Antibody-independent targeted quantification of TMPRSS2-ERG fusion protein products in prostate cancer

Multi-Omic blood analysis reveals differences in innate inflammatory sensitivity between species

Vertebrates differ greatly in responses to pro-inflammatory agonists such as bacterial lipopolysaccharide (LPS), complicating use of animal models to study human sepsis or inflammatory disorders. We compared transcriptomes of resting and LPS-exposed blood from six LPS-sensitive species (rabbit, pig, sheep, cow, chimpanzee, human) and four LPS-resilient species (mice, rats, baboon, rhesus), as well as plasma proteomes and lipidomes. Unexpectedly, at baseline, sensitive species already had enhanced expression of LPS-responsive genes relative to resilient species. After LPS stimulation, maximally different genes in resilient species included genes that detoxify LPS, diminish bacterial growth, discriminate sepsis from SIRS, and play roles in autophagy and apoptosis. The findings reveal the molecular landscape of species differences in inflammation, and may inform better selection of species for pre-clinical models.

Mass spectrometry-based targeted proteomics for analysis of protein mutations

Cancers are caused by accumulated DNA mutations. This recognition of the central role of mutations in cancer and recent advances in next-generation sequencing, has initiated the massive screening of clinical samples and the identification of 1000s of cancer-associated gene mutations. However, proteomic analysis of the expressed mutation products lags far behind genomic (transcriptomic) analysis. With comprehensive global proteomics analysis, only a small percentage of single nucleotide variants detected by DNA and RNA sequencing have been observed as single amino acid variants due to current technical limitations. Proteomic analysis of mutations is important with the potential to advance cancer biomarker development and the discovery of new therapeutic targets for more effective disease treatment. Targeted proteomics using selected reaction monitoring (also known as multiple reaction monitoring) and parallel reaction monitoring, has emerged as a powerful tool with significant advantages over global proteomics for analysis of protein mutations in terms of detection sensitivity, quantitation accuracy and overall practicality (e.g., reliable identification and the scale of quantification). Herein we review recent advances in the targeted proteomics technology for enhancing detection sensitivity and multiplexing capability and highlight its broad biomedical applications for analysis of protein mutations in human bodily fluids, tissues, and cell lines. Furthermore, we review recent applications of top-down proteomics for analysis of protein mutations. Unlike the commonly used bottom-up proteomics which requires digestion of proteins into peptides, top-down proteomics directly analyzes intact proteins for more precise characterization of mutation isoforms. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale targeted detection and quantification of important protein mutations are discussed.

The Mycobacterium tuberculosis protein O-phosphorylation landscape

Bacterial phosphosignalling has been synonymous with two-component systems and their histidine kinases, but many bacteria, including Mycobacterium tuberculosis (Mtb), also code for Ser/Thr protein kinases (STPKs). STPKs are the main phosphosignalling enzymes in eukaryotes but the full extent of phosphorylation on protein Ser/Thr and Tyr (O-phosphorylation) in bacteria is untested. Here we explored the global signalling capacity of the STPKs in Mtb using a panel of STPK loss-of-function and overexpression strains combined with mass spectrometry-based phosphoproteomics. A deep phosphoproteome with >14,000 unique phosphosites shows that O-phosphorylation in Mtb is a vastly underexplored protein modification that affects >80% of the proteome and extensively interfaces with the transcriptional machinery. Mtb O-phosphorylation gives rise to an expansive, distributed and cooperative network of a complexity that has not previously been seen in bacteria and that is on par with eukaryotic phosphosignalling networks. A resource of >3,700 high-confidence direct substrate-STPK interactions and their transcriptional effects provides signalling context for >80% of Mtb proteins and allows the prediction and assembly of signalling pathways for mycobacterial physiology.

Nutritional markers and proteome in patients undergoing treatment for pulmonary tuberculosis differ by geographic region

Introduction

Contemporary phase 2 TB disease treatment clinical trials have found that microbiologic treatment responses differ between African versus non-African regions, the reasons for which remain unclear. Understanding host and disease phenotypes that may vary by region is important for optimizing curative treatments.

Methods

We characterized clinical features and the serum proteome of phase 2 TB clinical trial participants undergoing treatment for smear positive, culture-confirmed TB, comparing host serum protein expression in clinical trial participants enrolled in African and Non-African regions. Serum samples were collected from 289 participants enrolled in the Centers for Disease Control and Prevention TBTC Study 29 (NCT00694629) at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment).

