A Urine Exosome Gene Expression Panel Distinguishes between Indolent and Aggressive Prostate Cancers at Biopsy.

PURPOSE: Prostate cancer is predominantly indolent at diagnosis with a small fraction (15% to 25%) representing aggressive subtype (Gleason score 7-10), which is prone to metastatic progression. It is critical to explore noninvasive assays for the early detection of this aggressive subtype, when it still can be treated effectively. Additionally, there is an emerging need to develop markers that perform equally well across races, as racial differences in the prevalence and mortality of prostate cancer has become evident. MATERIALS AND METHODS: First catch, nondigital rectal examination urine specimens were collected from patients undergoing diagnostic biopsy. Total RNA was extracted from urinary exosomes and a quantitative expression assay protocol using droplet digital polymerase chain reaction was developed for detection of candidate genes in exosomal mRNAs from urine. Clinical performance for the gene expression assay was evaluated to predict high grade cancer (Gleason score 7-10) from low grade cancer (Gleason score 6) and cancer negative cases at biopsy. Assay performance was examined in combination with standard of care to determine improvement in model prediction. RESULTS: In a racially diverse patient cohort a 2-gene panel (PCA3, PCGEM1), in combination with standard of care variables, significantly improved the prediction of high grade cancer at diagnosis compared to standard of care variables alone (AUC 0.88 vs 0.80, respectively, p=0.016). Decision curve analysis showed that there is a benefit of adopting the gene panel for detection of high grade cancer compared to standard of care alone. CONCLUSIONS: This study highlights the potential for developing broadly applicable prostate cancer diagnostic biomarker panels for aggressive prostate cancer using our novel gene expression assay platform.

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions.

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, and prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular, and functional aberrations within the lesion and within individual cells. Single-cell proteomic data will be essential for the establishment of new tools with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included (i) The best way/s to analyze single-cells from fresh and preserved tissue; (ii) Detection and analysis of secreted molecules and from single cells, especially from a tissue slice; (iii) Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment; (iv) Multiomic integration of data to support and inform proteomic measurements; (v) Subcellular organelles-identifying abnormal structure, function, distribution, and location within individual premalignant and malignant cells; (vi) How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post-translational modifications (PTM); (vii) The depth of coverage measured concurrently using single-cell techniques; (viii) Quantitation - absolute or semiquantitative? (ix) Single methodology or multiplexed combinations? (x) Application of analytical methods for identification of biologically significant subsets; (xi) Data visualization of N-dimensional data sets; (xii) How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME); (xiii) Associations between intrinsic cellular processes and extrinsic stimuli; (xiv) How to predict cellular responses to stress-inducing stimuli; (xv) Identification of new markers for prediction of progression from precursor, benign, and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells; (xvi) Identification of new targets for immunoprevention or immunotherapy-identification of neoantigens and surfactome of individual cells within a lesion.

Carrier-Assisted Single-Tube Processing Approach for Targeted Proteomics Analysis of Low Numbers of Mammalian Cells.

Heterogeneity in composition is inherent in all cell populations, even those containing a single cell type. Single-cell proteomics characterization of cell heterogeneity is currently achieved by antibody-based technologies, which are limited by the availability of high-quality antibodies. Herein we report a simple, easily implemented, mass spectrometry (MS)-based targeted proteomics approach, termed cLC-SRM (carrier-assisted liquid chromatography coupled to selected reaction monitoring), for reliable multiplexed quantification of proteins in low numbers of mammalian cells. We combine a new single-tube digestion protocol to process low numbers of cells with minimal loss together with sensitive LC-SRM for protein quantification. This single-tube protocol builds upon trifluoroethanol digestion and further minimizes sample losses by tube pretreatment and the addition of carrier proteins. We also optimized the denaturing temperature and trypsin concentration to significantly improve digestion efficiency. cLC-SRM was demonstrated to have sufficient sensitivity for reproducible detection of most epidermal growth factor receptor (EGFR) pathway proteins expressed at levels ≥30 000 and ≥3000 copies per cell for 10 and 100 mammalian cells, respectively. Thus, cLC-SRM enables reliable quantification of low to moderately abundant proteins in less than 100 cells and could be broadly useful for multiplexed quantification of important proteins in small subpopulations of cells or in size-limited clinical samples. Further improvements of this method could eventually enable targeted single-cell proteomics when combined with either SRM or other emerging ultrasensitive MS detection.

Reversible Capture and Release of Cl2 and Br2 with a Redox-Active Metal-Organic Framework.

