TY - JOUR
T1 - Development and Multicenter Case-Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction
AU - Salari, Keyan
AU - Sundi, Debasish
AU - Lee, Jason J.
AU - Wu, Shulin
AU - Wu, Chin Lee
AU - DiFiore, Gabrielle
AU - Yan, Q. Robert
AU - Pienkny, Andrew
AU - Lee, Chi K.
AU - Oberlin, Daniel
AU - Barme, Greg
AU - Piser, Joel
AU - Kahn, Robert
AU - Collins, Edward
AU - Phillips, Kevin G.
AU - Caruso, Vincent M.
AU - Goudarzi, Mahdi
AU - Garcia-Ransom, Monica
AU - Lentz, Peter S.
AU - Evans-Holm, Martha E.
AU - MacBride, Andrew R.
AU - Fischer, Daniel S.
AU - Haddadzadeh, Iden J.
AU - Mazzarella, Brian C.
AU - Gray, Joe W.
AU - Koppie, Theresa M.
AU - Bicocca, Vincent T.
AU - Levin, Trevor G.
AU - Lotan, Yair
AU - Feldman, Adam S.
N1 - Publisher Copyright:
© 2023 The Authors.
PY - 2023/9/15
Y1 - 2023/9/15
N2 - 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, copyneutral 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 recurrencerisk 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.
AB - 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, copyneutral 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 recurrencerisk 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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U2 - 10.1158/1078-0432.CCR-23-0570
DO - 10.1158/1078-0432.CCR-23-0570
M3 - Article
C2 - 37439796
AN - SCOPUS:85171392970
SN - 1078-0432
VL - 29
SP - 3668
EP - 3680
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 18
ER -