PD15-05 EXTERNAL VALIDATION OF A DECISION-SUPPORT TOOL FOR BORDERLINE OPERABLE MUSCLE-INVASIVE BLADDER CANCER (MIBC) PATIENTS CHOOSING BETWEEN RADICAL CYSTECTOMY AND CHEMO-RADIOTHERAPY

505 Background: Patients with high-risk muscle-invasive bladder cancer (MIBC) who are borderline medically operable for radical cystectomy (RC) face a difficult decision between RC which has higher short-term treatment-related morbidity/mortality & chemoradiotherapy (CRT) which is better tolerated in the short-term but may have worse long-term cancer control outcomes. There are no existing decision support tools to assist patients & providers in understanding these trade-offs. Herein, we developed a visualization tool to inform patients & providers how the relative risks & benefits of RC & CRT vary over time with respect to overall survival (OS). Methods: We identified cT2-3 N0 M0 urothelial bladder cancer patients ≥65 y/o treated with RC +/- chemo (n = 5981) or definitive-dose CRT after TURBT (n = 793) in the National Cancer Database, 2003-2011. The database was split into a development & validation cohort. Multivariate Cox regression with time-varying hazard ratio was performed to assess pre-treatment factors associated with OS. The inverse probability of treatment weighting method using the propensity score was employed to reduce selection bias. External validation was performed. Visualization tool showing adjusted survival curves based on pre-op patient features was generated with input from patients & a multidisciplinary expert panel. Tool calculates median OS & the “break-even point,” where the short-term OS disadvantage of RC equals the long-term advantage of RC (i.e. the point where the restricted mean survival for RC & CRT are equal). Results: On MVA, significant predictors of OS were age, Charlson Deyo comorbidity index, & cT stage (p < 0.001 for all). Using these results, we iteratively developed a web application that utilizes clinical inputs to generate patient-specific survival curves that display estimated OS differences over time. Median OS, the break-even point, & percent alive at the break-even point are provided. Conclusions: This is the first decision-support tool developed to assist high-risk borderline operable MIBC patients & their providers in understanding the short-term & long-term trade-offs between RC & CRT. Additional testing is underway.

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Development of a genomic-clinical classifier for predicting progression after radical cystectomy in patients with muscle invasive bladder cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.4542 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 4542-4542

Author(s):

Anirban Pradip Mitra ◽

Nicholas Erho ◽

Lucia L.C. Lam ◽

Ismael A. Vergara ◽

Thomas Sierocinski ◽

...

Keyword(s):

Bladder Cancer ◽

Radical Cystectomy ◽

Cancer Progression ◽

External Validation ◽

Multivariable Analysis ◽

Invasive Bladder Cancer ◽

K Nearest Neighbor ◽

Muscle Invasive Bladder Cancer ◽

Validation Set ◽

Muscle Invasive

4542 Background: The mainstay of muscle-invasive bladder cancer treatment is surgical resection with/without multi-agent chemotherapy. Management decisions are based on a small number of clinical and pathologic parameters with poor prognostic and predictive power. There is an urgent need for enhanced biomarkers to guide therapy of this lethal disease. Here we have developed a genomic signature of bladder cancer progression using whole transcriptome profiling technology. Methods: 251 FFPE bladder cancer specimens were obtained from patients undergoing radical cystectomy at the University of Southern California (1998-2004). All patients had pT2-T4a,N0 urothelial carcinoma in the absence of pre-operative chemotherapy. Median follow-up was 5 years. RNA expression levels were measured with 1.4 million feature oligonucleotide microarrays. Patients were divided into a training set (2/3 of cohort) to develop a genomic classifier for risk of progression (defined as any type of bladder cancer recurrence), and a validation set (1/3 of cohort). In parallel, multivariable analysis was used to develop a clinical classifier using typical clinical and pathologic variables. Finally, a genomic-clinical classifier was built combining the genomic classifier with clinical variables using logistic regression. The receiver-operator characteristic (ROC) area under the curve (AUC) metric was used to evaluate each classifier in the validation set. Results: The genomic classifier consisted of 89 features corresponding to 80 genes that were combined in a k-nearest neighbor model (KNN89). KNN89 showed an AUC of 0.77 in ROC analysis on the validation set. The best clinical classifier showed an AUC of 0.72. The genomic-clinical classifier demonstrated an AUC of 0.81. Multivariable analysis incorporating all clinical parameters and KNN89 further revealed that KNN89 was the only significant predictor of bladder cancer progression (p=0.0077). Conclusions: We have developed a combined genomic-clinical classifier that shows improved performance over clinical models alone for prediction of progression after radical cystectomy. External validation of this classifier is ongoing.

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