Molecular Biomarkers for the Detection of Clinically Significant Prostate Cancer: A Systematic Review and Meta-analysis

Abstract

Context: Prostate cancer (PCa) is the second most common type of cancer in men. Individualized risk stratification is crucial to adjust decision-making. A variety of molecular biomarkers have been developed in order to identify patients at risk of clinically significant PCa (csPCa) defined by the most common PCa risk stratification systems.

Objective: The present study aims to examine the effectiveness (diagnostic accuracy) of blood or urine-based PCa biomarkers to identify patients at high risk of csPCa.

Evidence acquisition: A systematic review of the literature was conducted. Medline and EMBASE were searched from inception to March 2021. Randomized or nonrandomized clinical trials, and cohort and case-control studies were eligible for inclusion. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Pooled estimates of sensitivity, specificity, and area under the curve were obtained.

Evidence synthesis: Sixty-five studies (N = 34 287) were included. Not all studies included prostate-specific antigen-selected patients. The pooled data showed that the Prostate Health Index (PHI), with any cutoff point between 15 and 30, had sensitivity of 0.95-1.00 and specificity of 0.14-0.33 for csPCa detection. The pooled estimates for SelectMDx test sensitivity and specificity were 0.84 and 0.49, respectively.

Conclusions: The PHI test has a high diagnostic accuracy rate for csPCa detection, and its incorporation in the diagnostic process could reduce unnecessary biopsies. However, there is a lack of evidence on patient-important outcomes and thus more research is needed.

Patient summary: It has been possible to verify that the application of biomarkers could help detect prostate cancer (PCa) patients with a higher risk of poorer evolution. The Prostate Health Index shows an ability to identify 95-100 for every 100 patients suffering from clinically significant PCa who take the test, preventing unnecessary biopsies in 14-33% of men without PCa or insignificant PCa.

Keywords: Clinically significant cancer; Meta-analysis; Molecular markers; Prostatic neoplasms; Systematic review.

Fig. 1

PRISMA flow chart detailing the screening process. PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-analyses.

Fig. 2

Risk of bias and applicability concerns (QUADAS-2 tool): (A) across studies and (B) within studies. QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies-2.

Fig. 2

Risk of bias and applicability concerns (QUADAS-2 tool): (A) across studies and (B) within studies. QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies-2.

Fig. 3

Accuracy of SelectMDx test for csPCa detection. (A) Forest plot of sensitivity and specificity. (B) Summary receiver operating characteristic (SROC) curve. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 3

Accuracy of SelectMDx test for csPCa detection. (A) Forest plot of sensitivity and specificity. (B) Summary receiver operating characteristic (SROC) curve. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 4

Accuracy of PHI test for csPCa detection. (A) Forest plot of sensitivity and specificity: cutoff point 15–20. (B) Summary receiver operating characteristic (SROC) curve: cutoff point 15–20. (C) Forest plot of sensitivity and specificity: cutoff point 20–25. (D) SROC curve: cutoff point 20–25. (E) Forest plot of sensitivity and specificity: cutoff point 25–30. (F) SROC curve: cutoff point 25–30. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; PHI = Prostate Health Index; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 4

Accuracy of PHI test for csPCa detection. (A) Forest plot of sensitivity and specificity: cutoff point 15–20. (B) Summary receiver operating characteristic (SROC) curve: cutoff point 15–20. (C) Forest plot of sensitivity and specificity: cutoff point 20–25. (D) SROC curve: cutoff point 20–25. (E) Forest plot of sensitivity and specificity: cutoff point 25–30. (F) SROC curve: cutoff point 25–30. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; PHI = Prostate Health Index; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 4

Accuracy of PHI test for csPCa detection. (A) Forest plot of sensitivity and specificity: cutoff point 15–20. (B) Summary receiver operating characteristic (SROC) curve: cutoff point 15–20. (C) Forest plot of sensitivity and specificity: cutoff point 20–25. (D) SROC curve: cutoff point 20–25. (E) Forest plot of sensitivity and specificity: cutoff point 25–30. (F) SROC curve: cutoff point 25–30. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; PHI = Prostate Health Index; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 4

Accuracy of PHI test for csPCa detection. (A) Forest plot of sensitivity and specificity: cutoff point 15–20. (B) Summary receiver operating characteristic (SROC) curve: cutoff point 15–20. (C) Forest plot of sensitivity and specificity: cutoff point 20–25. (D) SROC curve: cutoff point 20–25. (E) Forest plot of sensitivity and specificity: cutoff point 25–30. (F) SROC curve: cutoff point 25–30. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; PHI = Prostate Health Index; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 4

Accuracy of PHI test for csPCa detection. (A) Forest plot of sensitivity and specificity: cutoff point 15–20. (B) Summary receiver operating characteristic (SROC) curve: cutoff point 15–20. (C) Forest plot of sensitivity and specificity: cutoff point 20–25. (D) SROC curve: cutoff point 20–25. (E) Forest plot of sensitivity and specificity: cutoff point 25–30. (F) SROC curve: cutoff point 25–30. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; PHI = Prostate Health Index; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Fig. 4

Accuracy of PHI test for csPCa detection. (A) Forest plot of sensitivity and specificity: cutoff point 15–20. (B) Summary receiver operating characteristic (SROC) curve: cutoff point 15–20. (C) Forest plot of sensitivity and specificity: cutoff point 20–25. (D) SROC curve: cutoff point 20–25. (E) Forest plot of sensitivity and specificity: cutoff point 25–30. (F) SROC curve: cutoff point 25–30. AUC = area under the curve; CI = confidence interval; csPCa = clinically significant prostate cancer; FN = false negative; FP = false positive; PHI = Prostate Health Index; SENS = sensitivity; SPEC = specificity; TN = true negative; TP = true positive.

Supplementary figure