Additional file 1: of Gene expression signatures of neuroendocrine prostate cancer and primary small cell prostatic carcinoma

Additional methods on bioinformatic processing and analysis, and additional legends. (DOCX 49 kb

La Charente

08 juillet 18941894/07/08 (A23,N10404)-1894/07/08.Appartient à l’ensemble documentaire : PoitouCh

Cumulative incidence of metastasis at 5 years post radical prostatectomy.

<p>A) Difference in re-graded 2005 ISUP modified and original Gleason score; B) Difference in primary re-graded 2005 ISUP modified and original Gleason score; and C) Difference in secondary re-graded 2005 ISUP modified and original Gleason score.</p

(A) Fused glands (400x). In the original Gleason grading this pattern was considered pattern 3. According to the 2005 ISUP Gleason system it would be graded as pattern 4; B) Adenocarcinoma with glomeruloid features currently assigned a Gleason pattern 4 (100x); C) Ill-defined glands (100x). This pattern would be graded as Gleason pattern 4 by the ISUP 2005 Gleason grading; and D) Individual cells (100x).

<p>This pattern was originally accepted under Gleason pattern 3 and would be assigned a Gleason pattern 5 according to the 2005 ISUP Gleason system.</p

Study diagram and patient selection criteria.

<p>Study diagram and patient selection criteria.</p

A) Survival concordance index. Reviewed Gleason score by the 2005 ISUP modified Gleason system has the highest c-index compared to original Gleason score. B) Decision curve analysis at 5 years post radical prostatectomy shows the net benefit of original and reviewed Gleason score across probability thresholds.

<p>The reviewed Gleason score shows the highest net benefit.</p

Cumulative incidence curves of A) metastasis by original Gleason score, B) metastasis by reviewed Gleason score.

<p>The number at risk in each group is shown in the footnote lines. Patients in the sub-cohort who do not experience the event are weighted by the inverse of the sampling fraction in following the case-cohort design.</p

Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

<div><p>Purpose</p><p>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.</p> <p>Methods</p><p>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.</p> <p>Results</p><p>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.</p> <p>Conclusion</p><p>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.</p> </div

Performance of classifiers and individual clinicopathologic variables.

<p>For each predictor, the AUC obtained in the training and validation sets, as well as the 95% Confidence Interval for this metric is shown. CC: clinical-only classifier. GC: genomic classifier. GCC: combined genomic-clinical classifier.</p

Score distributions of multivariable classifiers in cases and controls in validation set.

<p>Distributions of scores are plotted for A) CC B) GC and C) GCC for controls and cases. Median scores and 95% confidence intervals are represented by a horizontal black line and notches, respectively. Non-overlapping notches indicate that differences in the distribution of scores between cases and controls are statistically significant. Outliers are represented as points beyond the boxplot whiskers.</p