Unknown,Transcriptomics,Genomics,Proteomics

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Discovery and validation of a novel expression signature for recurrence in high-risk bladder cancer post-cystectomy


ABSTRACT: Purpose: Selecting muscle-invasive bladder cancer patients for adjuvant therapy is currently based on clinical variables with limited power. We hypothesized that genomic-based signatures can outperform clinical models to identify patients at higher risk. Method: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. A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. A cohort comprised of 225 patients with organ-confined, muscle-invasive (pT2N0M0),extravesical (pT3-4aN0M0), and node-positive (pTanyN1-3M0) UCB who underwent radicalcystectomy at the University of Southern California between 1998 and 2004 was used. Each patient had aminimum two-year follow-up post-cystectomy unless they recurred prior to that date. Patients receiving neoadjuvant chemotherapy, and those with clinical evidence of lymphadenopathy or distant metastasis at diagnosis were excluded.

ORGANISM(S): Homo sapiens

SUBMITTER: Mohammed Alshalalfa 

PROVIDER: E-GEOD-57933 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

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

Mitra Anirban P AP   Lam Lucia L LL   Ghadessi Mercedeh M   Erho Nicholas N   Vergara Ismael A IA   Alshalalfa Mohammed M   Buerki Christine C   Haddad Zaid Z   Sierocinski Thomas T   Triche Timothy J TJ   Skinner Eila C EC   Davicioni Elai E   Daneshmand Siamak S   Black Peter C PC  

Journal of the National Cancer Institute 20141024 11


<h4>Background</h4>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.<h4>Methods</h4>Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival  ...[more]

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