Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy
By N. Erho, Anamaria Crisan, I. Vergara, Anirban P. Mitra, M. Ghadessi, C. Buerki, E. Bergstralh, T. Kollmeyer, S. Fink, Z. Haddad and 11 more. PLoS ONE, 2013
661 citations recorded by the source corpus.
Purpose Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.
Subfields: Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/012bdc01b6065abae1b0932cfbf5229adbe8eebb
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In Pulmonary and Respiratory Medicine
Citations in the corpus
- Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy. (2017, methodology): “Furthermore, we have developed the methodology for single patient subtyping in a CLIAcertified laboratory [16], which represents a significant step toward potential clinical application of molecular subtyping.”
- The Estrogen receptor alpha-regulated lncRNA NEAT1 is a critical modulator of prostate cancer (2014, positive): “…and prostate cancer clinical outcomes in 594 patients from two radical prostatectomy cohorts with long-term clinical follow-up from the Mayo Clinic 35,36 We assessed the prognostic potential of NEAT1 expression using several statistical measures and correlating it with biochemical recurrence…”
- Genetics and biology of prostate cancer (2018, background): “…≤6) and aggressive (Gleason score ≥8) tumors to better predict outcomes (e.g., cancer death, recurrence, and metastasis) of intermediate-risk disease (Gleason score 7) (Cuzick et al. 2011, 2012; Penney et al. 2011; Erho et al. 2013; Irshad et al. 2013; Hieronymus et al. 2014; Sinnott et al. 2017).”