Jouhyun Jeon1, Ekaterina Olkhov-Mitsel2, Honglei Xie1, Cindy Q Yao1, Fang Zhao2, Sahar Jahangiri3, Carmelle Cuizon2, Seville Scarcello3, Renu Jeyapala2, John D Watson1, Michael Fraser1, Jessica Ray3, Kristina Commisso3, Andrew Loblaw3, Neil E Fleshner4, Robert G Bristow4,5,6, Michelle Downes1, Danny Vesprini3, Stanley Liu3,5, Bharati Bapat2,7, Paul C Boutros1,5,8,9,10,11,12,13. 1. Ontario Institute for Cancer Research, Toronto, ON, Canada. 2. Lunenfeld-Tannenbaum Research Institute, Sinai Health System, Toronto, ON, Canada. 3. Sunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada. 4. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada. 5. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. 6. Manchester Cancer Research Centre, University of Manchester, Manchester, UK. 7. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada. 8. Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada. 9. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA. 10. Department of Urology, University of California, Los Angeles, Los Angeles, CA. 11. Broad Stem Cell Research Centre, University of California, Los Angeles, Los Angeles, CA. 12. Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA. 13. Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA.
Abstract
BACKGROUND: The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. METHODS: We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). RESULTS: We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). CONCLUSIONS: These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.
BACKGROUND: The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. METHODS: We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). RESULTS: We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). CONCLUSIONS: These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.
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