Dataset Information

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study.

ABSTRACT:

Purpose

Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk.

Methods

Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC).

Results

In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS.

Conclusion

This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

SUBMITTER: Huang HP

PROVIDER: S-EPMC10566053 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study.

Huang Hsiang-Po HP Chen Chung-Hsin CH Chang Kai-Hsiung KH Lee Ming-Shyue MS Lee Cheng-Fan CF Chao Yen-Hsiang YH Lu Shih-Yu SY Wu Tzu-Fan TF Liang Sung-Tzu ST Lin Chih-Yu CY Lin Yuan Chi YC Liu Shih-Ping SP Lu Yu-Chuan YC Shun Chia-Tung CT Huang William J WJ Lin Tzu-Ping TP Ku Ming-Hsuan MH Chung Hsiao-Jen HJ Chang Yen-Hwa YH Liao Chun-Hou CH Yu Chih-Chin CC Chung Shiu-Dong SD Tsai Yao-Chou YC Wu Chia-Chang CC Chen Kuan-Chou KC Ho Chen-Hsun CH Hsiao Pei-Wen PW Pu Yeong-Shiau YS

Journal of translational medicine 20231011 1

<h4>Purpose</h4>Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk.<h4>Methods</h4>Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated be ...[more]

PMID: 37821919

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Project description:BackgroundPrevention of unnecessary biopsies and overtreatment of indolent disease remains a challenge in the management of prostate cancer. Novel non-invasive tests that can identify clinically significant (intermediate-risk and high-risk) diseases are needed to improve risk stratification and monitoring of prostate cancer patients. Here, we investigated a panel of six DNA methylation biomarkers in urine samples collected post-digital rectal exam from patients undergoing prostate biopsy, for their utility to guide decision making for diagnostic biopsy and early detection of aggressive prostate cancer.ResultsWe recruited 408 patients in risk categories ranging from benign to low-, intermediate-, and high-risk prostate cancer from three international cohorts. Patients were separated into 2/3 training and 1/3 validation cohorts. Methylation biomarkers were analyzed in post-digital rectal exam urinary sediment DNA by quantitative MethyLight assay and investigated for their association with any or aggressive prostate cancers. We developed a Prostate Cancer Urinary Epigenetic (ProCUrE) assay based on an optimal two-gene (HOXD3 and GSTP1) LASSO model, derived from methylation values in the training cohort, and assessed ProCUrE's diagnostic and prognostic ability for prostate cancer in both the training and validation cohorts. ProCUrE demonstrated improved prostate cancer diagnosis and identification of patients with clinically significant disease in both the training and validation cohorts. Using three different risk stratification criteria (Gleason score, D'Amico criteria, and CAPRA score), we found that the positive predictive value for ProCUrE was higher (59.4-78%) than prostate specific antigen (PSA) (38.2-72.1%) for all risk category comparisons. ProCUrE also demonstrated additive value to PSA in identifying GS ≥ 7 PCa compared to PSA alone (DeLong's test p = 0.039), as well as additive value to the PCPT risk calculator for identifying any PCa and GS ≥ 7 PCa (DeLong's test p = 0.011 and 0.022, respectively).ConclusionsProCUrE is a promising non-invasive urinary methylation assay for the early detection and prognostication of prostate cancer. ProCUrE has the potential to supplement PSA testing to identify patients with clinically significant prostate cancer.

Project description:PurposePreclinical evaluation of PCA3 and AMACR transcript simultaneous detection in urine to diagnose clinical significant prostate cancer (prostate cancer with Gleason score ≥7) in a Russian cohort.Patients and methodsWe analyzed urine samples of patients with a total serum PSA ≥2 ng/mL: 31 men with prostate cancer scheduled for radical prostatectomy, 128 men scheduled for first diagnostic biopsy (prebiopsy cohort). PCA3, AMACR, PSA and GPI transcripts were detected by multiplex reverse transcription quantitative polymerase chain reaction, and the results were used for scores for calculation and statistical analysis.ResultsThere was no significant difference between clinically significant and nonsignificant prostate cancer PCA3 scores. However, there was a significant difference in the AMACR score (patients scheduled for radical prostatectomy p=0.0088, prebiopsy cohort p=0.029). We estimated AUCs, optimal cutoffs, sensitivities and specificities for PCa and csPCa detection in the prebiopsy cohort by tPSA, PCA3 score, PCPT Risk Calculator and classification models based on tPSA, PCA3 score and AMACR score. In the clinically significant prostate cancer ROC analysis, the PCA3 score AUC was 0.632 (95%CI: 0.511-0.752), the AMACR score AUC was 0.711 (95%CI: 0.617-0.806) and AUC of classification model based on the PCA3 score, the AMACR score and total PSA was 0.72 (95%CI: 0.58-0.83). In addition, the correlation of the AMACR score with the ratio of total RNA and RNA of prostate cells in urine was shown (tau=0.347, p=6.542e-09). Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use.ConclusionThe AMACR score is a good predictor of clinically significant prostate cancer. Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Evaluation of the AMACR score and classification models based on it for clinically significant prostate cancer detection with larger samples and a follow-up analysis is promising.

Dataset Information

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study.

Purpose

Methods

Results

Conclusion

Publications

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study.

Similar Datasets

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