Anqi Cheng1, Shanshan Zhao2, Liesel M FitzGerald3, Jonathan L Wright4,5, Suzanne Kolb4, R Jeffrey Karnes6, Robert B Jenkins7, Elai Davicioni8, Elaine A Ostrander9, Ziding Feng4, Jian-Bing Fan10,11, James Y Dai1,4, Janet L Stanford4,12. 1. Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington. 2. Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina. 3. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia. 4. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. 5. Department of Urology, University of Washington School of Medicine, Seattle, Washington. 6. Department of Urology, Mayo Clinic, Rochester, Minnesota. 7. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota. 8. GenomeDx Biosciences Inc, Vancouver, British Columbia, Canada. 9. Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland. 10. AnchorDx Corporation, Guangzhou, China. 11. School of Basic Medical Sciences, Southern Medical University, Guangzhou, China. 12. Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington.
Abstract
BACKGROUND: Molecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed. METHODS: Based on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic-lethal events from those who remained recurrence-free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan-Meier curves and receiver operating characteristic analyses to evaluate the model's performance. RESULTS: Thirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10-11 ). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69-0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56-0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment-Surgery). CONCLUSION: Our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.
BACKGROUND: Molecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed. METHODS: Based on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic-lethal events from those who remained recurrence-free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan-Meier curves and receiver operating characteristic analyses to evaluate the model's performance. RESULTS: Thirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four-gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic-lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10-11 ). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69-0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56-0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment-Surgery). CONCLUSION: Our validated 4GT score has prognostic value for metastatic-lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.
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