Potential of urine liquid biopsy in detecting of clinically relevant genomic alterations in advanced genitourinary cancers.
Abstract
e17133 Background: Liquid biopsy has transformed molecular profiling through non-invasive detection of tumor-derived genomic alterations; however, plasma-based assays may fail due to low circulating tumor DNA (ctDNA) burden, clonal evolution, or treatment-related effects. Urine is an emerging complementary biofluid that may potentially capture renal, urothelial and prostatic tumor DNA fragments and may detect clinically relevant alterations when plasma ctDNA is below detection thresholds. This study aims at evaluating the potential of urine biospecimen to detect molecular alterations in genitourinary cancers, especially renal, urothelial and prostatic cancers. Methods: Molecular spectrum analysis was retrospectively performed for 23 patients in multi-specimen (Plasma and Urine) sequenced by OncoIndx Next generation sequencing panel. ctDNA detection was performed for concordance and discordance across cancer types and sampling timepoints with urine findings as the critical aspect. Results: Among 23 patients with bladder or prostate cancer undergoing multi-specimen genomic testing, ctDNA positivity was detected in 39.1% (9/23) using plasma alone and 17.4% (4/23) using urine alone. Combined plasma and urine analysis increased overall ctDNA detection to 56.5% (13/23), identifying an additional 17.4% of ctDNA-positive cases that would have been missed by plasma-only testing. This enhanced detection supports earlier molecular identification of relapse and more timely clinical intervention. Conclusions: Urine-based liquid biopsy contributes to additional ctDNA detection rate when combined with plasma-based ctDNA detection alone. These findings support urine as a non-invasive, repeatable complementary or first-line genomic testing strategy, particularly when plasma ctDNA is undetectable.
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (9)
Jagdeesh Narhar Kulkarni
Bombay Hospital, Mumbai, India
Gowhar Shafi
1Cell.Ai, Mumbai, India
Trupti Inamdar
1Cell.Ai, Mumbai, India
Sandhya Iyer
Aarthi Ramesh
1Cell.Ai, Pune, India
Hrishita Kothavade
1Cell.Ai, Mumbai, India
Sumit Halder
1Cell.Ai, Pune, India
Madhura Basavalingegowda
1Cell.Ai, Mumbai, India
Ganesh Divekar
1Cell.Ai, Mumbai, India