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Clinical Parameters Outperform Molecular Subtypes for Predicting Outcome in Bladder Cancer: Results from Multiple Cohorts, Including TCGA.

Authors
  • Morera, Daley S1
  • Hasanali, Sarrah L1
  • Belew, Daniel2
  • Ghosh, Santu3
  • Klaassen, Zachary2
  • Jordan, Andre R4
  • Wang, Jiaojiao1
  • Terris, Martha K2
  • Bollag, Roni J5
  • Merseburger, Axel S6
  • Stenzl, Arnulf7
  • Soloway, Mark S8
  • Lokeshwar, Vinata B1
  • 1 Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, Augusta, Georgia. , (Georgia)
  • 2 Division of Urology, Department of Surgery, Medical College of Georgia, Augusta University, Augusta, Georgia. , (Georgia)
  • 3 Biostatistics and Data Science, Medical College of Georgia, Augusta University, Augusta, Georgia. , (Georgia)
  • 4 Sheila and David Fuente Graduate Program in Cancer Biology, Sylvester Comprehensive Cancer Center, University of Miami-Miller School of Medicine, Miami, Florida.
  • 5 Departments of Pathology, Bio-Repository Alliance of Georgia for Oncology at Georgia Cancer Center, Medical College of Georgia, Augusta University, Augusta, Georgia. , (Georgia)
  • 6 University-Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany. , (Germany)
  • 7 Departments of Urology, Eberhard Karls University Tübingen, Tübingen, Germany. , (Germany)
  • 8 Memorial Healthcare System, Aventura, Florida.
Type
Published Article
Journal
The Journal of urology
Publication Date
Jan 01, 2020
Volume
203
Issue
1
Pages
62–72
Identifiers
DOI: 10.1097/JU.0000000000000351
PMID: 31112107
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Studies indicate that molecular subtypes in muscle invasive bladder cancer predict the clinical outcome. We evaluated whether subtyping by a simplified method and established classifications could predict the clinical outcome. We subtyped institutional cohort 1 of 52 patients, including 39 with muscle invasive bladder cancer, an Oncomine™ data set of 151 with muscle invasive bladder cancer and TCGA (The Cancer Genome Atlas) data set of 402 with muscle invasive bladder cancer. Subtyping was done using simplified panels (MCG-1 and MCG-Ext) which included only transcripts common in published studies and were analyzed for predicting metastasis, and cancer specific, overall and recurrence-free survival. TCGA data set was further analyzed using the Lund taxonomy, the Bladder Cancer Molecular Taxonomy Group Consensus and TCGA 2017 mRNA subtype classifications. Muscle invasive bladder cancer specimens from cohort 1 and the Oncomine data set showed intratumor heterogeneity for transcript and protein expression. MCG-1 subtypes did not predict the outcome on univariate or Kaplan-Meier analysis. On multivariate analysis N stage (p ≤0.007), T stage (p ≤0.04), M stage (p=0.007) and/or patient age (p=0.01) predicted metastasis, cancer specific and overall survival, and/or the cisplatin based adjuvant chemotherapy response. In TCGA data set publications showed that subtypes risk stratified patients for overall survival. Consistently the MCG-1 and MCG-Ext subtypes were associated with overall but not recurrence-free survival on univariate and Kaplan-Meier analyses. TCGA data set included 21 low grade specimens of the total of 402 and subtypes associated with tumor grade (p=0.005). However, less than 1% of muscle invasive bladder cancer cases are low grade. In only high grade specimens the MCG-1 and MCG-Ext subtypes could not predict overall survival. On univariate analysis subtypes according to the Bladder Cancer Molecular Taxonomy Group Consensus, TCGA 2017 and the Lund taxonomy were associated with tumor grade (p <0.0001) and overall survival (p=0.01 to <0.0001). Regardless of classification, subtypes had about 50% to 60% sensitivity and specificity to predict overall and recurrence-free survival. On multivariate analyses N stage and lymphovascular invasion consistently predicted recurrence-free and overall survival (p=0.039 and 0.003, respectively). Molecular subtypes reflect bladder tumor heterogeneity and are associated with tumor grade. In multiple cohorts and subtyping classifications the clinical parameters outperformed subtypes for predicting the outcome.

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