PURPOSE: In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. PATIENTS AND METHODS: Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. RESULTS: Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. CONCLUSION: We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.
PURPOSE: In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. PATIENTS AND METHODS: Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. RESULTS: Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. CONCLUSION: We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.
Authors: Benjamin A Mooso; Ruth L Vinall; Maria Mudryj; Stanley A Yap; Ralph W deVere White; Paramita M Ghosh Journal: J Urol Date: 2014-08-23 Impact factor: 7.450
Authors: Mirentxu Santos; Mónica Martínez-Fernández; Marta Dueñas; Ramón García-Escudero; Begoña Alfaya; Felipe Villacampa; Cristina Saiz-Ladera; Clotilde Costa; Marta Oteo; José Duarte; Victor Martínez; Mª José Gómez-Rodriguez; Mª Luisa Martín; Manoli Fernández; Patrick Viatour; Miguel A Morcillo; Julien Sage; Daniel Castellano; Jose L Rodriguez-Peralto; Federico de la Rosa; Jesús M Paramio Journal: Cancer Res Date: 2014-09-24 Impact factor: 12.701
Authors: Woonyoung Choi; Sima Porten; Seungchan Kim; Daniel Willis; Elizabeth R Plimack; Jean Hoffman-Censits; Beat Roth; Tiewei Cheng; Mai Tran; I-Ling Lee; Jonathan Melquist; Jolanta Bondaruk; Tadeusz Majewski; Shizhen Zhang; Shanna Pretzsch; Keith Baggerly; Arlene Siefker-Radtke; Bogdan Czerniak; Colin P N Dinney; David J McConkey Journal: Cancer Cell Date: 2014-02-10 Impact factor: 31.743
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