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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for Cancer Research 2023
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.
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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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