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Guo J, Gu L, Johnson H, Gu D, Lu Z, Luo B, Yuan Q, Zhang X, Xia T, Zeng Q, Wu AHB, Johnson A, Dizeyi N, Abrahamsson PA, Zhang H, Chen L, Xiao K, Zou C, Persson JL. A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis. Prostate Cancer Prostatic Dis 2024:10.1038/s41391-023-00758-z. [PMID: 38308042 DOI: 10.1038/s41391-023-00758-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 02/04/2024]
Abstract
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.
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Affiliation(s)
- Jinan Guo
- 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
| | - Liangyou Gu
- Department of Urology, The Third Medical Centre, Chinese PLA General Hospital, Beijing, China
| | | | - Di Gu
- Department of Urology, The First affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhenquan Lu
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Binfeng Luo
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Qian Yuan
- 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
| | - Xuhui Zhang
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Taolin Xia
- Department of Urology, Foshan First People's Hospital, Foshan, China
| | - Qingsong Zeng
- Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Alan H B Wu
- Clinical Laboratories, San Francisco General Hospital, San Francisco, CA, USA
| | | | - Nishtman Dizeyi
- Department of Translational Medicine, Lund University, Clinical Research Centre, Malmö, Sweden
| | - Per-Anders Abrahamsson
- Department of Translational Medicine, Lund University, Clinical Research Centre, Malmö, Sweden
| | - Heqiu Zhang
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Lingwu Chen
- Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Kefeng Xiao
- 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
| | - Chang Zou
- Department of Urology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), 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.
- Key Laboratory of Medical Electrophysiology of Education Ministry, School of Pharmacy, Southwest Medical University, Luzhou, China.
| | - Jenny L Persson
- Department of Molecular Biology, Umeå University, Umeå, Sweden.
- Department of Biomedical Sciences, Malmö University, Malmö, Sweden.
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K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer. Cancers (Basel) 2022; 14:cancers14215322. [PMID: 36358741 PMCID: PMC9657686 DOI: 10.3390/cancers14215322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/14/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.
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Johnson H, El-Schich Z, Ali A, Zhang X, Simoulis A, Wingren AG, Persson JL. Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients. Cancers (Basel) 2022; 14:cancers14082045. [PMID: 35454952 PMCID: PMC9030299 DOI: 10.3390/cancers14082045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.
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Affiliation(s)
| | - Zahra El-Schich
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Amjad Ali
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
| | - Xuhui Zhang
- Department of Bio-Diagnosis, Institute of Basic Medical Sciences, Beijing 100005, China;
| | - Athanasios Simoulis
- Department of Clinical Pathology and Cytology, Skåne University Hospital, SE-205 02 Malmö, Sweden;
| | - Anette Gjörloff Wingren
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
| | - Jenny L. Persson
- Department of Biomedical Sciences, Malmö University, SE-206 06 Malmö, Sweden; (Z.E.-S.); (A.G.W.)
- Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden;
- Correspondence: ; Tel.: +46-0706391199
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Guo J, Zhang X, Xia T, Johnson H, Feng X, Simoulis A, Wu AHB, Li F, Tan W, Johnson A, Dizeyi N, Abrahamsson PA, Kenner L, Xiao K, Zhang H, Chen L, Zou C, Persson JL. Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients. Front Med (Lausanne) 2021; 8:721554. [PMID: 34595190 PMCID: PMC8476767 DOI: 10.3389/fmed.2021.721554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/16/2021] [Indexed: 12/02/2022] Open
Abstract
Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.
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Affiliation(s)
- Jinan Guo
- Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), 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
| | - Xuhui Zhang
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Taolin Xia
- Department of Urology, Foshan First People's Hospital, Foshan, China
| | | | - Xiaoyan Feng
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Athanasios Simoulis
- Department of Clinical Pathology and Cytology, Skåne University Hospital, Malmö, Sweden
| | - Alan H B Wu
- Clinical Laboratories, San Francisco General Hospital, San Francisco, CA, United States
| | - Fei Li
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wanlong Tan
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | | | - Nishtman Dizeyi
- Department of Translational Medicine, Clinical Research Centre, Lund University, Malmö, Sweden
| | - Per-Anders Abrahamsson
- Department of Translational Medicine, Clinical Research Centre, Lund University, Malmö, Sweden
| | - Lukas Kenner
- Department of Experimental Pathology, Medical University Vienna & Unit of Laboratory Animal Pathology, University of Veterinary Medicine, Vienna, Austria
| | - Kefeng Xiao
- Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), 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
| | - Heqiu Zhang
- Department of Bio-diagnosis, Institute of Basic Medical Sciences, Beijing, China
| | - Lingwu Chen
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chang Zou
- Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), 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.,Key Laboratory of Medical Electrophysiology of Education Ministry, School of Pharmacy, Southwest Medical University, Luzhou, China
| | - Jenny L Persson
- Department of Molecular Biology, Umeå University, Umeå, Sweden.,Department of Biomedical Sciences, Malmö University, Malmö, Sweden.,Division of Experimental Cancer Research, Department of Translational Medicine, Lund University, Malmö, Sweden
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