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

Autor: Guo J; Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China.; Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, Shenzhen, China.; Shenzhen Urology Minimally Invasive Engineering Center, Shenzhen, China.; Shenzhen Public Service Platform on Tumor Precision Medicine and Molecular Diagnosis, Clinical Medicine Research Centre, Shenzhen, China.; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China., Gu L; Department of Urology, The Third Medical Centre, Chinese PLA General Hospital, Beijing, China., Johnson H; Olympia Diagnostics, Inc., Sunnyvale, CA, USA., Gu D; Department of Urology, The First affiliated Hospital of Guangzhou Medical University, Guangzhou, China., Lu Z; The University of Hong Kong-Shenzhen Hospital, Shenzhen, China., Luo B; The University of Hong Kong-Shenzhen Hospital, Shenzhen, China., Yuan Q; Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China.; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China., Zhang X; Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China., Xia T; Department of Urology, Foshan First People's Hospital, Foshan, China., Zeng Q; Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China., Wu AHB; Clinical Laboratories, San Francisco General Hospital, San Francisco, CA, USA., Johnson A; Kinetic Reality, Santa Clara, USA., Dizeyi N; Department of Translational Medicine, Lund University, Clinical Research Centre, Malmö, Sweden., Abrahamsson PA; Department of Translational Medicine, Lund University, Clinical Research Centre, Malmö, Sweden., Zhang H; Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China., Chen L; Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China., Xiao K; Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China.; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China., Zou C; Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, China. zou.chang@szhospital.com.; Shenzhen Urology Minimally Invasive Engineering Center, Shenzhen, China. zou.chang@szhospital.com.; Shenzhen Public Service Platform on Tumor Precision Medicine and Molecular Diagnosis, Clinical Medicine Research Centre, Shenzhen, China. zou.chang@szhospital.com.; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China. zou.chang@szhospital.com.; Key Laboratory of Medical Electrophysiology of Education Ministry, School of Pharmacy, Southwest Medical University, Luzhou, China. zou.chang@szhospital.com., Persson JL; Department of Molecular Biology, Umeå University, Umeå, Sweden. jenny.persson@umu.se.; Department of Biomedical Sciences, Malmö University, Malmö, Sweden. jenny.persson@umu.se.
Jazyk: angličtina
Zdroj: Prostate cancer and prostatic diseases [Prostate Cancer Prostatic Dis] 2024 Feb 02. Date of Electronic Publication: 2024 Feb 02.
DOI: 10.1038/s41391-023-00758-z
Abstrakt: Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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