Novel Genomic Signature Predicts Postcystectomy Recurrence in High-Risk Bladder Cancer
As reported in the Journal of the National Cancer Institute, Mitra et al have identified a novel genomic-based signature that improves prediction of postcystectomy recurrence in patients with high-risk bladder cancer. Use of the signature could help guide selection of patients for adjuvant therapy in this setting.
Study Details
The study involved generation of transcriptome-wide expression profiles using 1.4 million feature-arrays on archival tumors from 225 patients with muscle-invasive or node-positive bladder cancer who had undergone radical cystectomy. Genomic classifiers and clinicopathologic classifiers for predicting recurrence were developed in a discovery set (n = 133). The predictive performances of genomic classifiers, clinicopathologic classifiers (individual features and an optimized prognostic model developed in the discovery set including age, gender, pathological stage, and lymphovascular invasion status), the independent clinical nomogram of the International Bladder Cancer Nomogram Consortium (IBCNC), and combined genomic-clinicopathologic classifiers were assessed in the discovery and independent validation (n = 66) sets.
In the discovery set, patients had a median age of 68.5 years (interquartile range = 63.6–75.6 years), median follow-up was 9.3 years, 51% of patients had recurrence, and 58% of patients had died at last follow-up. In the validation set, median age was 69.8 years (interquartile range = 63.1–74.3 years), median follow-up was 10.8 years, 50% of patients had recurrence, and 68% of patients had died at last follow-up
Genomic Markers
In the discovery set, 15 genomic markers were identified corresponding to RNAs from genome regions that were differentially expressed in recurrence; associated genes included those involved in cell proliferation/cell-cycle regulation/apoptosis, cell differentiation, transcription regulation, and signaling pathways and transduction (FOX06, HSD17B7, ARID4B, ENAH, MAP4K3, MARCH7, MECOM, LRBA, MUT, CRCP, SYPL1, ARFGEF1, EHF, METTL7A, and PPP1R12A).
Findings in Discovery Set
In the discovery set, area under the standard receiver-operating characteristic (ROC) curve values (AUCs) were 0.88 for the genomic classifiers compared with 0.70 for nodal status, 0.69 for lymphovascular invasion, 0.58 for tumor stage, 0.53 for age, 0.47 for gender, 0.73 for IBCNC, 0.89 for genomic-IBCNC, 0.81 for the clinicopathologic model, and 0.93 for genomic-clinical classifiers.
Predictive Ability in Validation Set
In the validation set, the genomic signature had a survival ROC AUC of 0.75, higher than the values for the individual clinical parameters of nodal status (0.71), lymphovascular invasion (0.68), tumor stage (0.57), age (0.50), and gender (0.44) and similar to those for IBCNC (0.73) and the clinicopathologic model (0.78); survival ROC AUC was increased with addition of the genomic signature to both IBCNC (0.82) and the clinicopathologic model (0.86).
On multivariate analysis adjusting for demographic, clinicopathologic and treatment factors, the genomic signature was the best predictor of recurrence (hazard ratio [HR] = 1.42, P = .005); other significant predictors were nodal status (P = .017), lymphovascular invasion (P = .045), and interaction of the genomic signature and nodal status (P = .03), whereas age, sex, race, tumor stage, and receipt of adjuvant therapy were not independent predictors. Multivariate analysis of the predictive models showed independent predictive effect when the genomic signature was added to IBCNC (HR = 1.18, P = .016) compared with IBCNC alone (HR = 1.04, P = .62) and when the genomic signature was added to the clinicopathologic model (HR = 1.18, P = .008) compared with the clinicopathologic model alone (HR = 1.10, P = .30).
Reclassification and Decision Curve Analyses
An analysis in the validation set categorizing patients as high-risk or low-risk based on genomic-clinicopathologic scoring and adjusting for competing risks showed 4-year probability of recurrence of 81.5% vs 20.6% (P < .001). Further, addition of the genomic signature to the IBCNC nomogram reclassified 18 patients into different risk categories, with 12 (67%) correctly reclassified based on outcome. The genomic-clinicopathologic model reclassified 12 patients from initial IBCNC risk categories, with 10 (83%) correctly reclassified.
Decision curve analysis for decision to treat based on specificity and sensitivity of predictive models at different thresholds of probability for postcystectomy recurrence showed a greater overall benefit for genomic-based classifiers. Thus, for example, for a 50% threshold probability of postcystectomy recurrence, compared with ‘treat none’ and ‘treat all’ options, the genomic-IBCNC model resulted in a reduction in unnecessary treatment (ie, in low risk patients) by 27% compared with 17% for IBCNC alone, and the genomic-clinicopathologic model resulted in reduction in overtreatment in 28% compared with 20% for the clinicopathologic model alone.
External Validation
In the validation cohort, the genomic signature showed better survival predictive ability (survival ROC AUC = 0.77) than seven other previously reported genomic signatures for muscle-invasive or node-positive disease (AUCs = 0.72-0.51).
Finally, the genomic signature was validated in four additional external bladder urothelial carcinoma datasets (n=341). For overall survival (three data sets), HRs ranged from 2.26 to 7.57, with P values of .016 to < .001, and concordance indices of 0.65 to 0.80. For cancer-specific survival (one dataset), the HR was 2.23, P = .001, and the concordance index was 0.65. For recurrence-free survival (one dataset), the HR was 3.33, P < .001, and the concordance index was 0.67.
The investigators concluded: “The validated genomic-based classifiers outperform clinical models for predicting postcystectomy bladder cancer recurrence. This may be used to better identify patients who need more aggressive management.”
Anirban P. Mitra, MD, PhD, of University of Southern California, is the corresponding author for the Journal of the National Cancer Institute article. Siamak Daneshmand, MD, University of Southern California, and Peter C. Black, MD, University of British Columbia, contributed equally to the work.
The work was supported by Genome British Columbia. For full disclosures of the study authors, visit jnci.oxfordjournals.org.
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
