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

  1. Simeon U Springer  Is a corresponding author
  2. Chung-Hsin Chen
  3. Maria Del Carmen Rodriguez Pena  Is a corresponding author
  4. Lu Li
  5. Christopher Douville  Is a corresponding author
  6. Yuxuan Wang
  7. Joshua David Cohen  Is a corresponding author
  8. Diana Taheri
  9. Natalie Silliman
  10. Joy Schaefer  Is a corresponding author
  11. Janine Ptak  Is a corresponding author
  12. Lisa Dobbyn  Is a corresponding author
  13. Maria Papoli  Is a corresponding author
  14. Isaac Kinde  Is a corresponding author
  15. Bahman Afsari
  16. Aline C Tregnago  Is a corresponding author
  17. Stephania M Bezerra
  18. Christopher VandenBussche  Is a corresponding author
  19. Kazutoshi Fujita
  20. Dilek Ertoy
  21. Isabela W Cunha  Is a corresponding author
  22. Lijia Yu
  23. Trinity J Bivalacqua  Is a corresponding author
  24. Arthur P Grollman
  25. Luis A Diaz
  26. Rachel Karchin
  27. Ludmila Danilova
  28. Chao-Yuan Huang
  29. Chia-Tung Shun
  30. Robert J Turesky
  31. Byeong Hwa Yun
  32. Thomas A Rosenquist
  33. Yeong-Shiau Pu
  34. Ralph H Hruban  Is a corresponding author
  35. Cristian Tomasetti
  36. Nickolas Papadopoulos
  37. Ken W Kinzler
  38. Bert Vogelstein  Is a corresponding author
  39. Kathleen G Dickman  Is a corresponding author
  40. George J Netto  Is a corresponding author
  1. Howard Hughes Medical Institute, Ludwig Center for Cancer Genetics and Therapeutics, United States
  2. Sidney Kimmel Comprehensive Cancer Center, United States
  3. National Taiwan University Hospital, Taiwan
  4. Johns Hopkins University, United States
  5. University of Alabama at Birmingham, United States
  6. Johns Hopkins Bloomberg School of Public Health, United States
  7. Isfahan University of Medical Sciences, Iran
  8. Johns Hopkins School of Medicine, United States
  9. AC Camargo Cancer Center, Brazil
  10. Osaka University, Japan
  11. Hacettepe University, Turkey
  12. Stony Brook University, United States
  13. Memorial Sloan Kettering Cancer Center, United States
  14. University of Minnesota, United States
6 figures, 2 tables and 22 additional files

Figures

Schematic of the approach used to evaluate urinary cells in this study.

UroSEEK assay is designed to detect urothelial neoplasms that are in direct contact with urine (A) of variable pathologic stages originating in upper urinary tract (B) or bladder (C).

https://doi.org/10.7554/eLife.32143.002
Flow diagram indicating the number of patients in the three cohorts evaluated in this study and summarizing the salient findings.

Cytology was performed on only a subset of the patients (see main text).

https://doi.org/10.7554/eLife.32143.003
Fraction of mutations found in the ten-gene panel in 231 urinary cell samples assessed in the BC early detection cohort, 56 urinary cell samples assessed in the UTUC cohort, and 132 urinary cell samples assessed in the BC surveillance cohort.
https://doi.org/10.7554/eLife.32143.005
Venn diagram showing the distribution of positive results for each of the three UroSEEK assays for the (A) BC early detection (B) UTUC and (C) BC surveillance cohorts.

URO = Ten gene panel, TERT = TERT promoter region, ANEU = Aneuploidy test.

https://doi.org/10.7554/eLife.32143.006
Bar graphs of the lead time between a positive UroSEEK test and the detection of disease at the clinical level in the (A) BC early detection and (B) BC surveillance cohorts.
https://doi.org/10.7554/eLife.32143.007
Bar graphs representing the performance of Cytology vs. UroSEEK in diagnosis of low- and high-grade urothelial neoplasms in the early detection and surveillance BC cohorts and the UTUC cohort.
https://doi.org/10.7554/eLife.32143.009

