Generation of a Concise Gene Panel for Outcome Prediction in Urinary Bladder Cancer

Abstract

Purpose This study sought to determine if alterations in molecular pathways could supplement TNM staging to more accurately predict clinical outcome in patients with urothelial carcinoma (UC). Patients and Methods Expressions of 69 genes involved in known cancer pathways were quantified on bladder specimens from 58 patients with UC (stages Ta-T4) and five normal urothelium controls. All tumor transcript values beyond two standard deviations from the normal mean expression were designated as over- or underexpressed. Univariate and multivariable analyses were conducted to obtain a predictive expression signature. A published external data set was used to confirm the potential of the prognostic gene panels. Results In univariate analysis, six genes were significantly associated with time to recurrence, and 10 with overall survival. Recursive partitioning identified three genes as significant determinants for recurrence, and three for overall survival. Of all genes identified by either univariate or partitioning analysis, four were found to significantly predict both recurrence and survival (JUN, MAP2K6, STAT3, and ICAM1); overexpression was associated with worse outcome. Comparing the favorable (low or normal) expression of ≥ three of four versus ≤ two of four of these oncogenes showed 5-year recurrence probability of 41% versus 88%, respectively (P < .001), and 5-year overall survival probability of 61% versus 5%, respectively (P < .001). The prognostic potential of this four-gene panel was confirmed in a large independent external cohort (disease-specific survival, P = .039). Conclusion We have documented the generation of a concise, biologically relevant four-gene panel that significantly predicts recurrence and survival and may also identify potential therapeutic targets for UC.

Three differentiation states risk-stratify bladder cancer into distinct subtypes

“…Identification of new molecular markers would allow for improved risk stratification so that we may better use risk-adapted therapies. Recent molecular profiling of unfractionated BCs has identified unique prognostic gene signatures (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). However, these gene signatures have not been clinically used and their biological relevance has remained to be elucidated.…”

Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

“…A total of 28 bladder cancer gene signatures from 14 studies (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), as well as 11 nonbladder cancer-derived gene signatures devised for various purposes (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) were extracted from the literature (Supplementary Table S1). GeneSymbols in the signatures were updated using the official HGNC GeneSymbols downloaded from the HGNC Web site (33).…”

“…The signatures have been derived by various means including clustering methods (e.g., hierarchical clustering analysis) or inference (e.g., t test) and, in some cases, by more advanced classification algorithms that both derive the gene signatures and evaluate their performances (7-9, 11, 13, 17). Validation of the suggested gene signatures in independent external data sets is, however, scarce (8,9,17,21), which may lead to an overestimation of gene signature performance and robustness.…”

“…A large number of molecular markers have been described in the literature (6), but few have entered into clinical decision making. In recent years, several investigations have used genome-wide gene expression analyses to define specific gene expression signatures associated with both urothelial cancer biology and the clinical course of the disease (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Many of these have the potential to be of great clinical value.…”

560 Combination of Molecular Alterations and Smoking Intensity Predicts Bladder Cancer Outcome: A Report From the Los Angeles Cancer Surveillance Program

“…More recently, we and others have used multimarker approaches to profile alterations across several pathways. [3][4][5][6] However, most of these studies do not account for common UCB risk factors and their influence on outcome. Cigarette smoking is an established risk factor for UCB in the U.S. due to carcinogenic effects of aromatic amines in tobacco smoke.…”