Extreme toxicity, corrosiveness, and volatility pose serious challenges for the safe storage and transportation of elemental chlorine and bromine, which play critical roles in the chemical industry. Solid materials capable of forming stable nonvolatile compounds upon reaction with elemental halogens may partially mitigate these challenges by allowing safe halogen release on demand. Here we demonstrate that elemental halogens quantitatively oxidize coordinatively unsaturated Co(II) ions in a robust azolate metal-organic framework (MOF) to produce stable and safe-to-handle Co(III) materials featuring terminal Co(III)-halogen bonds. Thermal treatment of the oxidized MOF causes homolytic cleavage of the Co(III)-halogen bonds, reduction to Co(II), and concomitant release of elemental halogens. The reversible chemical storage and thermal release of elemental halogens occur with no significant losses of structural integrity, as the parent cobaltous MOF retains its crystallinity and porosity even after three oxidation/reduction cycles. These results highlight a material operating via redox mechanism that may find utility in the storage and capture of other noxious and corrosive gases.

Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics.

BACKGROUND: Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations. METHODS: To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations. RESULTS: Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/µg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein. CONCLUSIONS: In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer.

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification.

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.

Analytical platform evaluation for quantification of ERG in prostate cancer using protein and mRNA detection methods.

BACKGROUND: The established methods for detecting prostate cancer (CaP) are based on tests using PSA (blood), PCA3 (urine), and AMACR (tissue) as biomarkers in patient samples. The demonstration of ERG oncoprotein overexpression due to gene fusion in CaP has thus provided ERG as an additional biomarker. Based on this, we hypothesized that ERG protein quantification methods can be of use in the diagnosis of prostate cancer. METHODS: An antibody-free assay for ERG3 protein detection was developed based on PRISM (high-pressure high-resolution separations with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry. We utilized TMPRSS2-ERG positive VCaP and TMPRSS2-ERG negative LNCaP cells to simulate three different sample types (cells, tissue, and post-DRE urine sediment). Enzyme-linked immunosorbent assay (ELISA), western blot, NanoString, and qRT-PCR were also used in the analysis of these samples. RESULTS: Recombinant ERG3 protein spiked into LNCaP cell lysates could be detected at levels as low as 20 pg by PRISM-SRM analysis. The sensitivity of the PRISM-SRM assay was approximately 10,000 VCaP cells in a mixed cell population model of VCaP and LNCaP cells. Interestingly, ERG protein could be detected in as few as 600 VCaP cells spiked into female urine. The sensitivity of the in-house ELISA was similar to the PRISM-SRM assay, with detection of 30 pg of purified recombinant ERG3 protein and 10,000 VCaP cells. On the other hand, qRT-PCR exhibited a higher sensitivity, as TMPRSS2-ERG transcripts were detected in as few as 100 VCaP cells, in comparison to NanoString methodologies which detected ERG from 10,000 cells. CONCLUSIONS: Based on this data, we propose that the detection of both ERG transcriptional products with RNA-based assays, as well as protein products of ERG using PRISM-SRM assays, may be of clinical value in developing diagnostic and prognostic assays for prostate cancer given their sensitivity, specificity, and reproducibility.

Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum.

Sensitive detection of low-abundance proteins in complex biological samples has typically been achieved by immunoassays that use antibodies specific to target proteins; however, de novo development of antibodies is associated with high costs, long development lead times, and high failure rates. To address these challenges, we developed an antibody-free strategy that involves PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing) for sensitive selected reaction monitoring (SRM)-based targeted protein quantification. The strategy capitalizes on high-resolution reversed-phase liquid chromatographic separations for analyte enrichment, intelligent selection of target fractions via on-line SRM monitoring of internal standards, and fraction multiplexing before nano-liquid chromatography-SRM quantification. Application of this strategy to human plasma/serum demonstrated accurate and reproducible quantification of proteins at concentrations in the 50-100 pg/mL range, which represents a major advance in the sensitivity of targeted protein quantification without the need for specific-affinity reagents. Application to a set of clinical serum samples illustrated an excellent correlation between the results obtained from the PRISM-SRM assay and those from clinical immunoassay for the prostate-specific antigen level.

A highly sensitive targeted mass spectrometric assay for quantification of AGR2 protein in human urine and serum.

Anterior gradient 2 (AGR2) is a secreted, cancer-associated protein in many types of epithelial cancer cells. We developed a highly sensitive targeted mass spectrometric assay for quantification of AGR2 in urine and serum. Digested peptides from clinical samples were processed by PRISM (high pressure and high resolution separations coupled with intelligent selection and multiplexing), which incorporates high pH reversed-phase liquid chromatography (LC) separations to fractionate and select target fractions for follow-on LC-selected reaction monitoring (LC-SRM) analyses. The PRISM-SRM assay for AGR2 showed a reproducibility of <10% CV and limit of quantification (LOQ) values of ∼130 pg/mL in serum and ∼10 pg per 100 µg of total protein mass in urine, respectively. A good correlation (R(2) = 0.91) was observed for the measurable AGR2 concentrations in urine between SRM and enzyme-linked immunosorbent assay (ELISA). On the basis of an initial cohort of 37 subjects, urinary AGR2/PSA concentration ratios showed a significant difference (P = 0.026) between noncancer and cancer. Large clinical cohort studies are needed for the validation of AGR2 as a useful diagnostic biomarker for prostate cancer. Our work validated the approach of identifying candidate secreted protein biomarkers through genomics and measurement by targeted proteomics, especially for proteins where no immunoassays are available.