Results

After a peptide level proteome analysis utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS) and subsequent statistical analysis, a total of 183 core proteins demonstrated significant differences at both baseline and at week 8 timepoints between participants enrolled from African and non-African regions. The majority of the differentially expressed proteins were upregulated in participants from the African region, and included acute phase proteins, mediators of inflammation, as well as coagulation and complement pathways. Downregulated proteins in the African population were primarily linked to nutritional status and lipid metabolism pathways.

Conclusions

We have identified differentially expressed nutrition and lipid pathway proteins by geographic region in TB patients undergoing treatment for pulmonary tuberculosis, which appear to be associated with differential treatment responses. Future TB clinical trials should collect expanded measures of nutritional status and further evaluate the relationship between nutrition and microbiologic treatment response.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

Mapping Isoform Abundance and Interactome of the Endogenous TMPRSS2-ERG Fusion Protein by Orthogonal Immunoprecipitation-Mass Spectrometry Assays

TMPRSS2-ERG gene fusion, a molecular alteration found in nearly half of primary prostate cancer cases, has been intensively characterized at the transcript level. However limited studies have explored the molecular identity and function of the endogenous fusion at the protein level. Here, we developed immunoprecipitation-mass spectrometry assays for the measurement of a low-abundance T1E4 TMPRSS2-ERG fusion protein, its isoforms, and its interactome in VCaP prostate cancer cells. Our assays quantified total ERG (∼27,000 copies/cell) and its four unique isoforms and revealed that the T1E4-ERG isoform accounted for 52 ± 3% of the total ERG protein in VCaP cells, and 50 ± 11% in formalin-fixed paraffin-embedded prostate cancer tissues. For the first time, the N-terminal peptide (methionine-truncated and N-acetylated TASSSSDYGQTSK) unique for the T1/E4 fusion was identified. ERG interactome profiling with the C-terminal, but not the N-terminal, antibodies identified 29 proteins, including mutually exclusive BRG1- and BRM-associated canonical SWI/SNF chromatin remodeling complexes. Our sensitive and selective IP-SRM assays present alternative tools to quantify ERG and its isoforms in clinical samples, thus paving the way for development of more accurate diagnostics of prostate cancer.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

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.

Multiplex Immuno-Liquid Chromatography-Mass Spectrometry-Parallel Reaction Monitoring (LC-MS-PRM) Quantitation of CD8A, CD4, LAG3, PD1, PD-L1, and PD-L2 in Frozen Human Tissues

The immune status of tumors critically influences their responsiveness to PD1 blockades and other immune-based therapies. Programmed death ligand 1 (PD-L1) immunohistochemistry (IHC) is a clinically validated predictive biomarker of response to checkpoint-inhibitor therapy in a limited number of clinical settings but is poorly predictive in most. With emerging evidence that multiple pathways and immune-checkpoint proteins may coordinately contribute to the adaptive immune resistance, the identification and quantitation of multiple immune markers in tumor tissue could help identify the controlling pathways in a given patient, guide the selection of optimal therapy, and monitor response to treatment. We developed and validated a sensitive and robust immuno-liquid chromatography-parallel reaction monitoring assay to simultaneously quantify the expression levels of six immune markers (CD8A, CD4, LAG3, PD1, PD-L1, and PD-L2) using as little as 1-2 mg of fresh frozen tissue. The lower limit of quantitation ranged from 0.07 ng/mg protein for PD1 to 1.0 ng/mg protein for CD4. The intrabatch accuracy was within -16.6% to 15.0% for all proteins at all concentrations, and the variation ranged from 0.8% to 14.7%, while interbatch accuracy was within -6.3% to 8.6%, and the variation ranged from 1.3% to 12.8%. The validated assay was then applied to quantify all six biomarkers in different tissues and was confirmed to have sufficient sensitivity (0.07-1.00 ng/mg protein) and reproducibility (variation ranged from 4.3 to 12.0%). In an analysis of 26 cervical tumors, CD8A and CD4 were detected in all tumors, followed by PD-L1 in 85%, LAG-3 in 65%, PD1 in 50%, and PD-L2 in 35%. The strongest correlations were observed between CD8A and CD4 ( r = 0.88) and CD8A and LAG-3 ( r = 0.86). PD1 was not significantly correlated with any of the other proteins tested. This method can be applied to survey the immune signatures across tumor types and tailored to incorporate additional markers as needed.

