Development and validation of the utLIFE-PC algorithm for noninvasive detection of prostate cancer in urine: A prospective, observational study

Abstract

Overbiopsy is a serious health issue in prostate cancer (PCa) diagnostics. We have developed a urine tumor DNA multidimensional bioinformatic algorithm, utLIFE, to avoid unnecessary biopsy. The objective is to recognize all or clinically significant PCa. Of the 801 participants recruited in our study, 630 are selected for subsequent analysis. In the training cohort (n = 237), utLIFE-PC gets an area under the receiver operating characteristic curve (AUC) of 0.967 and a sensitivity of 85.57% at 95% specificity. In the independent prospective validation cohort (n = 343), utLIFE-PC has an AUC of 0.929, sensitivity of 84.24%, and specificity of 93.26%. Notably, in patients with ≥grade group (GG)2 and ≥GG3, the assay's sensitivity is still excellent (85.33% and 87.10%, respectively). The model shows better performance than prostate-specific antigen (PSA) (p < 0.001) or the single-dimensional biomarkers (methylation, p < 0.001; copy-number variations [CNVs], p < 0.001; mutation, p < 0.001). The utLIFE-PC model can potentially optimize the PCa diagnostic process and avoid unnecessary biopsies. This study was registered at Chinese Clinical Trial Registry: ChiCTR2300071837.

Graphical abstract

Figure 1

Study design and participant enrollment In the discovery phase, 824 tissue samples and 237 urine samples were used to screen the mutation biomarkers. In all, 138 genes shared in tissue and urine were selected as candidate biomarkers. For methylation biomarkers screening, 10 pairs of urine samples and 531 tissue samples from TCGA were used. Two prostate cancer-related biomarkers shared in urine and TCGA were selected as candidate biomarkers. In the model construction and validation phase, the study recruited 801 participants. 171 cases (61 without biopsy results, 101 failed QC, 9 other cancers) were excluded according to the exclusion criteria, sample QC. Finally, 50 healthy participants were used for baseline construction; 237 participants were included in the training cohort; and 343 participants were included in the validation cohort. QC, quality control; PCa, prostate cancer; BxN, biopsy negative.

Figure 2

Stacked bar plots showing the proportions of mutation/methylation/CNV features in the case-control group of the training cohort (A) The blue box means the percentage of samples with mutations. The red box means the percentage of samples without mutations. The p values were calculated using the Fisher exact test. (B) CNV scores of 19 selected CNV biomarkers in urine. The blue boxplots represent the CNV scores in healthy populations and BxN patients. The red boxplots represent the CNV scores in PCa patients. Mann-Whitney U test was performed in statistical analysis. (C) Methylation levels of APC and GSTP1 in urine discriminate cases from controls. The blue boxplots represent the methylation levels in healthy populations and BxN patients. The red boxplots represent the methylation levels in PCa patients. PCa, prostate cancer; BxN, biopsy negative. Data are represented as mean ± SD. Mann-Whitney U test was performed in statistical analysis. A p value ≤0.05 was considered statistically significant (∗). A p value ≤0.01 was considered highly statistically significant (∗∗). A p value ≤0.001 was considered extremely statistically significant (∗∗∗). See also Figure S1 and Tables 1 and 2. All experiments have no repeats.

Figure 3

Schematic illustration of the utLIFE-PC algorithm Urine samples were collected from initial biopsy patients, as well as GUC participants and healthy controls. The ucfDNA was then extracted from the urine supernatant samples and subjected to target sequencing and whole-genome sequencing. The uexDNA was extracted from the urine sediment samples and subjected to qMSP. Mutation, CNV, and methylation features were extracted, and a base model was constructed. The DNA features were then calculated into a large matrix, which was subsequently trained by random forest. Then the utLIFE-PC algorithm was validated in a prospective independent cohort. See also Tables S3–S6.

Figure 4

Clinical performance of the utLIFE-PC model validation cohorts The receiver operating characteristic analysis (ROC) plots detailing AUC of different risk models: (A) any cancer in the training cohort, (C) any cancer in the validation cohort, (E) detection of ≥GG2 of validation cohort, and (G) detection of ≥GG3 of validation cohort. Decision curve analysis (DCA) plots detail the standardized net benefit (sNB) of adopting different risk models. Panels show the sNB based upon the detection of varying levels of disease severity: (B) any cancer of training cohort, (D) any cancer of validation cohort, (F) ≥GG2 PCa of validation cohort, and (H) ≥GG3 PCa of validation cohort. All these analyses are shown to compare the performances of the utLIFE-PC model, methylation features, CNV features, mutation features, and PSA. utLIFE-PC, utLIFE-PC model; Mut, mutation feature; CNV, CNV feature; Mut+CNV, model combined mutation and CNV; Meth, methylation feature; PSA, total prostate-specific antigen. (I) Analysis of the utLIFE-PC scores in relation to prostate biopsy outcomes in the validation cohort. utLIFE-PC score is shown on the y axis. Each colored bar represents an individual patient’s utLIFE-PC score and true biopsy diagnosis, increasing left to right (0–1 scale). Light green indicates benign biopsy; purple, biopsy GS 3 + 3; blue, biopsy GS 3 + 4; orange, dominant GG 3 or higher. Gray horizontal lines represent the cutoff point. A data statistic of different GGs is shown below the waterfall plot. utLIFE-PC score = risk score of utLIFE-PC model; BxN, biopsy negative; GG, grade group. See also Figures S2 and S3 and Tables S7–S10.