Postoperative Nomogram Predicting Risk of Recurrence After Radical Cystectomy for Bladder Cancer

Bochner, Bernard H.; Dalbagni, Guido; Kattan, Michael W.; Fearn, Paul; Vora, Kinjal; Hee, Song Seo; Zoref, Lauren; Abol‐Enein, Hassan; Ghoneim, Mohamed A.; Scardino, Peter T.; Bajorin, Dean F.; Skinner, Donald G.; Stein, John P.; Miranda, Gus; Gschwend, Jürgen E.; Volkmer, Bjoern G.; Hautmann, Richard E.; Chang, Sam S.; Cookson, Michael S.; Smith, Joseph A.; Thalman, George; Studer, Urs E.; Lee, Cheryl T.; Montie, James E.; Wood, David P.; Palou, Juan; Fradet, Y.; Lacombe, Louis; Simard, Pierre; Schoenberg, Mark; Lerner, Seth P.; Vazina, Amnon; Bassi, Pier Francesco; Murai, Masaru; Kikuchi, Eiji

doi:10.1200/jco.2005.05.3884

Cited by 411 publications

(123 citation statements)

References 23 publications

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“…The feature associated with MECOM was determined to be the most important variable (Supplementary Figure 3, available online). GC performance was compared with individual clinicopathologic variables, the IBCNC postoperative nomogram (8), and a clinical-only classifier (CC) that represented an optimized clinicopathologic prognostic model developed on the discovery set.…”

Section: Resultsmentioning

confidence: 99%

“…Identification of candidates at highest risk for recurrence who may need adjuvant therapy is currently based on clinical criteria that may not reflect the entire biology of the disease (8). We hypothesized that an unbiased transcriptome-wide profiling approach of primary UCBs from clinically high-risk patients could be employed to accurately stratify the risk of postcystectomy recurrence.…”

Section: Discussionmentioning

confidence: 99%

“…Prognostic ability of GC was benchmarked against two clinical nomograms for predicting postcystectomy recurrence risk: the International Bladder Cancer Nomogram Consortium (IBCNC) (8), and an in-house “clinical-only” classifier (CC). The latter incorporated age, gender, pathological stage, and lymphovascular invasion status into a logistic regression model.…”

Section: Methodsmentioning

confidence: 99%

“…Identification of candidates at high risk for recurrence who may benefit most from adjuvant chemotherapy is currently based on standard clinicopathologic criteria (7). Even when combined as multivariable nomograms, these metrics do not fully account for the diverse clinical behavior of muscle-invasive UCB (8,9). It is now recognized that biomarker panels that reflect the biological heterogeneity of UCB can better identify patients who need aggressive therapy than single molecular markers alone (10,11).…”

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confidence: 99%

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Discovery and Validation of Novel Expression Signature for Postcystectomy Recurrence in High-Risk Bladder Cancer

Mitra¹,

Lam²,

Ghadessi³

et al. 2014

JNCI: Journal of the National Cancer Institute

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BackgroundNearly 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.MethodsTranscriptome-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.ResultsA 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.ConclusionsThe 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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Discovery and Validation of Novel Expression Signature for Postcystectomy Recurrence in High-Risk Bladder Cancer

Mitra¹,

Lam²,

Ghadessi³

et al. 2014

JNCI: Journal of the National Cancer Institute

View full text Add to dashboard Cite

show abstract

“…The results were compared to previously published data sets, and a survival-associated gene expression subtype was identified using hierarchical clustering. Used together, survival-associated gene expression and the clinical nomogram validated by the International Bladder Cancer Nomogram Consortium 123 make predictions of recurrence significantly more accurate.…”

Section: Biomarker Studies In Mibcmentioning

confidence: 99%