Tables

Table 1
Demographic, clinical and genetic features of the early detection cohort.
https://doi.org/10.7554/eLife.32143.004
Gendern%Ten-gene multiplex positiveTERT positiveAneuploidy positiveUroSEEK positiveCytology positive*Uroseek or cytology positive*
Table 1a. Demographic, clinical and genetic features of the early detection cohort
Males without recurrence17259%3 (2%)10 (6%)2 (1%)13 (8%)0 (0%)13 (8%)
Males with recurrence3211%26 (81%)21 (66%)19 (59%)29 (91%)16 (50%)30 (94%)
Females without recurrence8128%2 (2%)2 (2%)1 (1%)5 (6%)0 (0%)5 (6%)
Females with recurrence93%4 (44%)4 (44%)3 (33%)6 (67%)1 (11%)6 (67%)
Indication
Hematuria without recurrence34661%6 (2%)15 (4%)5 (1%)22 (6%)0 (0%)17 (5%)
Hematuria with recurrence16329%108 (66%)90 (55%)76 (47%)134 (82%)18 (11%)32 (2%)
LUTS without recurrence112%0 (0%)2 (18%)0 (0%)2 (18%)0 (0%)2 (18%)
LUTS with recurrence31%2 (67%)1 (33%)0 (0%)2 (67%)1 (33%)2 (67%)
Other without recurrence387%1 (3%)0 (0%)1 (3%)2 (5%)0 (0%)2 (5%)
Other with recurrence92%9 (100%)8 (89%)5 (56%)9 (100%)2 (22%)9 (100%)
Detected Tumor Diagnosis
PUNLMP21%0 (0%)1 (50%)0 (0%)1 (50%)0 (0%)0 (0%)
CIS75%4 (57%)4 (57%)1 (14%)6 (86%)3 (43%)6 (86%)
LGTCC3121%15 (48%)18 (58%)9 (29%)22 (71%)0 (0%)4 (13%)
HGTCC4933%34 (69%)28 (57%)26 (53%)40 (82%)4 (8%)11 (22%)
INTCC6141%48 (79%)36 (59%)35 (57%)57 (93%)9 (15%)16 (26%)
Cytology diagnosis*
Positive216%16 (76%)12 (57%)16 (76%)20 (95%)N/AN/A
Atypical10530%21 (20%)21 (30%)12 (11%)30 (29%)N/AN/A
Negative22164%4 (2%)9 (4%)1 (0.4%)12 (5%)N/AN/A
Table 1b. Demographic, clinical and genetic features of the Surveillance cohort.
Males without recurrence5930%3 (5%)8 (14%)3 (5%)10 (17%)0 (0%)8 (14%)
Males with recurrence9045%45 (50%)53 (59%)20 (22%)59 (66%)20 (22%)53 (59%)
Females without recurrence179%5 (29%)3 (18%)0 (0%)6 (35%)0 (0%)6 (35%)
Females with recurrence3317%15 (45%)19 (58%)11 (33%)33 (100%)6 (18%)19 (58%)
Original Tumor Diagnosis
PUNLMP124%5 (42%)2 (17%)1 (8%)6 (50%)0 (0%)2 (17%)
CIS258%11 (44%)13 (52%)6 (24%)14 (56%)5 (20%)10 (40%)
LGTCC10735%27 (25%)34 (32%)8 (7%)41 (38%)0 (0%)59 (55%)
HGTCC6220%22 (36%)24 (39%)10 (16%)30 (49%)4 (7%)16 (26%)
INTCC10434%39 (38%)47 (45%)29 (28%)54 (52%)20 (19%)34 (33%)
Original Tumor Stage
pTis258%11 (44%)13 (52%)6 (24%)14 (56%)5 (20%)10 (40%)
pTa18158%54 (30%)60 (33%)19 (19%)77 (43%)4 (2%)77 (43%)
pT17123%28 (39%)35 (49%)22 (31%)39 (55%)14 (20%)23 (32%)
pT2237%9 (9%)9 (39%)7 (30%)12 (52%)5 (22%)10 (43%)
pT393%1 (11%)2 (22%)02 (22%)1 (11%)1 (11%)
pT410.3%1 (100%)1 (100%)01 (100%)N/AN/A
Routine cytology diagnosis*
Positive3015%21 (21%)25 (83%)20 (67%)27 (90%)N/AN/A
Atypical9548%38 (40%)43 (45%)18 (19%)50 (53%)N/AN/A
Negative7136%12 (17%)13 (18%)3 (4%)19 (27%)N/AN/A
  1. *Cytology was available on only a subset of cases.

    N/A Not Available.

Table 2
Demographic, clinical and genetic features of the UTUC cohort stratified by UroSEEK results.
https://doi.org/10.7554/eLife.32143.008
N%Ten-gene multiplex positiveTERT positiveAneuploidy positiveUroSEEK positive
All subjects56100%64%29%39%75%
Gender
 Males2443%71%33%54%83%
 Females3257%59%25%28%69%
CKD stage
 0–22545%68%36%44%76%
 3A1425%50%21%43%71%
 3B1018%80%20%40%80%
 447%25%50%0%50%
 535%100%0%33%100%
Tumor grade
 Low611%67%50%17%67%
 High5089%64%26%42%76%
Tumor stage
 Ta1120%73%55%45%82%
 T1814%50%0%38%75%
 T21018%80%20%10%80%
 T32443%67%33%54%79%
 T435%0%0%0%0%
Upper urinary tract tumor site
 Lower ureter1730%76%18%35%76%
 Upper ureter12%100%0%0%100%
 Ureterovesical junction24%0%0%0%0%
 Lower ureter and upper ureter24%100%50%50%100%
 Renal pelvis2138%57%38%38%76%
 Renal pelvis and lower ureter47%75%25%50%100%
 Renal pelvis and upper ureter59%40%40%60%60%
 Renal pelvis, lower ureter, upper ureter47%75%25%50%75%
Synchronous bladder cancer
 Present2138%52%29%33%62%
 Absent3563%71%29%43%83%
UTUC risk factors
 Aristolactam-DNA adducts present5496%65%30%39%74%
 Smoking history1018%70%30%60%70%
 CKD, chronic kidney disease.

