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Keywords {🔍}

cancer, prostate, pubmed, article, google, scholar, molecular, clinical, men, homogeneity, lethal, cas, study, set, analysis, disease, score, gene, results, additional, data, genes, file, models, samples, group, erg, expression, tumor, heterogeneity, progression, gleason, figure, cases, central, patients, model, cohort, indolent, groups, design, psa, signature, rubin, usa, learning, validation, rearrangement, average, participated,

Topics {✒️}

paraffin-embedded formalin-fixed blocks pre-prostate specific antigen tailed t-test p-values open access article pre-publication history prostatic intraepithelial neoplasia predict cancer-specific survival castration-resistant prostate cancer tmprss2-erg fusion heterogeneity article download pdf correlation-based score proposed iterative cross-validation procedure gene expression profiling high-density tumor regions jan-erik johansson inter-tumor heterogeneity plays gleason-score correlated genes study end-point committee circulating lung-cancer cells model including topoisomerase-2a split-sample validation procedure complementary dna-mediated annealing full size image predicting disease progression silhouette width tmprss2-erg fusion molecular biomarkers erg gene fusion privacy choices/manage cookies microarray analysis identifies tmprss2 gene rearrangements molecular signatures relevant hans-olov adami high-grade pin [38] prostate cancer progression gene expression omnibus measure gene expression gene expression monitoring gene expression correlates gene expression alterations erg gene rearrangement paraffin-embedded tissues cancer specific death early prostate cancer gene expression data gene expression patterns gene expression measurements human lung carcinomas clinically localized disease localized prostate cancer

Questions {❓}

• Andren O, Fall K, Franzen L, Andersson S, Johansson J, Rubin MA: How Well Does the Gleason Score Predict Prostate Cancer Death?
• How can we account for this clinical heterogeneity?
• How does sampling play a role in this scenario?

Schema {🗺️}

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         headline:Molecular sampling of prostate cancer: a dilemma for predicting disease progression
         description: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.
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      headline:Molecular sampling of prostate cancer: a dilemma for predicting disease progression
      description: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.
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         Prostate Specific Antigen
         Gleason Score
         Predict Disease Progression
         Silhouette Width
         Human Genetics
         Microarrays
         Gene Expression
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      name:Department of Pathology and Laboratory Medicine, Weill Cornell Medical Center, New York, USA
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      name:Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
      name:Department of Biomedical Sciences and Biotechnologies, University of Brescia, Brescia, Italy
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      name:Department of Pathology, Brigham and Women's Hospital, Boston, USA
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      name:The Dana Farber Cancer Institute, Boston, USA
      name:Department of Pathology and Laboratory Medicine, Weill Cornell Medical Center, New York, USA
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      name:Department of Urology, Linköping University Hospital, Linköping, Sweden
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      name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
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      name:The Broad Institute of MIT and Harvard, Cambridge, USA
      name:Department of Urology, Örebro University Hospital, Örebro, Sweden

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