BACKGROUND: Neoadjuvant chemotherapy before cystectomy confers a survival benefit in bladder cancer, but it has not been widely adopted since most patients do not benefit and we are at present unable to predict those that do. Since the most important predictor of recurrence after cystectomy is pathologically positive nodes, our aim was to assess techniques that define this stage for the selection of patients for neoadjuvant chemotherapy. METHODS: We developed a gene expression model (GEM) to predict the pathological node status in primary tumour tissue from three independent cohorts of patients who were clinically node negative. From a subset of transcripts detected faithfully by microarrays from both paired frozen and formalin-fixed tissues (32 pairs), we developed both the GEM and cutoffs that identified patient strata with raised risk of nodal involvement by use of two separate training cohorts (90 and 66 patients). We then assessed the GEM and cutoffs to predict node-positive disease in tissues from a phase 3 trial cohort (AUO-AB-05/95; 185 patients). FINDINGS: We developed a 20-gene GEM with an area under the curve of 0·67 (95% CI 0·60-0·75) for prediction of nodal disease at cystectomy in AUO-AB-05/95. The cutoff system identified patients with high relative risk (1·74, 95% CI 1·03-2·93) and low relative risk (0·70, 95% CI 0·51-0·96) of node-positive disease. Multivariate logistic regression showed the GEM predictor was independent of age, sex, pathological stage, and lymphovascular space invasion (coefficient 9·81, 95% CI 1·64-18·00; p=0·019). INTERPRETATION: Selecting patients for neoadjuvant chemotherapy on the basis of risk of node-positive disease has the potential to benefit high-risk patients while sparing other patients toxic effects and delay to cystectomy. FUNDING: US National Cancer Institute (R01CA143971).
BACKGROUND: Neoadjuvant chemotherapy before cystectomy confers a survival benefit in bladder cancer, but it has not been widely adopted since most patients do not benefit and we are at present unable to predict those that do. Since the most important predictor of recurrence after cystectomy is pathologically positive nodes, our aim was to assess techniques that define this stage for the selection of patients for neoadjuvant chemotherapy. METHODS: We developed a gene expression model (GEM) to predict the pathological node status in primary tumour tissue from three independent cohorts of patients who were clinically node negative. From a subset of transcripts detected faithfully by microarrays from both paired frozen and formalin-fixed tissues (32 pairs), we developed both the GEM and cutoffs that identified patient strata with raised risk of nodal involvement by use of two separate training cohorts (90 and 66 patients). We then assessed the GEM and cutoffs to predict node-positive disease in tissues from a phase 3 trial cohort (AUO-AB-05/95; 185 patients). FINDINGS: We developed a 20-gene GEM with an area under the curve of 0·67 (95% CI 0·60-0·75) for prediction of nodal disease at cystectomy in AUO-AB-05/95. The cutoff system identified patients with high relative risk (1·74, 95% CI 1·03-2·93) and low relative risk (0·70, 95% CI 0·51-0·96) of node-positive disease. Multivariate logistic regression showed the GEM predictor was independent of age, sex, pathological stage, and lymphovascular space invasion (coefficient 9·81, 95% CI 1·64-18·00; p=0·019). INTERPRETATION: Selecting patients for neoadjuvant chemotherapy on the basis of risk of node-positive disease has the potential to benefit high-risk patients while sparing other patients toxic effects and delay to cystectomy. FUNDING: US National Cancer Institute (R01CA143971).
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Authors: Jeffrey S Damrauer; Katherine A Hoadley; David D Chism; Cheng Fan; Christopher J Tiganelli; Sara E Wobker; Jen Jen Yeh; Matthew I Milowsky; Gopa Iyer; Joel S Parker; William Y Kim Journal: Proc Natl Acad Sci U S A Date: 2014-02-11 Impact factor: 11.205
Authors: Kamran Zargar-Shoshtari; Homayoun Zargar; Colin P Dinney; Cesar E Ercole; Pranav Sharma; Evan Kovac; Petros D Grivas; Andrew J Stephenson; Jay B Shah; Peter C Black; Philippe E Spiess Journal: World J Urol Date: 2015-08-19 Impact factor: 4.226