Molecular sampling of prostate cancer: a dilemma for predicting disease progression
Name
Sboner-2010-Molecular sampling of prostate cancer.pdf
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1.29 MB
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Checksum (MD5)
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Author(s)
Sboner, Andrea
Demichelis, Francesca
Calza, Stefano
Pawitan, Yudi
Setlur, Sunita R.
Hoshida, Yujin
Perner, Sven
Adami, Hans-Olov
Fall, Katja
Mucci, Lorelei A.
Date Issued
March 2010
Journal
BMC Medical Genomics
Publisher
BioMed Central Ltd.
Citation
Sboner, Andrea et al. “Molecular Sampling of Prostate Cancer: a Dilemma for Predicting Disease Progression.” BMC Medical Genomics 3.1 (2010): 8.
Version
Final published version
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/1755-8794-3-8