Additional files

Supplementary file 1

Development of PCR-based assays to identify tumor-specific mutations in urinary cells.

https://doi.org/10.7554/eLife.32143.010
Supplementary file 2

Demographic and clinical features of the BC early detection cohort.

https://doi.org/10.7554/eLife.32143.011
Supplementary file 3

Mutations detected by Multiplex Assay in primary tumor tissues from the early detection cohort.

https://doi.org/10.7554/eLife.32143.012
Supplementary file 4

Primers used to detect mutations in the 10-gene multiplex panel and TERT.

https://doi.org/10.7554/eLife.32143.013
Supplementary file 5

Mutations detected by the 10-gene Multiplex Assay in urine samples from the early detection cohort.

https://doi.org/10.7554/eLife.32143.014
Supplementray file 6

Mutations detected by the TERT Assay in urine samples from the early detection cohort.

https://doi.org/10.7554/eLife.32143.015
Supplementary file 7

Chromosome arm gains and losses detected by the Aneuploidy Assay in urine samples from early detection cohort.

https://doi.org/10.7554/eLife.32143.016
Supplementary file 8

Mutations detected by TERT Assay in primary tumor tissues from the early detection cohort.

https://doi.org/10.7554/eLife.32143.017
Supplementary file 9

Individual clinical data for the UTUC cohort.

https://doi.org/10.7554/eLife.32143.018
Supplementary file 10

Mutations detected in urinary cell DNA from UTUC patients by the 10-gene multiplex assay.

https://doi.org/10.7554/eLife.32143.019
Supplementary file 11

TERT promoter mutations identified in urinary cell DNA from UTUC patients.

https://doi.org/10.7554/eLife.32143.020
Supplementary file 12

Urinary cell DNA samples from UTUC patients that scored positive for aneuploidy.

https://doi.org/10.7554/eLife.32143.021
Supplementary file 13

Comparison of copy number variations in matched tumor and urinary cell DNA samples from the UTUC cohort.

https://doi.org/10.7554/eLife.32143.022
Supplementary file 14

Mutations detected in primary tumor DNA from UTUC patients by the 10-gene multiplex assay.

https://doi.org/10.7554/eLife.32143.023
Supplementary file 15

TERT promoter mutations identified in primary tumor DNA from UTUC patients.

https://doi.org/10.7554/eLife.32143.024
Supplementary file 16

Demographic and clinical features of the Surveillance Cohort.

https://doi.org/10.7554/eLife.32143.025
Supplementary file 17

Mutations detected by the 10-gene Multiplex Assay on urine samples from the BC Surveillance cohort.

https://doi.org/10.7554/eLife.32143.026
Supplementary file 18

Mutations detected by the TERT Assay on urine samples from the BC Surveillance cohort.

https://doi.org/10.7554/eLife.32143.027
Supplementary file 19

Chromosome arm gains and losses detected by the Aneuploidy Assay in urine samples from the BC Surveillance cohort.

https://doi.org/10.7554/eLife.32143.028
Supplementary file 20

Summary of the performance of Cytology vs.UroSEEK in both BC cohort tumors.

https://doi.org/10.7554/eLife.32143.029
Supplementary file 21

Primers used for identity matching of tumor and urinary cell DNA samples from UTUC patients.

https://doi.org/10.7554/eLife.32143.030
Transparent reporting form
https://doi.org/10.7554/eLife.32143.031

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  1. Simeon U Springer
  2. Chung-Hsin Chen
  3. Maria Del Carmen Rodriguez Pena
  4. Lu Li
  5. Christopher Douville
  6. Yuxuan Wang
  7. Joshua David Cohen
  8. Diana Taheri
  9. Natalie Silliman
  10. Joy Schaefer
  11. Janine Ptak
  12. Lisa Dobbyn
  13. Maria Papoli
  14. Isaac Kinde
  15. Bahman Afsari
  16. Aline C Tregnago
  17. Stephania M Bezerra
  18. Christopher VandenBussche
  19. Kazutoshi Fujita
  20. Dilek Ertoy
  21. Isabela W Cunha
  22. Lijia Yu
  23. Trinity J Bivalacqua
  24. Arthur P Grollman
  25. Luis A Diaz
  26. Rachel Karchin
  27. Ludmila Danilova
  28. Chao-Yuan Huang
  29. Chia-Tung Shun
  30. Robert J Turesky
  31. Byeong Hwa Yun
  32. Thomas A Rosenquist
  33. Yeong-Shiau Pu
  34. Ralph H Hruban
  35. Cristian Tomasetti
  36. Nickolas Papadopoulos
  37. Ken W Kinzler
  38. Bert Vogelstein
  39. Kathleen G Dickman
  40. George J Netto
(2018)
Non-invasive detection of urothelial cancer through the analysis of driver gene mutations and aneuploidy
eLife 7:e32143.
https://doi.org/10.7554/eLife.32143