Molecular sampling of prostate cancer: a dilemma for predicting disease progression
- Equal contributors
1 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, 06520, USA
2 Department of Pathology and Laboratory Medicine, Weill Cornell Medical Center, New York, New York, USA
3 Institute for Computational Biomedicine, Weill Cornell Medical Center, New York, New York, USA
4 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
5 Department of Biomedical Sciences and Biotechnologies, University of Brescia, Brescia, Italy
6 Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, 02115, USA
7 The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
8 The Dana Farber Cancer Institute, Boston, Massachusetts, 02115, USA
9 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, 02115, USA
10 Department of Urology, Örebro University Hospital, Örebro, SE-701 85, Sweden
11 Harvard Medical School, Boston, Massachusetts 02115, USA
12 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
13 Department of Urology, Linköping University Hospital, Linköping, SE 581 85, Sweden
14 Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA
15 Department of Computer Science, Yale University, New Haven, Connecticut, 06520, USA
16 The Howard Hughes Medical Institute at The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
BMC Medical Genomics 2010, 3:8 doi:10.1186/1755-8794-3-8Published: 16 March 2010
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.
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.
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.
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.