Genomic Predictors of Good Outcome, Recurrence, or Progression in High-Grade T1 Non–Muscle-Invasive Bladder Cancer

High-grade T1 (HGT1) bladder cancer is the highest risk subtype of non-muscle-invasive bladder cancer with unpredictable outcome and poorly understood risk factors. Here, we examined the association of somatic mutation profiles with nonrecurrent disease (GO, good outcome), recurrence (R), or progres...

Full description

Saved in:
Bibliographic Details
Published inCancer research (Chicago, Ill.) Vol. 80; no. 20; pp. 4476 - 4486
Main Authors Bellmunt, Joaquim, Kim, Jaegil, Reardon, Brendan, Perera-Bel, Júlia, Orsola, Anna, Rodriguez-Vida, Alejo, Wankowicz, Stephanie A., Bowden, Michaela, Barletta, Justine A., Morote, Juan, de Torres, Inés, Juanpere, Nuria, Lloreta-Trull, Josep, Hernandez, Silvia, Mouw, Kent W., Taplin, Mary-Ellen, Cejas, Paloma, Long, Henry W., Van Allen, Eliezer M., Getz, Gad, Kwiatkowski, David J.
Format Journal Article
LanguageEnglish
Published United States 15.10.2020
Subjects
Online AccessGet full text
ISSN0008-5472
1538-7445
1538-7445
DOI10.1158/0008-5472.CAN-20-0977

Cover

More Information
Summary:High-grade T1 (HGT1) bladder cancer is the highest risk subtype of non-muscle-invasive bladder cancer with unpredictable outcome and poorly understood risk factors. Here, we examined the association of somatic mutation profiles with nonrecurrent disease (GO, good outcome), recurrence (R), or progression (PD) in a cohort of HGT1 patients. Exome sequencing was performed on 62 HGT1 and 15 matched normal tissue samples. Both tumor only (TO) and paired analyses were performed, focusing on 95 genes known to be mutated in bladder cancer. Somatic mutations, copy-number alterations, mutation load, and mutation signatures were studied. Thirty-three GO, 10 R, 18 PD, and 1 unknown outcome patients were analyzed. Tumor mutational burden (TMB) was similar to muscle-invasive disease and was highest in GO, intermediate in PD, and lowest in R patients ( = 0.017). DNA damage response gene mutations were associated with higher TMB ( < 0.0001) and GO ( = 0.003). ERCC2 and BRCA2 mutations were associated with GO. TP53, ATM, ARID1A, AHR, and SMARCB1 mutations were more frequent in PD. Focal copy-number gain in CCNE1 and CDKN2A deletion was enriched in PD or R ( = 0.047; = 0.06). APOBEC (46%) and COSMIC5 (34%) signatures were most frequent. APOBEC-A and ERCC2 mutant tumors (COSMIC5) were associated with GO ( = 0.047; = 0.0002). pT1b microstaging was associated with a genomic cluster ( = 0.05) with focal amplifications of E2F3/SOX4, PVRL4, CCNE1, and TP53 mutations. Findings were validated using external public datasets. These findings require confirmation but suggest that management of HGT1 bladder cancer may be improved via molecular characterization to predict outcome. SIGNIFICANCE: Detailed genetic analyses of HGT1 bladder tumors identify features that correlate with outcome, e.g., high mutational burden, ERCC2 mutations, and high APOBEC-A/ERCC2 mutation signatures were associated with good outcome.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
J. Bellmunt and J. Kim contributed equally to this article.
Authors’ Contributions
J. Bellmunt: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. J. Kim: Conceptualization, data curation, software, formal analysis, supervision, validation, investigation, visualization, methodology, writing-original draft. B. Reardon: Data curation, software, formal analysis, validation, investigation, visualization, methodology, writing-review and editing. J. Perera-Bel: Conceptualization, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. A. Orsola: Conceptualization, resources, data curation, formal analysis, methodology, writing- review and editing. A. Rodriguez-Vida: Resources, visualization, writing-review and editing. S.A. Wankowicz: Conceptualization, data curation, software, formal analysis, investigation, visualization, methodology, writing-review and editing. M. Bowden: Conceptualization, methodology, writing-review and editing. J.A. Barletta: Data curation, investigation, methodology, writing-review and editing. J. Morote: Resources, data curation, writing-review and editing. I. de Torres: Resources, data curation, writing-review and editing. N. Juanpere: Resources, data curation, investigation, visualization, methodology, writing-review and editing. J. Lloreta-Trull: Resources, data curation, investigation, writing-review and editing. S. Hernandez: Conceptualization, data curation, formal analysis, writing-review and editing. K.W. Mouw: Investigation, methodology, writing-review and editing. M.-E. Taplin: Resources, funding acquisition, writing-review and editing. P. Cejas: Investigation, visualization, methodology, writing-review and editing. H.W. Long: Investigation, visualization, methodology, writing-review and editing. E.M. Van Allen: Conceptualization, resources, data curation, software, formal analysis, supervision, investigation, visualization, methodology, writing-review and editing. G. Getz: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. D.J. Kwiatkowski: Conceptualization, data curation, formal analysis, supervision, investigation, visualization, methodology, writing-original draft, project administration, writing-review and editing.
ISSN:0008-5472
1538-7445
1538-7445
DOI:10.1158/0008-5472.CAN-20-0977