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 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.
Sboner
Andrea
A
Department of Pathology and Laboratory Medicine, Weill Cornell Medical Center, New York, NY, USA.
Demichelis
Francesca
F
Calza
Stefano
S
Pawitan
Yudi
Y
Setlur
Sunita R
SR
Hoshida
Yujin
Y
Perner
Sven
S
Adami
Hans-Olov
HO
Fall
Katja
K
Mucci
Lorelei A
LA
Kantoff
Philip W
PW
Stampfer
Meir
M
Andersson
Swen-Olof
SO
Varenhorst
Eberhard
E
Johansson
Jan-Erik
JE
Gerstein
Mark B
MB
Golub
Todd R
TR
Rubin
Mark A
MA
Andrén
Ove
O
D014408:Biomarkers, Tumor / Q000235:genetics; D018450:Disease Progression; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000473:pathology*
D014408:Biomarkers, Tumor