Targeted quantification of low ng/mL level proteins in human serum without immunoaffinity depletion.

We recently reported an antibody-free targeted protein quantification strategy, termed high-pressure, high-resolution separations with intelligent selection and multiplexing (PRISM), for achieving significantly enhanced sensitivity using selected reaction monitoring (SRM) mass spectrometry. Integrating PRISM with front-end IgY14 immunoaffinity depletion, sensitive detection of targeted proteins at 50-100 pg/mL levels in human blood plasma/serum was demonstrated. However, immunoaffinity depletion is often associated with undesired losses of target proteins of interest. Herein we report further evaluation of PRISM-SRM quantification of low-abundance serum proteins without immunoaffinity depletion. Limits of quantification (LOQ) at low ng/mL levels with a median coefficient of variation (CV) of ∼12% were achieved for proteins spiked into human female serum. PRISM-SRM provided >100-fold improvement in the LOQ when compared to conventional LC-SRM measurements. PRISM-SRM was then applied to measure several low-abundance endogenous serum proteins, including prostate-specific antigen (PSA), in clinical prostate cancer patient sera. PRISM-SRM enabled confident detection of all target endogenous serum proteins except the low pg/mL-level cardiac troponin T. A correlation coefficient >0.99 was observed for PSA between the results from PRISM-SRM and immunoassays. Our results demonstrate that PRISM-SRM can successfully quantify low ng/mL proteins in human plasma or serum without depletion. We anticipate broad applications for PRISM-SRM quantification of low-abundance proteins in candidate biomarker verification and systems biology studies.

Long-gradient separations coupled with selected reaction monitoring for highly sensitive, large scale targeted protein quantification in a single analysis.

Long-gradient separations coupled to tandem mass spectrometry (MS) were recently demonstrated to provide a deep proteome coverage for global proteomics; however, such long-gradient separations have not been explored for targeted proteomics. Herein, we investigate the potential performance of the long-gradient separations coupled with selected reaction monitoring (LG-SRM) for targeted protein quantification. Direct comparison of LG-SRM (5 h gradient) and conventional liquid chromatography (LC)-SRM (45 min gradient) showed that the long-gradient separations significantly reduced background interference levels and provided an 8- to 100-fold improvement in limit of quantification (LOQ) for target proteins in human female serum. On the basis of at least one surrogate peptide per protein, an LOQ of 10 ng/mL was achieved for the two spiked proteins in nondepleted human serum. The LG-SRM detection of seven out of eight endogenous plasma proteins expressed at ng/mL or subng/mL levels in clinical patient sera was also demonstrated. A correlation coefficient of >0.99 was observed for the results of LG-SRM and enzyme-linked immunosorbent assay (ELISA) measurements for prostate-specific antigen (PSA) in selected patient sera. Further enhancement of LG-SRM sensitivity was achieved by applying front-end IgY14 immunoaffinity depletion. Besides improved sensitivity, LG-SRM potentially offers much higher multiplexing capacity than conventional LC-SRM due to an increase in average peak widths (~3-fold) for a 300 min gradient compared to a 45 min gradient. Therefore, LG-SRM holds great potential for bridging the gap between global and targeted proteomics due to its advantages in both sensitivity and multiplexing capacity.

The Early Detection Research Network's Specimen reference sets: paving the way for rapid evaluation of potential biomarkers.

BACKGROUND: The mission of the National Cancer Institute's Early Detection Research Network (EDRN) is to identify and validate cancer biomarkers for clinical use. Since its inception, EDRN investigators have learned a great deal about the process of validating biomarkers for clinical use. Translational research requires a broad spectrum of research expertise, and coordinating collaborative activities can be challenging. The EDRN has developed a robust triage and validation system that serves the roles of both "facilitator" and "brake." CONTENT: The system consists of (a) establishing a reference set of specimens collected under PRoBE (Prospective Specimen Collection Retrospective Blinded Evaluation) design criteria; (b) using the reference set to prevalidate candidate biomarkers before committing to full-scale validation; (c) performing full-scale validation for those markers that pass prevalidation testing; and (d) ensuring that the reference set is sufficiently large in numbers and volumes of sample that it can also be used to study future candidate biomarkers. This system provides rigorous and efficient evaluation of candidate biomarkers and biomarker panels. Reference sets should also be constructed to enable high-quality biomarker-discovery research. SUMMARY: We describe the process of establishing our system in the hope that it will serve as an example of how to validate biomarkers for clinical application. We also hope that this description of the biospecimen reference sets available from the EDRN will encourage the biomarker research community--from academia or industry--to use this resource to advance biomarkers into clinical use.

