An expanded biomarker panel for the detection of prostate cancer from urine DNA.

BACKGROUND: Prostate cancer diagnosis using the PSA test remains controversial because of overdiagnosis and overtreatment of potentially indolent cancers. There remains a need to increase the diagnostic lead time and to target treatment to patients with significant disease. One possible approach to overcome the limitations of PSA is to screen men for the molecular signature of early PCA, monitor the rate of disease progression and target treatment to patients who are likely to benefit from it. Such an approach requires a large panel of markers that define a molecular clock for PCA. We recently developed a panel of 19 markers for the non-invasive detection of PCA from urine DNA. It raised the possibility that additional methylation markers could be successfully analyzed from urine DNA, a prerequisite for increasing the diagnostic lead time and enabling disease monitoring. METHODS: We developed semi-quantitative polymerase chain reaction assays for 13 additional markers and determined their methylation status in 150 urine DNAs from 94 patients with elevated PSA. Eighty five samples were obtained following DRE and 65 samples were from first void. We combined the data of the 13 new markers with the previously reported 19 markers and calculated the sensitivity, specificity, negative and positive predictive values at every threshold from one to 32 positive markers. RESULTS: Using 10of32 positive markers as the threshold to recommend a biopsy yields a sensitivity of 81% (95% CI 0.68-0.93) and 93% (95% CI 0.84-1.02) and a specificity of 76% (95% CI 0.63-0.88) and 77% (95% CI 0.63-0.91) from DRE and FV DNA, respectively. The PPV was 71% and 77% and the NPV was 85% and 93% from DRE and FV, respectively. CONCLUSIONS: This study shows that large marker panels can be analyzed from urine DNA without loss of sensitivity or specificity. Using 32 markers improved the stratification of patients undergoing screening for PCA particularly for patients below the 10of32 threshold. The results show the utility of larger biomarker panels for PCA diagnosis and suggest that the development of the panels needed to monitor disease progression could be successfully accomplished.

A panel of DNA methylation markers for the detection of prostate cancer from FV and DRE urine DNA.

Background: Early screening for prostate cancer (PCA) remains controversial because of overdiagnosis and overtreatment of clinically insignificant cancers. Even though a number of diagnostic tests have been developed to improve on PSA testing, there remains a need for a more informative non-invasive test for PCA. The objective of this study is to identify a panel of DNA methylation markers suitable for a non-invasive diagnostic test from urine DNA collected following a digital rectal exam (DRE) and/or from first morning void (FV). A secondary objective is to determine if the cumulative methylation is indicative of biopsy findings. Methods: DRE and FV urine samples were prospectively collected from 94 patients and analyzed using 24 methylation-specific quantitative PCR assays derived from 19 CpG islands. The methylation of individual markers and various combinations of markers was compared to biopsy results. A methylation threshold for cancer classification was determined using a target specificity of 70%. The average methylation and the number of positive markers were also compared to the result of the biopsy, and the area under the receiver operating characteristic curves (AUCs) were calculated. Results: Methylation of all 19 markers was detected in FV and DRE DNAs. Combining the methylation of two or more markers improved on individual marker results. Using 6of19 methylated markers as the threshold for cancer classification yielded a specificity of 71% (95% CI, 0.57-0.86) from both DRE and FV and a sensitivity of 89% (95% CI, 0.79-0.97) from DRE and 94% (95% CI, 0.84-1.0) from FV. The negative predictive value at the 6of19 threshold was ≥ 90 for both DNA types. Conclusions: PCA-specific methylation was detected in both FV and DRE DNA. There was no significant difference in diagnostic accuracy at the 6of19 threshold between DRE and FV urine DNA. The results support the development of a non-invasive diagnostic test to reduce unnecessary biopsies in men with elevated PSA. The test can also provide patients with personalized recommendations based on their own methylation profile.

A panel of DNA methylation markers reveals extensive methylation in histologically benign prostate biopsy cores from cancer patients.

BACKGROUND: Men with a negative first prostate biopsy will undergo one or more additional biopsies if they remain at high suspicion of prostate cancer. To date, there are no diagnostic tests capable of identifying patients at risk for a positive diagnosis with the predictive power needed to eliminate unnecessary repeat biopsies. Efforts to develop clinical tests using the epigenetic signature of cores recovered from first biopsies have been limited to a few markers and lack the sensitivity and specificity needed for widespread clinical adoption. METHODS: We developed methylation-specific quantitative polymerase chain reaction assays for a panel of 24 markers that are preferentially methylated in prostate cancer. We modified the bisulfite conversion conditions to allow the integration of the methylation information from multiple markers. We determined the methylation status of the 24 markers in 213 prostate biopsy cores from 104 patients, 37 prostate cancer patients and 67 controls. We performed logistic regression on combinations of markers as well as the entire panel of 24 markers to identify the best candidates for a diagnostic test. RESULTS: The marker panel differentiated between cancer cores and benign cores from non-cancer patients with 100% sensitivity and 97% specificity. Furthermore, the panel detected significant methylation in benign cores from prostate cancer patients that was not present in controls. Using methylation of 5 out of 24 to define a cancer case, the analysis of a single benign biopsy core identified 62% of prostate cancer patients undergoing repeat biopsies. ROC curve analysis showed that markers commonly methylated in benign cores from cancer patients are the best candidates for a diagnostic test. The results suggest that 5 to 10 markers will be needed to achieve optimal predictive power. CONCLUSIONS: This study shows that epigenetic field effects differ significantly between cancer patients and controls. Their detection in benign biopsy cores can form the basis of diagnostic tests to identify patients in need of repeat biopsies, reducing the cost of continued PCA screening by up to 40%. They could also be used to identify prostate cancer patients with low grade disease who are likely candidates for active surveillance or focal therapy.

