A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis

TL;DR

A new non-invasive urine test using 25 genes can accurately detect prostate cancer spread to lymph nodes, potentially reducing unnecessary surgeries.

Contribution

The first non-invasive machine learning-based urine test for detecting prostate cancer pelvic lymph node metastasis.

Findings

The 25-G PLNM-Score achieved high accuracy (AUC 0.93) in both retrospective and prospective cohorts.

The test could spare 96% and 80% of unnecessary surgeries in retrospective and prospective groups, missing less than 1% of metastases.

It significantly outperformed the MSKCC nomogram in avoiding unnecessary surgeries while maintaining high sensitivity.

Abstract

Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM.

An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score.

An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85–1.01) and 0.93 (95% CI: 0.87–0.99) in the retrospective and prospective urine cohorts respectively. Kaplan–Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing.

The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

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