng Xiao
Jenny L. Persson
Lingwu Chen
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2020
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-020-01834-0

Other articles of this Issue 1/2020

BMC Medicine 1/2020 Go to the issue