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Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction
PURPOSE: Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk. EXPERI...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for Cancer Research
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502470/ https://www.ncbi.nlm.nih.gov/pubmed/37439796 http://dx.doi.org/10.1158/1078-0432.CCR-23-0570 |
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author | Salari, Keyan Sundi, Debasish Lee, Jason J. Wu, Shulin Wu, Chin-Lee DiFiore, Gabrielle Yan, Q. Robert Pienkny, Andrew Lee, Chi K. Oberlin, Daniel Barme, Greg Piser, Joel Kahn, Robert Collins, Edward Phillips, Kevin G. Caruso, Vincent M. Goudarzi, Mahdi Garcia-Ransom, Monica Lentz, Peter S. Evans-Holm, Martha E. MacBride, Andrew R. Fischer, Daniel S. Haddadzadeh, Iden J. Mazzarella, Brian C. Gray, Joe W. Koppie, Theresa M. Bicocca, Vincent T. Levin, Trevor G. Lotan, Yair Feldman, Adam S. |
author_facet | Salari, Keyan Sundi, Debasish Lee, Jason J. Wu, Shulin Wu, Chin-Lee DiFiore, Gabrielle Yan, Q. Robert Pienkny, Andrew Lee, Chi K. Oberlin, Daniel Barme, Greg Piser, Joel Kahn, Robert Collins, Edward Phillips, Kevin G. Caruso, Vincent M. Goudarzi, Mahdi Garcia-Ransom, Monica Lentz, Peter S. Evans-Holm, Martha E. MacBride, Andrew R. Fischer, Daniel S. Haddadzadeh, Iden J. Mazzarella, Brian C. Gray, Joe W. Koppie, Theresa M. Bicocca, Vincent T. Levin, Trevor G. Lotan, Yair Feldman, Adam S. |
author_sort | Salari, Keyan |
collection | PubMed |
description | PURPOSE: Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk. EXPERIMENTAL DESIGN: This is a multicenter case–control study using banked urine specimens collected from patients undergoing initial diagnosis/hematuria workup or urothelial carcinoma surveillance. A total of 581 samples were analyzed by uCGP: 333 for disease classification and grading algorithm development, and 248 for blinded validation. uCGP testing was done using the UroAmp platform, which identifies five classes of mutation: single-nucleotide variants, copy-number variants, small insertion-deletions, copy-neutral loss of heterozygosity, and aneuploidy. UroAmp algorithms predicting urothelial carcinoma tumor presence, grade, and recurrence risk were compared with cytology, cystoscopy, and pathology. RESULTS: uCGP algorithms had a validation sensitivity/specificity of 95%/90% for initial cancer diagnosis in patients with hematuria and demonstrated a negative predictive value (NPV) of 99%. A positive diagnostic likelihood ratio (DLR) of 9.2 and a negative DLR of 0.05 demonstrate the ability to risk-stratify patients presenting with hematuria. In surveillance patients, binary urothelial carcinoma classification demonstrated an NPV of 91%. uCGP recurrence-risk prediction significantly prognosticated future recurrence (hazard ratio, 6.2), whereas clinical risk factors did not. uCGP demonstrated positive predictive value (PPV) comparable with cytology (45% vs. 42%) with much higher sensitivity (79% vs. 25%). Finally, molecular grade predictions had a PPV of 88% and a specificity of 95%. CONCLUSIONS: uCGP enables noninvasive, accurate urothelial carcinoma diagnosis and risk stratification in both hematuria and urothelial carcinoma surveillance patients. |
format | Online Article Text |
id | pubmed-10502470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for Cancer Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-105024702023-09-16 Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction Salari, Keyan Sundi, Debasish Lee, Jason J. Wu, Shulin Wu, Chin-Lee DiFiore, Gabrielle Yan, Q. Robert Pienkny, Andrew Lee, Chi K. Oberlin, Daniel Barme, Greg Piser, Joel Kahn, Robert Collins, Edward Phillips, Kevin G. Caruso, Vincent M. Goudarzi, Mahdi Garcia-Ransom, Monica Lentz, Peter S. Evans-Holm, Martha E. MacBride, Andrew R. Fischer, Daniel S. Haddadzadeh, Iden J. Mazzarella, Brian C. Gray, Joe W. Koppie, Theresa M. Bicocca, Vincent T. Levin, Trevor G. Lotan, Yair Feldman, Adam S. Clin Cancer Res Precision Medicine and Imaging PURPOSE: Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk. EXPERIMENTAL DESIGN: This is a multicenter case–control study using banked urine specimens collected from patients undergoing initial diagnosis/hematuria workup or urothelial carcinoma surveillance. A total of 581 samples were analyzed by uCGP: 333 for disease classification and grading algorithm development, and 248 for blinded validation. uCGP testing was done using the UroAmp platform, which identifies five classes of mutation: single-nucleotide variants, copy-number variants, small insertion-deletions, copy-neutral loss of heterozygosity, and aneuploidy. UroAmp algorithms predicting urothelial carcinoma tumor presence, grade, and recurrence risk were compared with cytology, cystoscopy, and pathology. RESULTS: uCGP algorithms had a validation sensitivity/specificity of 95%/90% for initial cancer diagnosis in patients with hematuria and demonstrated a negative predictive value (NPV) of 99%. A positive diagnostic likelihood ratio (DLR) of 9.2 and a negative DLR of 0.05 demonstrate the ability to risk-stratify patients presenting with hematuria. In surveillance patients, binary urothelial carcinoma classification demonstrated an NPV of 91%. uCGP recurrence-risk prediction significantly prognosticated future recurrence (hazard ratio, 6.2), whereas clinical risk factors did not. uCGP demonstrated positive predictive value (PPV) comparable with cytology (45% vs. 42%) with much higher sensitivity (79% vs. 25%). Finally, molecular grade predictions had a PPV of 88% and a specificity of 95%. CONCLUSIONS: uCGP enables noninvasive, accurate urothelial carcinoma diagnosis and risk stratification in both hematuria and urothelial carcinoma surveillance patients. American Association for Cancer Research 2023-09-15 2023-07-13 /pmc/articles/PMC10502470/ /pubmed/37439796 http://dx.doi.org/10.1158/1078-0432.CCR-23-0570 Text en ©2023 The Authors; Published by the American Association for Cancer Research https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. |
spellingShingle | Precision Medicine and Imaging Salari, Keyan Sundi, Debasish Lee, Jason J. Wu, Shulin Wu, Chin-Lee DiFiore, Gabrielle Yan, Q. Robert Pienkny, Andrew Lee, Chi K. Oberlin, Daniel Barme, Greg Piser, Joel Kahn, Robert Collins, Edward Phillips, Kevin G. Caruso, Vincent M. Goudarzi, Mahdi Garcia-Ransom, Monica Lentz, Peter S. Evans-Holm, Martha E. MacBride, Andrew R. Fischer, Daniel S. Haddadzadeh, Iden J. Mazzarella, Brian C. Gray, Joe W. Koppie, Theresa M. Bicocca, Vincent T. Levin, Trevor G. Lotan, Yair Feldman, Adam S. Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction |
title | Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction |
title_full | Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction |
title_fullStr | Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction |
title_full_unstemmed | Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction |
title_short | Development and Multicenter Case–Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction |
title_sort | development and multicenter case–control validation of urinary comprehensive genomic profiling for urothelial carcinoma diagnosis, surveillance, and risk-prediction |
topic | Precision Medicine and Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502470/ https://www.ncbi.nlm.nih.gov/pubmed/37439796 http://dx.doi.org/10.1158/1078-0432.CCR-23-0570 |
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