Motivation: Genomic DNA regions are frequently lost or gained during tumor progression. Array Comparative Genomic Hybridization (array CGH) technology makes it possible to assess these changes in DNA in cancers, by comparison with a normal reference. The identification of systematically deleted or amplified genomic regions in a set of tumors enables biologists to identify genes involved in cancer progression because tumor suppressor genes are thought to be located in lost genomic regions and oncogenes, in gained regions. Array CGH profiles should also improve the classification of tumors. The achievement of these goals requires a methodology for detecting the breakpoints delimiting altered regions in genomic patterns and assigning a status (normal, gained or lost) to each chromosomal region. Results: We have developed a methodology for the automatic detection of breakpoints from array CGH profile, and the assignment of a status to each chromosomal region. The breakpoint detection step is based on the Adaptive Weights Smoothing (AWS) procedure and provides highly convincing results: our algorithm detects 97, 100 and 94% of breakpoints in simulated data, karyotyping results and manually analyzed profiles, respectively. The percentage of correctly assigned statuses ranges from 98.9 to 99.8% for simulated data and is 100% for karyotyping results. Our algorithm also outperforms other solutions on a public reference dataset. Availability: The R package GLAD (Gain and Loss Analysis of DNA) is available upon request

A subtype of aggressive human muscle-invasive bladder cancer expresses basal epithelial markers and is sensitive to EGFR inhibition in preclinical models.

Purpose: Fibroblast growth factor receptor 3 (FGFR3) mutations were recently found at a high frequency in well-differentiated urothelial cell carcinoma (UCC). We investigated the relationship between FGFR3 status and three molecular markers (MIB-1, P53, and P27^kip1) associated with worse prognosis and determined the reproducibility of pathologic grade and molecular variables. Patients and Methods: In this multicenter study, we included 286 patients with primary (first diagnosis) UCC. The histologic slides were reviewed. FGFR3 status was examined by polymerase chain reaction–single strand conformation polymorphism and sequencing. Expression levels of MIB-1, P53, and P27^kip1 were determined by immunohistochemistry. Mean follow-up was 5.5 years (range, 0.4 to 18.4 years). Results: FGFR3 mutations were detected in 172 (60%) of 286 UCCs. Grade 1 tumors had an FGFR3 mutation in 88% of patient samples and grade 3 tumors in 16% of patient samples. Conversely, aberrant expression patterns of MIB-1, P53, and P27^kip1 were seen in 5%, 2%, and 3% of grade 1 tumors and in 85%, 60%, and 56% of grade 3 tumors, respectively. In multivariate analysis with recurrence rate, progression, and disease-specific survival as end points, the combination of FGFR3 and MIB-1 proved independently significant in all three cases. By using these two molecular markers, three molecular grades (mGs) could be identified: mG1 (mutation; normal expression), favorable prognosis; mG2 (two remaining combinations), intermediate prognosis; and mG3 (no mutation; high expression), poor prognosis. The molecular variables were more reproducible than pathologic grade (85% to 100% v 47% to 61%). Conclusion: The FGFR3 mutation represents the favorable molecular pathway of UCC. Molecular grading provides a new, simple, and highly reproducible tool for clinical decision making in UCC patients.