Knockout of the erythromycin biosynthetic cluster gene, eryBI, blocks isoflavone glucoside bioconversion during erythromycin fermentations in Aeromicrobium erythreum but not in Saccharopolyspora erythraea.

Isoflavone glucosides are valuable nutraceutical compounds and are present in commercial fermentations, such as the erythromycin fermentation, as constituents of the soy flour in the growth medium. The purpose of this study was to develop a method for recovery of the isoflavone glucosides as value-added coproducts at the end of either Saccharopolyspora erythraea or Aeromicrobium erythreum fermentation. Because the first step in isoflavone metabolism was known to be the conversion of isoflavone glucosides to aglycones by a beta-glucosidase, we chose to knock out the only beta-glucosidase gene known at the start of the study, eryBI, to see what effect this had on metabolism of isoflavone glucosides in each organism. In the unicellular erythromycin producer A. erythreum, knockout of eryBI was sufficient to block the conversion of isoflavone glucosides to aglycones. In S. erythraea, knockout of eryBI had no effect on this reaction, suggesting that other beta-glucosidases are present. Erythromycin production was not significantly affected in either strain as a result of the eryBI knockout. This study showed that isoflavone metabolism could be blocked in A. erythreum by eryBI knockout but that eryBI knockout was not sufficient to block isoflavone metabolism in S. erythraea.

Engineering of the methylmalonyl-CoA metabolite node of Saccharopolyspora erythraea for increased erythromycin production.

Engineering of the methylmalonyl-CoA (mmCoA) metabolite node of the Saccharopolyspora erythraea wild-type strain through duplication of the mmCoA mutase (MCM) operon led to a 50% increase in erythromycin production in a high-performance oil-based fermentation medium. The MCM operon was carried on a 6.8kb DNA fragment in a plasmid which was inserted by homologous recombination into the S. erythraea chromosome. The fragment contained one uncharacterized gene, ORF1; three MCM related genes, mutA, mutB, meaB; and one gntR-family regulatory gene, mutR. Additional strains were constructed containing partial duplications of the MCM operon, as well as a knockout of ORF1. None of these strains showed any significant alteration in their erythromycin production profile. The combined results showed that increased erythromycin production only occurred in a strain containing a duplication of the entire MCM operon including mutR and a predicted stem-loop structure overlapping the 3' terminus of the mutR coding sequence.

Effects of methylmalonyl-CoA mutase gene knockouts on erythromycin production in carbohydrate-based and oil-based fermentations of Saccharopolyspora erythraea.

In carbohydrate-based fermentations of Saccharopolyspora erythraea, a polar knockout of the methylmalonyl-CoA mutase (MCM) gene, mutB, improved erythromycin production an average of 126% (within the range of 102-153% for a 0.95 confidence interval). In oil-based fermentations, where erythromycin production by the wild-type strain averages 184% higher (141-236%, 0.95 CI) than in carbohydrate-based fermentations, the same polar knockout in mutB surprisingly reduced erythromycin production by 66% (53-76%, 0.95 CI). A metabolic model is proposed where in carbohydrate-based fermentations MCM acts as a drain on the methylmalonyl-CoA metabolite pool, and in oil-based fermentations, MCM acts in the reverse direction to fill the methylmalonyl-CoA pool. Therefore, the model explains, in part, how the well-known oil-based process improvement for erythromycin production operates at the biochemical level; furthermore, it illustrates how the mutB erythromycin strain improvement mutation operates at the genetic level in carbohydrate-based fermentations.

Engineering precursor flow for increased erythromycin production in Aeromicrobium erythreum.

Metabolic engineering technology for industrial microorganisms is under development to create rational, more reliable, and more cost-effective approaches to strain improvement. Strain improvement is a critical component of the drug development process, yet the genetic basis for high production by industrial microorganisms is still a mystery. In this study, a search was begun for genetic modifications critical for high-level antibiotic production. The model system used was erythromycin production studied in the unicellular actinomycete, Aeromicrobium erythreum. A tagged-mutagenesis approach allowed reverse engineering of improved strains, revealing two genes, mutB and cobA, in the primary metabolic branch for methylmalonyl-CoA utilization. Knockouts in these genes created a permanent metabolic switch in the flow of methylmalonyl-CoA, from the primary branch into a secondary metabolic branch, driving erythromycin overproduction. The model provides insights into the regulation and evolution of secondary metabolism.

The erythromycin biosynthetic gene cluster of Aeromicrobium erythreum.

The erythromycin-biosynthetic (ery) gene cluster of Aeromicrobium erythreum was cloned and characterized. The 55.4-kb cluster contains 25 ery genes. Homologues were found for each gene in the previously characterized ery gene cluster from Saccharopolyspora erythraea. In addition, four new predicted ery genes were identified. Two of the new predicted genes, coding for a phosphopantetheinyl transferase (eryP) and a type II thioesterase (eryTII), were internal to the ery cluster. The other two new genes, coding for a thymidine 5'-diphosphate-glucose synthase (eryDI) and a MarR-family transcriptional repressor (ery-ORF25), were found at the two ends of the ery cluster. A knockout in eryDI showed it to be essential for erythromycin biosynthesis. The gene order of the two ery clusters was conserved within a core region of 15 contiguous genes, with the exception of IS1136 which was not found in the A. erythreum cluster. Beyond the core region, gene shuffling had occurred between the two sides of the cluster. The flanking regions of the two ery clusters were not alike in the type of genes found.