The impact of prostate volume, number of biopsy cores and American Urological Association symptom score on the sensitivity of cancer detection using the Prostate Cancer Prevention Trial risk calculator.

PURPOSE: We assessed the independent predictive value of prostate volume, number of biopsy cores and AUASS (American Urological Association symptom score) compared to risk factors included in the PCPTRC (Prostate Cancer Prevention Trial risk calculator for prostate cancer) and PCPTHG (Prostate Cancer Prevention Trial risk calculator for high grade cancer [Gleason grade 7 or greater]). MATERIALS AND METHODS: Of 5,519 PCPT (Prostate Cancer Prevention Trial) participants used to construct the PCPTRC 4,958 with AUASS and prostate specific antigen 10 ng/ml or less were included on logistic regression analysis. Risk algorithms were evaluated in 571 EDRN (Early Detection Research Network) participants using the ROC AUC. RESULTS: A total of 1,094 participants (22.1%) had prostate cancer, of whom 232 (21.2%) had high grade disease. For prostate cancer prediction higher prostate specific antigen, abnormal digital rectal examination, family history of prostate cancer and number of cores were associated with increased risk, while volume was associated with decreased risk. Excluding prostate volume and number of cores, a history of negative biopsy and increased AUASS were also associated with lower risk. For high grade cancer higher prostate specific antigen, abnormal digital rectal examination, black race and number of cores were associated with increased risk and volume, while AUASS was associated with decreased risk. The AUC of the PCPTRC adjusted for volume and number of cores was 72.7% (using EDRN data), 68.2% when adjusted for AUASS alone and 67.6% PCPTRC. For high grade disease the AUCs were 74.8%, 74.0% and 73.5% (PCPTHG), respectively. CONCLUSIONS: Adjusted PCPT risk calculators for volume, number of cores and AUASS improve cancer detection.

Proteomic analysis of cancer-cell mitochondria.

Mitochondrial dysfunction and mutations in mitochondrial DNA have been frequently reported in cancer cells. Mitochondrial gene-expression signatures of transformed cells have been identified; however, the phenotypic effects of these genetic alterations remain to be established. Identification of mitochondrial proteins that are aberrantly expressed in cancer cells has been made possible by the recent development of mitochondrial functional proteomics and could identify new markers for early detection and risk assessment, as well as targets for therapeutic intervention.

Updating risk prediction tools: a case study in prostate cancer.

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics.

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-ß-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for 'all-in-one' one-pot sample preparation. This 'all-in-one' method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

Proteomic Analysis of Exosomes for Discovery of Protein Biomarkers for Prostate and Bladder Cancer.

Extracellular vesicles (EVs) are released by nearly all cell types as part of normal cell physiology, transporting biological cargo, including nucleic acids and proteins, across the cell membrane. In pathological states such as cancer, EV-derived cargo may mirror the altered state of the cell of origin. Exosomes are the smaller, 50-150 nanometer-sized EVs released from fusion of multivesicular endosomes with the plasma membrane. Exosomes play important roles in cell-cell communication and participate in multiple cancer processes, including invasion and metastasis. Therefore, proteomic analysis of exosomes is a promising approach to discover potential cancer biomarkers, even though it is still at an early stage. Herein, we critically review the advances in exosome isolation methods and their compatibility with mass spectrometry (MS)-based proteomic analysis, as well as studies of exosomes in pathogenesis and progression of prostate and bladder cancer, two common urologic cancers whose incidence rates continue to rise annually. As urological tumors, both urine and blood samples are feasible for noninvasive or minimally invasive analysis. A better understanding of the biological cargo and functions of exosomes via high-throughput proteomics will help provide new insights into complex alterations in cancer and provide potential therapeutic targets and personalized treatment for patients.

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression.

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

Towards Clinical Application of Methylated DNA Sequences as Cancer Biomarkers: A Joint NCI's EDRN and NIST Workshop on Standards, Methods, Assays, Reagents and Tools.

The workshop report, entitled Towards Clinical Application of Methylated DNA Sequences as Cancer Biomarkers: A Joint National Cancer Institute's Early Detection Research Network and National Institute of Standards and Technology Workshop, presents a summary of the main issues, current challenges, outcomes, and recommendations toward application of methylated DNA sequences as cancer biomarkers.

Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence.

BACKGROUND: As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression. METHODS: We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes. RESULTS: After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays. CONCLUSIONS: Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.