Molecular Markers Increase Precision of the European Association of Urology Non–Muscle-Invasive Bladder Cancer Progression Risk Groups

Abstract

The European Association of Urology (EAU) guidelines for non-muscle-invasive bladder cancer (NMIBC) recommend risk stratification based on clinicopathologic parameters. Our aim was to investigate the added value of biomarkers to improve risk stratification of NMIBC. We prospectively included 1,239 patients in follow-up for NMIBC in six European countries. Fresh-frozen tumor samples were analyzed for , and methylation and , and mutation status. Cox regression analyses identified markers that were significantly associated with progression to muscle-invasive disease. The progression incidence rate (PIR = rate of progression per 100 patient-years) was calculated for subgroups. In our cohort, 276 patients had a low, 273 an intermediate, and 555 a high risk of tumor progression based on the EAU NMIBC guideline. Fifty-seven patients (4.6%) progressed to muscle-invasive disease. The limited number of progressors in this large cohort compared with older studies is likely due to improved treatment in the past two decades. Overall, wild-type and methylation of and were significantly associated with progression (HR = 0.34, 2.53, and 2.64, respectively). The PIR for EAU high-risk patients was 4.25. On the basis of mutation status and methylation of , this cohort could be reclassified into a good class (PIR = 0.86, 26.2% of patients), a moderate class (PIR = 4.32, 49.7%), and a poor class (PIR = 7.66, 24.0%). We conclude that the addition of selected biomarkers to the EAU risk stratification increases its accuracy and identifies a subset of NMIBC patients with a very high risk of progression. .

Traditional Classification and Novel Subtyping Systems for Bladder Cancer

“…Therefore, there may be some missing data, resulting in publication bias. Representative studies include UROMOL 2016 subtyping (33) and Van Kessel 2018 high-risk NMIBC subtyping (34). Thus, far, these two studies have the largest number of experimental cases in NMIBC molecular subtyping studies.…”

“…Van Kessel et al (34) found that the methylation status of GATA2, TBX2, TBX3, and ZIC4 and mutations in FGFR3, TERT, PIK3CA, and RAS are correlated with the progression of NMIBC (41,42). Then, the research team detected and analyzed frozen tumor tissues of 1,239 patients with NMIBC from 6 European countries (276 patients were classified as low-risk, 273 as medium-risk and 555 as high-risk according to the EAU guidelines); ultimately, the high-risk NMIBCs were divided into three subtypes.…”

“…The methylation state of GATA2 may be related to the progression of NMIBC. Van Kessel analyzed the GATA2 methylation status and FGFR3 mutation status of high-risk NMIBC and found that NMIBC with GATA2 methylation and FGFR3 wild type was more likely to progress into MIBC (34). Although it is not clear which genes drive the transformation of NMIBC into MIBC, molecular typing provides important clues.…”

Molecular changes during progression from nonmuscle invasive to advanced urothelial carcinoma

“…High‐risk NMIBC patients are treated with intravesical Bacillus Calmette‐Guerin (BCG) or even radical cystectomy (RC). Predictors of increased risk include pathological stage T1, high pathological grade, molecular risk scores and an aggressive, Genomically Unstable or Basal/Squamous, molecular subtype . Recurrent as well as progressive disease has also been studied longitudinally in clinical and molecular studies .…”

OSCA: a tool for omic-data-based complex trait analysis

“…The rapid proliferation of genetic and omic data in large cohort-based samples in the past decade have greatly advanced our understanding of the genetic architecture of omic profiles and the molecular mechanisms underpinning the genetic variation of human complex traits [1][2][3]. These advances include the identification of a large number of genetic variants associated with gene expression [4,5], DNA methylation [6,7], histone modification [8,9], and protein abundance [10,11]; the discovery of omic measures associated with complex traits [12,13]; the improved accuracy in predicting a trait using omic data [14,15]; and the prioritization of gene targets for complex traits by integrating genetic and omic data in large samples [3,13,[16][17][18]. These advances have also led to the development of software tools, focusing on a range of different aspects of omic data analysis.…”