Collaborative study from the Bladder Cancer Advocacy Network for the genomic analysis of metastatic urothelial cancer

Jeffrey S Damrauer; Wolfgang Beckabir; Jeff Klomp; Mi Zhou; Elizabeth R Plimack; Matthew D Galsky; Petros Grivas; Noah M Hahn; Peter H O'Donnell; Gopa Iyer; David I Quinn; Benjamin G Vincent; Diane Zipursky Quale; Sara E Wobker; Katherine A Hoadley; William Y Kim; Matthew I Milowsky

doi:10.1038/s41467-022-33980-9

Collaborative study from the Bladder Cancer Advocacy Network for the genomic analysis of metastatic urothelial cancer

Nat Commun. 2022 Nov 4;13(1):6658. doi: 10.1038/s41467-022-33980-9.

Authors

Jeffrey S Damrauer^#¹, Wolfgang Beckabir^#^{1

2}, Jeff Klomp^{1

3}, Mi Zhou¹, Elizabeth R Plimack⁴, Matthew D Galsky⁵, Petros Grivas^{6

7}, Noah M Hahn⁸, Peter H O'Donnell⁹, Gopa Iyer¹⁰, David I Quinn¹¹, Benjamin G Vincent^{1

2

12

13

14}, Diane Zipursky Quale¹⁵, Sara E Wobker^{1

16}, Katherine A Hoadley^{1

17}, William Y Kim^{18

19

20

21

22}, Matthew I Milowsky^{23

24

25}

Affiliations

¹ Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
² Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA.
³ Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA.
⁴ Department of Hematology and Oncology, Fox Chase Cancer Center, Temple Health, Philadelphia, PA, USA.
⁵ Division of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁶ Department of Medicine, Division of Medical Oncology, University of Washington, Seattle, USA.
⁷ Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, USA.
⁸ Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁹ Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA.
¹⁰ Memorial Sloan Kettering Cancer Center, New York, NY, USA.
¹¹ University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
¹² Division of Hematology, University of North Carolina, Chapel Hill, NC, USA.
¹³ Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.
¹⁴ Curriculum in Bioinformatics and Computational Biology, Computational Medicine Program, University of North Carolina, Chapel Hill, USA.
¹⁵ Bladder Cancer Advocacy Network, Bethesda, MD, USA.
¹⁶ Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA.
¹⁷ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
¹⁸ Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA. wykim@med.unc.edu.
¹⁹ Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA. wykim@med.unc.edu.
²⁰ Department of Medicine, University of North Carolina, Chapel Hill, NC, USA. wykim@med.unc.edu.
²¹ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA. wykim@med.unc.edu.
²² Division of Oncology, University of North Carolina, Chapel Hill, NC, USA. wykim@med.unc.edu.
²³ Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA. matt_milowsky@med.unc.edu.
²⁴ Department of Medicine, University of North Carolina, Chapel Hill, NC, USA. matt_milowsky@med.unc.edu.
²⁵ Division of Oncology, University of North Carolina, Chapel Hill, NC, USA. matt_milowsky@med.unc.edu.

^# Contributed equally.

Abstract

Urothelial Cancer - Genomic Analysis to Improve Patient Outcomes and Research (NCT02643043), UC-GENOME, is a genomic analysis and biospecimen repository study in 218 patients with metastatic urothelial carcinoma. Here we report on the primary outcome of the UC-GENOME-the proportion of subjects who received next generation sequencing (NGS) with treatment options-and present the initial genomic analyses and clinical correlates. 69.3% of subjects had potential treatment options, however only 5.0% received therapy based on NGS. We found an increased frequency of TP53^E285K mutations as compared to non-metastatic cohorts and identified features associated with benefit to chemotherapy and immune checkpoint inhibition, including: Ba/Sq and Stroma-rich subtypes, APOBEC mutational signature (SBS13), and inflamed tumor immune phenotype. Finally, we derive a computational model incorporating both genomic and clinical features predictive of immune checkpoint inhibitor response. Future work will utilize the biospecimens alongside these foundational analyses toward a better understanding of urothelial carcinoma biology.

Publication types

Clinical Study
Research Support, N.I.H., Extramural

MeSH terms

Carcinoma, Transitional Cell* / genetics
Genomics
High-Throughput Nucleotide Sequencing
Humans
Urinary Bladder / pathology
Urinary Bladder Neoplasms* / pathology

Abstract

Publication types

MeSH terms

Grants and funding