Discovery and validation of novel expression signature for postcystectomy recurrence in high-risk bladder cancer - PubMed (original) (raw)

. 2014 Oct 24;106(11):dju290.

doi: 10.1093/jnci/dju290. Print 2014 Nov.

Lucia L Lam 2, Mercedeh Ghadessi 2, Nicholas Erho 2, Ismael A Vergara 2, Mohammed Alshalalfa 2, Christine Buerki 2, Zaid Haddad 2, Thomas Sierocinski 2, Timothy J Triche 2, Eila C Skinner 2, Elai Davicioni 2, Siamak Daneshmand 2, Peter C Black 2

Affiliations

Discovery and validation of novel expression signature for postcystectomy recurrence in high-risk bladder cancer

Anirban P Mitra et al. J Natl Cancer Inst. 2014.

Abstract

Background: Nearly half of muscle-invasive bladder cancer patients succumb to their disease following cystectomy. Selecting candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. This study aimed to develop and validate genomic-based signatures that can better identify patients at risk for recurrence than clinical models alone.

Methods: Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival tumors from 225 patients who underwent radical cystectomy and had muscle-invasive and/or node-positive bladder cancer. Genomic (GC) and clinical (CC) classifiers for predicting recurrence were developed on a discovery set (n = 133). Performances of GC, CC, an independent clinical nomogram (IBCNC), and genomic-clinicopathologic classifiers (G-CC, G-IBCNC) were assessed in the discovery and independent validation (n = 66) sets. GC was further validated on four external datasets (n = 341). Discrimination and prognostic abilities of classifiers were compared using area under receiver-operating characteristic curves (AUCs). All statistical tests were two-sided.

Results: A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. This was higher than individual clinical variables, IBCNC (AUC = 0.73), and comparable to CC (AUC = 0.78). Performance was improved upon combining GC with clinical nomograms (G-IBCNC, AUC = 0.82; G-CC, AUC = 0.86). G-CC high-risk patients had elevated recurrence probabilities (P < .001), with GC being the best predictor by multivariable analysis (P = .005). Genomic-clinicopathologic classifiers outperformed clinical nomograms by decision curve and reclassification analyses. GC performed the best in validation compared with seven prior signatures. GC markers remained prognostic across four independent datasets.

Conclusions: 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.

© The Author 2014. Published by Oxford University Press.

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Figures

Figure 1.

Figure 1.

Performance of classifiers and individual clinicopathologic variables as assessed by standard-ROC analysis in the discovery and validation sets for predicting postcystectomy recurrence. GC had the highest AUC compared to single clinicopathologic variables. Its AUC increased when combined with IBCNC and CC. Circles and whiskers represent AUC and associated 95% confidence intervals, respectively. AUCs for variables are also listed under the respective sets. ROC = receiver-operating characteristic; AUC = area under ROC curve; GC = genomic classifier; IBCNC = postcystectomy recurrence nomogram from the International Bladder Cancer Nomogram Consortium; G-IBCNC = integrated genomic-IBCNC classifier; CC = “clinical-only” classifier; G-CC = integrated genomic-CC classifier.

Figure 2.

Figure 2.

Performance of individual clinicopathologic variables and classifiers in the validation set for predicting cancer recurrence. A) Survival ROC curves show that GC outperforms individual clinicopathologic variables for predicting postcystectomy recurrence. In addition, B) G-IBCNC had higher AUC compared to IBCNC, and C) G-CC had higher AUC compared to CC by survival-ROC analysis. AUCs and associated 95% confidence intervals are shown at the bottom right of each ROC curve panel. D) Cumulative incidence plot for recurrence-free survival comparing patients with high versus low G-CC scores as determined by majority rule (cutoff = 0.5) indicate a statistically significantly elevated recurrence probability for patients with high G-CC scores (log-rank P < .001). Death from non-bladder-cancer causes was considered a competing risk. Probabilities of disease recurrence at 2 and 4 years postcystectomy are shown. All statistical tests were two-sided. ROC = receiver-operating characteristic; AUC = area under ROC curve; GC = genomic classifier; IBCNC = post-cystectomy recurrence nomogram from the International Bladder Cancer Nomogram Consortium; G-IBCNC = integrated genomic-IBCNC classifier; CC = “clinical-only” classifier; G-CC = integrated genomic-CC classifier.

Figure 3.

Figure 3.

Reclassification of IBCNC score categories by genomic-clinicopathologic classifier scores for patients in the validation set. After categorizing based on their IBCNC scores, patients were reclassified based on their A) G-IBCNC and B) G-CC scores. Individual patients are represented as dots colored by recurrence event; sizes of dots represent pathological stage as indicated. Gray quadrants represent situations where the genomic-clinicopathologic classifier reclassifies patients compared to IBCNC. Patients who did not recur (blue dots) in the top-left quadrant and patients who recurred (red dots) in the bottom-right quadrant are reclassified correctly by the genomic-clinicopathologic classifier. Of patients who were reclassified, a majority were done so correctly. IBCNC = postcystectomy recurrence nomogram from the International Bladder Cancer Nomogram Consortium; G-IBCNC = integrated genomic-IBCNC classifier; G-CC = integrated genomic-clinical classifier.

Figure 4.

Figure 4.

Cumulative incidence plots for recurrence-free survival for node-negative patients in the validation set. Patients were stratified based on their A) CC and B) GC scores into high-risk and low-risk groups as determined by majority rule (cutoff = 0.5). Risk stratification based on GC scores was statistically significant (P = .004) while that based on CC scores showed a trend towards statistical significance (P = .051). Death from non-bladder-cancer causes was considered a competing risk. Probabilities of disease recurrence at 2 and 4 years postcystectomy are shown. P values were determined by the log-rank test and are two-sided. CC = “clinical-only” classifier; GC = genomic classifier.

Figure 5.

Figure 5.

Kaplan-Meier survival plots showing independent external validation of GC performance. Bladder cancer patients from four external datasets including A) TCGA bladder urothelial carcinoma database (P = .016), B) NCBI–GEO GSE13507 (P < .001), C) NCBI–GEO GSE5287 (P < .001), and D) NCBI–GEO GSE31684 (P = .013) were stratified into low-risk (green) and high-risk (red) groups as determined by median split rule. Stratification was based on the prognostic index, which was calculated using all available markers related to the 15-feature GC. P values were determined by the log-rank test and are two-sided. GC = genomic classifier.

Comment in

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