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Non-invasive detection of urothelial cancer through the analysis of driver gene mutations and aneuploidy

Authors :
Simeon U Springer
Chung-Hsin Chen
Maria Del Carmen Rodriguez Pena
Lu Li
Christopher Douville
Yuxuan Wang
Joshua David Cohen
Diana Taheri
Natalie Silliman
Joy Schaefer
Janine Ptak
Lisa Dobbyn
Maria Papoli
Isaac Kinde
Bahman Afsari
Aline C Tregnago
Stephania M Bezerra
Christopher VandenBussche
Kazutoshi Fujita
Dilek Ertoy
Isabela W Cunha
Lijia Yu
Trinity J Bivalacqua
Arthur P Grollman
Luis A Diaz
Rachel Karchin
Ludmila Danilova
Chao-Yuan Huang
Chia-Tung Shun
Robert J Turesky
Byeong Hwa Yun
Thomas A Rosenquist
Yeong-Shiau Pu
Ralph H Hruban
Cristian Tomasetti
Nickolas Papadopoulos
Ken W Kinzler
Bert Vogelstein
Kathleen G Dickman
George J Netto
Source :
eLife, Vol 7 (2018)
Publication Year :
2018
Publisher :
eLife Sciences Publications Ltd, 2018.

Abstract

Current non-invasive approaches for detection of urothelial cancers are suboptimal. We developed a test to detect urothelial neoplasms using DNA recovered from cells shed into urine. UroSEEK incorporates massive parallel sequencing assays for mutations in 11 genes and copy number changes on 39 chromosome arms. In 570 patients at risk for bladder cancer (BC), UroSEEK was positive in 83% of those who developed BC. Combined with cytology, UroSEEK detected 95% of patients who developed BC. Of 56 patients with upper tract urothelial cancer, 75% tested positive by UroSEEK, including 79% of those with non-invasive tumors. UroSEEK detected genetic abnormalities in 68% of urines obtained from BC patients under surveillance who demonstrated clinical evidence of recurrence. The advantages of UroSEEK over cytology were evident in low-grade BCs; UroSEEK detected 67% of cases whereas cytology detected none. These results establish the foundation for a new non-invasive approach for detection of urothelial cancer.

Details

Language :
English
ISSN :
2050084X
Volume :
7
Database :
Directory of Open Access Journals
Journal :
eLife
Publication Type :
Academic Journal
Accession number :
edsdoj.035e9fc0aa4749039ab3ee51913706ab
Document Type :
article
Full Text :
https://doi.org/10.7554/eLife.32143