Molecular Sampling of Prostate Cancer: A Dilemma for Predicting Disease Progression

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Molecular Sampling of Prostate Cancer: A Dilemma for Predicting Disease Progression

Show simple item record Sboner, Andrea Demichelis, Francesca Calza, Stefano Pawitan, Yudi Hoshida, Yujin Perner, Sven Andersson, Swen-Olof Varenhorst, Eberhard Johansson, Jan-Erik Gerstein, Mark B Rubin, Mark A Andrén, Ove Setlur, Sunita Ramakrishna Adami, Hans-Olov Fall, Katja Mucci, Lorelei Ann Kantoff, Philip Wayne Stampfer, Meir Jonathan Golub, Todd R. 2011-12-23T02:08:21Z 2010
dc.identifier.citation Sboner, Andrea, Francesca Demichelis, Stefano Calza, Yudi Pawitan, Sunita R. Setlur, Yujin Hoshida, Sven Perner, et al. 2010. Molecular sampling of prostate cancer: A dilemma for predicting disease progression. BMC Medical Genomics 3: 8. en_US
dc.identifier.issn 1755-8794 en_US
dc.description.abstract Background: Current prostate cancer prognostic models are based on pre-treatment prostate specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. Methods: We analyzed a Swedish Watchful Waiting cohort with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several statistical and machine learning models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Results: Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Conclusions: The determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses making the development of molecular biomarkers for prostate cancer progression challenging. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1755-8794-3-8 en_US
dc.relation.hasversion en_US
dash.license LAA
dc.title Molecular Sampling of Prostate Cancer: A Dilemma for Predicting Disease Progression en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMC Medical Genomics en_US Adami, Hans-Olov 2011-12-23T02:08:21Z
dash.affiliation.other SPH^Epidemiology en_US
dash.affiliation.other SPH^Epidemiology en_US
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other HMS^Pediatrics-Children's Hospital en_US

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