Molecular sampling of prostate cancer: a dilemma for predicting disease progression
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 can...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
| Published: |
BioMed Central Ltd.
2012
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| Online Access: | http://hdl.handle.net/1721.1/69537 |
| _version_ | 1811083192445173760 |
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| author | Sboner, Andrea Demichelis, Francesca Calza, Stefano Pawitan, Yudi Setlur, Sunita R. Hoshida, Yujin Perner, Sven Adami, Hans-Olov Fall, Katja Mucci, Lorelei A. Kantoff, Philip W. Stampfer, Meir Andersson, Swen-Olof Varenhorst, Eberhard Johansson, Jan-Erik Gerbstein, Mark B. Golub, Todd R. Rubin, Mark A. Andren, Ove |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Sboner, Andrea Demichelis, Francesca Calza, Stefano Pawitan, Yudi Setlur, Sunita R. Hoshida, Yujin Perner, Sven Adami, Hans-Olov Fall, Katja Mucci, Lorelei A. Kantoff, Philip W. Stampfer, Meir Andersson, Swen-Olof Varenhorst, Eberhard Johansson, Jan-Erik Gerbstein, Mark B. Golub, Todd R. Rubin, Mark A. Andren, Ove |
| author_sort | Sboner, Andrea |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:27:59Z |
| format | Article |
| id | mit-1721.1/69537 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:27:59Z |
| publishDate | 2012 |
| publisher | BioMed Central Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/695372022-09-28T08:04:04Z Molecular sampling of prostate cancer: a dilemma for predicting disease progression Sboner, Andrea Demichelis, Francesca Calza, Stefano Pawitan, Yudi Setlur, Sunita R. Hoshida, Yujin Perner, Sven Adami, Hans-Olov Fall, Katja Mucci, Lorelei A. Kantoff, Philip W. Stampfer, Meir Andersson, Swen-Olof Varenhorst, Eberhard Johansson, Jan-Erik Gerbstein, Mark B. Golub, Todd R. Rubin, Mark A. Andren, Ove Harvard University--MIT Division of Health Sciences and Technology Hoshida, Yujin Hoshida, Yujin 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. National Cancer Institute (U.S.) (NCI grant P50 90381) National Institutes of Health (U.S.) (NIH grant RR19895) Biomedical High Performance Computing Center 2012-03-01T17:03:50Z 2012-03-01T17:03:50Z 2010-03 2009-11 Article http://purl.org/eprint/type/JournalArticle http://hdl.handle.net/1721.1/69537 Sboner, Andrea et al. “Molecular Sampling of Prostate Cancer: a Dilemma for Predicting Disease Progression.” BMC Medical Genomics 3.1 (2010): 8. en_US http://dx.doi.org/10.1186/1755-8794-3-8 BMC Medical Genomics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 application/pdf BioMed Central Ltd. BioMed Central |
| spellingShingle | Sboner, Andrea Demichelis, Francesca Calza, Stefano Pawitan, Yudi Setlur, Sunita R. Hoshida, Yujin Perner, Sven Adami, Hans-Olov Fall, Katja Mucci, Lorelei A. Kantoff, Philip W. Stampfer, Meir Andersson, Swen-Olof Varenhorst, Eberhard Johansson, Jan-Erik Gerbstein, Mark B. Golub, Todd R. Rubin, Mark A. Andren, Ove Molecular sampling of prostate cancer: a dilemma for predicting disease progression |
| title | Molecular sampling of prostate cancer: a dilemma for predicting disease progression |
| title_full | Molecular sampling of prostate cancer: a dilemma for predicting disease progression |
| title_fullStr | Molecular sampling of prostate cancer: a dilemma for predicting disease progression |
| title_full_unstemmed | Molecular sampling of prostate cancer: a dilemma for predicting disease progression |
| title_short | Molecular sampling of prostate cancer: a dilemma for predicting disease progression |
| title_sort | molecular sampling of prostate cancer a dilemma for predicting disease progression |
| url | http://hdl.handle.net/1721.1/69537 |
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