Metabolomic Profiling Identifies Biochemical Pathways Associated with Castration-Resistant Prostate Cancer

Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools

Metabolomic Profiling Identifies Biochemical Pathways Associated with Castration-Resistant Prostate Cancer

Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools

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

Univariable and multivariable odds Ratios for CC, GC and GCC, and clinicopathologic variables.

<p>Odd ratios for multivariable classifiers are adjusted as indicated in the Materials and Methods. CC: clinical-only classifier. GC: genomic classifier. GCC: integrated genomic-clinical classifier.</p

Performance of external signatures in training and validation sets.

<p>For each signature, the institution associated to it, year of publication, lead author, the AUC obtained in the training and validation sets, as well as the 95% Confidence Interval for this metric is shown.</p

Reclassification by GC of GS risk categories among cases and controls in the validation set of patients.

<p>Pathologic GS is categorized into four groups: ≤6,7, 8 and ≥9. Gleason groups are re-classified by high (>0.5) and low GC risk scores. Total number of patients in each category is further subdivided into the number of cases and those that died of prostate cancer (PCSM).</p

Consort diagram.

<p>Study breakdown into cases and controls. Training and validation sets are shown.</p

Clinical characteristics of cases and controls among training and validation sets.

<p>Clinical characteristics of cases and controls among training and validation sets.</p