Molecular sampling of prostate cancer: A dilemma for predicting disease progression

Andrea Sboner, Francesca Demichelis, Stefano Calza, Yudi Pawitan, Sunita R. Setlur, Yujin Hoshida, Sven Perner, Hans Olov Adami, Katja Fall, Lorelei A. Mucci, Philip W. Kantoff, Meir Stampfer, Swen Olof Andersson, Eberhard Varenhorst, Jan Erik Johansson, Mark B. Gerstein, Todd R. Golub, Mark A. Rubin, Ove Andrén

Research output: Contribution to journalArticle

143 Citations (Scopus)

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.

Original languageEnglish (US)
Article number8
JournalBMC Medical Genomics
Volume3
DOIs
StatePublished - May 7 2010
Externally publishedYes

Fingerprint

Disease Progression
Prostatic Neoplasms
Watchful Waiting
Transurethral Resection of Prostate
Molecular Models
Neoplasm Grading
Gene Expression Profiling
Prostate-Specific Antigen
Transcriptome
Paraffin
Formaldehyde
Ligation
Neoplasms
Complementary DNA
Biomarkers
Neoplasm Metastasis
Biopsy
Therapeutics

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Sboner, A., Demichelis, F., Calza, S., Pawitan, Y., Setlur, S. R., Hoshida, Y., ... Andrén, O. (2010). Molecular sampling of prostate cancer: A dilemma for predicting disease progression. BMC Medical Genomics, 3, [8]. https://doi.org/10.1186/1755-8794-3-8

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; Gerstein, Mark B.; Golub, Todd R.; Rubin, Mark A.; Andrén, Ove.

In: BMC Medical Genomics, Vol. 3, 8, 07.05.2010.

Research output: Contribution to journalArticle

Sboner, A, Demichelis, F, Calza, S, Pawitan, Y, Setlur, SR, Hoshida, Y, Perner, S, Adami, HO, Fall, K, Mucci, LA, Kantoff, PW, Stampfer, M, Andersson, SO, Varenhorst, E, Johansson, JE, Gerstein, MB, Golub, TR, Rubin, MA & Andrén, O 2010, 'Molecular sampling of prostate cancer: A dilemma for predicting disease progression', BMC Medical Genomics, vol. 3, 8. https://doi.org/10.1186/1755-8794-3-8
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 ; Gerstein, Mark B. ; Golub, Todd R. ; Rubin, Mark A. ; Andrén, Ove. / Molecular sampling of prostate cancer : A dilemma for predicting disease progression. In: BMC Medical Genomics. 2010 ; Vol. 3.
@article{21a5b4b94d394a508e346b3a52486099,
title = "Molecular sampling of prostate cancer: A dilemma for predicting disease progression",
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.",
author = "Andrea Sboner and Francesca Demichelis and Stefano Calza and Yudi Pawitan and Setlur, {Sunita R.} and Yujin Hoshida and Sven Perner and Adami, {Hans Olov} and Katja Fall and Mucci, {Lorelei A.} and Kantoff, {Philip W.} and Meir Stampfer and Andersson, {Swen Olof} and Eberhard Varenhorst and Johansson, {Jan Erik} and Gerstein, {Mark B.} and Golub, {Todd R.} and Rubin, {Mark A.} and Ove Andr{\'e}n",
year = "2010",
month = "5",
day = "7",
doi = "10.1186/1755-8794-3-8",
language = "English (US)",
volume = "3",
journal = "BMC Medical Genomics",
issn = "1755-8794",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Molecular sampling of prostate cancer

T2 - A dilemma for predicting disease progression

AU - Sboner, Andrea

AU - Demichelis, Francesca

AU - Calza, Stefano

AU - Pawitan, Yudi

AU - Setlur, Sunita R.

AU - Hoshida, Yujin

AU - Perner, Sven

AU - Adami, Hans Olov

AU - Fall, Katja

AU - Mucci, Lorelei A.

AU - Kantoff, Philip W.

AU - Stampfer, Meir

AU - Andersson, Swen Olof

AU - Varenhorst, Eberhard

AU - Johansson, Jan Erik

AU - Gerstein, Mark B.

AU - Golub, Todd R.

AU - Rubin, Mark A.

AU - Andrén, Ove

PY - 2010/5/7

Y1 - 2010/5/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77951723424&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951723424&partnerID=8YFLogxK

U2 - 10.1186/1755-8794-3-8

DO - 10.1186/1755-8794-3-8

M3 - Article

C2 - 20233430

AN - SCOPUS:77951723424

VL - 3

JO - BMC Medical Genomics

JF - BMC Medical Genomics

SN - 1755-8794

M1 - 8

ER -