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| 2. |
- Guo, Jinan, et al.
(författare)
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A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis
- 2024
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Ingår i: Prostate Cancer and Prostatic Diseases. - : Nature Publishing Group. - 1365-7852 .- 1476-5608.
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Tidskriftsartikel (refereegranskat)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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- Guo, Jinan, et al.
(författare)
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A panel of biomarkers for diagnosis of prostate cancer using urine samples
- 2018
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Ingår i: Anticancer research. - : Anticancer Research USA Inc.. - 0250-7005 .- 1791-7530. ; 38:3, s. 1471-1477
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Tidskriftsartikel (refereegranskat)abstract
- Background/Aim: Prostate cancer (PCa) diagnosis using patient urine samples represents a non-invasive and more convenient method than the conventional biopsy and prostate-specific antigen (PSA) test. This study intended to identify a biomarker panel to distinguish PCa from benign prostate using urine samples. Materials and Methods: We identified six biomarkers with differential gene expression in 154 PCa and benign prostate specimens. We then determined mRNA expression signature and the diagnostic performance of the 6-biomarker panel in 156 urine samples from patients with PCa and benign disease. Results: The 6-biomarker panel distinguished PCa from benign prostate cases with sensitivity of 80.6%, specificity of 62.9% and area under the curve (AUC) of 0.803 (p<0.0001), whereas serum PSA at 4 ng/ml cutoff had sensitivity of 95.5%, specificity of 20.2% and AUC of 0.521 (p<0.0001). Conclusion: The 6-biomarker panel for use in urine samples was able to distinguish PCa from benign prostate with higher specificity and accuracy than PSA and may be useful in clinical settings.
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| 4. |
- Guo, Jinan, et al.
(författare)
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Establishing a Urine-Based Biomarker Assay for Prostate Cancer Risk Stratification
- 2020
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Ingår i: Frontiers in Cell and Developmental Biology. - Swiss : Frontiers Media S.A.. - 2296-634X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- One of the major features of prostate cancer (PCa) is its heterogeneity, which often leads to uncertainty in cancer diagnostics and unnecessary biopsies as well as overtreatment of the disease. Novel non-invasive tests using multiple biomarkers that can identify clinically high-risk cancer patients for immediate treatment and monitor patients with low-risk cancer for active surveillance are urgently needed to improve treatment decision and cancer management. In this study, we identified 14 promising biomarkers associated with PCa and tested the performance of these biomarkers on tissue specimens and pre-biopsy urinary sediments. These biomarkers showed differential gene expression in higher- and lower-risk PCa. The 14-Gene Panel urine test (PMP22, GOLM1, LMTK2, EZH2, GSTP1, PCA3, VEGFA, CST3, PTEN, PIP5K1A, CDK1, TMPRSS2, ANXA3, and CCND1) was assessed in two independent prospective and retrospective urine study cohorts and showed high diagnostic accuracy to identify higher-risk PCa patients with the need for treatment and lower-risk patients for surveillance. The AUC was 0.897 (95% CI 0.939–0.855) in the prospective cohort (n = 202), and AUC was 0.899 (95% CI 0.964–0.834) in the retrospective cohort (n = 97). In contrast, serum PSA and Gleason score had much lower accuracy in the same 202 patient cohorts [AUC was 0.821 (95% CI 0.879–0.763) for PSA and 0.860 (95% CI 0.910–0.810) for Gleason score]. In addition, the 14-Gene Panel was more accurate at risk stratification in a subgroup of patients with Gleason scores 6 and 7 in the prospective cohort (n = 132) with AUC of 0.923 (95% CI 0.968–0.878) than PSA [AUC of 0.773 (95% CI 0.852–0.794)] and Gleason score [AUC of 0.776 (95% CI 0.854–0.698)]. Furthermore, the 14-Gene Panel was found to be able to accurately distinguish PCa from benign prostate with AUC of 0.854 (95% CI 0.892–0.816) in a prospective urine study cohort (n = 393), while PSA had lower accuracy with AUC of 0.652 (95% CI 0.706–0.598). Taken together, the 14-Gene Panel urine test represents a promising non-invasive tool for detection of higher-risk PCa to aid treatment decision and lower-risk PCa for active surveillance.
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| 5. |
- Guo, Jinan, et al.
(författare)
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Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
- 2021
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Ingår i: Frontiers in Medicine. - : Frontiers Media S.A.. - 2296-858X. ; 8
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Tidskriftsartikel (refereegranskat)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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| 6. |
- Johnson, Heather, et al.
(författare)
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Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up
- 2020
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Ingår i: BMC Medicine. - : BioMed Central. - 1741-7015. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Background: Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. Methods: Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRTPCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. Results: The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963–0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929–0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956–0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980–0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947–0.909) in the combination cohort (n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy. Conclusions: The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.
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| 7. |
- Xiao, Kefeng, et al.
(författare)
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Use of two gene panels for prostate cancer diagnosis and patient risk stratification
- 2016
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Ingår i: Tumor Biology. - : Springer. - 1010-4283 .- 1423-0380. ; 37:8, s. 10115-10122
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Tidskriftsartikel (refereegranskat)abstract
- Currently, no ideal prostate cancer (PCa) diagnostic or prognostic test is available due to the lack of biomarkers with high sensitivity and specificity. There is an unmet medical need to develop combinations of multiple biomarkers which may have higher accuracy in detection of PCa and stratification of aggressive and indolent cancer patients. The aim of this study was to test two biomarker gene panels in distinguishing PCa from benign prostate and high-risk, aggressive PCa from low-risk, indolent PCa, respectively. We identified a five-gene panel that can be used to distinguish PCa from benign prostate. The messenger RNA (mRNA) expression signature of the five genes was determined in 144 PCa and benign prostate specimens from prostatectomy. We showed that the five-gene panel distinguished PCa from benign prostate with sensitivity of 96.59 %, specificity of 92.86 %, and area under the curve (AUC) of 0.992 (p < 0.0001). The five-gene panel was further validated in a 137 specimen cohort and showed sensitivity of 84.62 %, specificity of 91.84 %, and AUC of 0.942 (p < 0.0001). To define subtypes of PCa for treatment guidance, we examined mRNA expression signature of an eight-gene panel in 87 PCa specimens from prostatectomy. The signature of the eight-gene panel was able to distinguish aggressive PCa (Gleason score >6) from indolent PCa (Gleason score ≤6) with sensitivity of 90.28 %, specificity of 80.00 %, and AUC of 0.967 (p < 0.0001). This panel was further validated in a 158 specimen cohort and showed significant difference between aggressive PCa and indolent PCa with sensitivity of 92.57 %, specificity of 70.00 %, and AUC of 0.962 (p < 0.0001). Our findings in assessing multiple biomarkers in combination may provide new tools to detect PCa and distinguish aggressive and indolent PCa for precision and personalized treatment. The two biomarker panels may be used in clinical settings for accurate PCa diagnosis and patient risk stratification for biomarker-guided treatment.
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