Facile carrier-assisted targeted mass spectrometric approach for proteomic analysis of low numbers of mammalian cells

There is an unmet technical challenge for mass spectrometry (MS)-based proteomic analysis of single mammalian cells. Quantitative proteomic analysis of single cells has been previously achieved by antibody-based immunoassays but is limited by the availability of high-quality antibodies. Herein we report a facile targeted MS-based proteomics method, termed cPRISM-SRM (carrier-assisted high-pressure, high-resolution separations with intelligent selection and multiplexing coupled to selected reaction monitoring), for reliable analysis of low numbers of mammalian cells. The method capitalizes on using “carrier protein” to assist processing of low numbers of cells with minimal loss, high-resolution PRISM separation for target peptide enrichment, and sensitive SRM for protein quantification. We have demonstrated that cPRISM-SRM has sufficient sensitivity to quantify proteins expressed at ≥200,000 copies per cell at the single-cell level and ≥3000 copies per cell in 100 mammalian cells. We envision that with further improvement cPRISM-SRM has the potential to move toward targeted MS-based single-cell proteomics.

Tujin Shi et al. report a mass spectrometry-based proteomics approach, cPRISM-SRM, that allows for accurate quantification of proteins in small numbers of mammalian cells through the use of a carrier protein to prevent sample loss. The sensitivity of cPRISM-SRM allows for measurement of the 2500 most abundant proteins in a human cell.

A novel mouse model of human prostate cancer to study intraprostatic tumor growth and the development of lymph node metastases

BACKGROUND

In this study, we aimed to establish a versatile in vivo model of prostate cancer, which adequately mimics intraprostatic tumor growth, and the natural routes of metastatic spread. In addition, we analyzed the capability of high-resolution ultrasonography (hrUS), in vivo micro-CT (μCT), and 9.4T MRI to monitor tumor growth and the development of lymph node metastases.

METHODS

A total of 5 × 10⁵ VCaP cells or 5 × 10⁵ cells of LuCaP136- or LuCaP147 spheroids were injected into the prostate of male CB17-SCID mice (n = 8 for each cell type). During 12 weeks of follow-up, orthotopic tumor growth, and metastatic spread were monitored by repetitive serum-PSA measurements and imaging studies including hrUS, μCT, and 9.4T MRI. At autopsy, primary tumors and metastases were harvested and examined by histology and immunohistochemistry (CK5, CK8, AMACR, AR, Ki67, ERG, and PSA). From imaging results and PSA-measurements, tumor volume doubling time, tumor-specific growth rate, and PSA-density were calculated.

RESULTS

All 24 mice developed orthotopic tumors. The tumor growth could be reliably monitored by a combination of hrUS, μCT, MRI, and serum-PSA measurements. In most animals, lymph node metastases could be detected after 12 weeks, which could also be well visualized by hrUS, and MRI. Immunohistochemistry showed positive signals for CK8, AMACR, and AR in all xenograft types. CK5 was negative in VCaP- and focally positive in LuCaP136- and LuCaP147-xenografts. ERG was positive in VCaP- and negative in LuCaP136- and LuCaP147-xenografts. Tumor volume doubling times and tumor-specific growth rates were 21.2 days and 3.9 %/day for VCaP-, 27.6 days and 3.1 %/day for LuCaP136- and 16.2 days and 4.5 %/day for LuCaP147-xenografts, respectively. PSA-densities were 433.9 ng/mL per milliliter tumor for VCaP-, 6.5 ng/mL per milliliter tumor for LuCaP136-, and 11.2 ng/mL per milliliter tumor for LuCaP147-xenografts.

CONCLUSIONS

By using different monolayer and 3D spheroid cell cultures in an orthotopic xenograft model, we established an innovative, versatile in vivo model of prostate cancer, which enables the study of both intraprostatic tumor growth as well as metastatic spread to regional lymph nodes. HrUS and MRI are feasible tools to monitor tumor growth and the development of lymph node metastases while these cannot be visualized by μCT.

Multiplexed targeted mass spectrometry assays for prostate cancer-associated urinary proteins

Biomarkers for effective early diagnosis and prognosis of prostate cancer are still lacking. Multiplexed assays for cancer-associated proteins could be useful for identifying biomarkers for cancer detection and stratification. Herein, we report the development of sensitive targeted mass spectrometry assays for simultaneous quantification of 10 prostate cancer-associated proteins in urine. The diagnostic utility of these markers was evaluated with an initial cohort of 20 clinical urine samples. Individual marker concentration was normalized against the measured urinary prostate-specific antigen level as a reference of prostate-specific secretion. The areas under the receiver-operating characteristic curves for the 10 proteins ranged from 0.75 for CXL14 to 0.87 for CEAM5. Furthermore, MMP9 level was found to be significantly higher in patients with high Gleason scores, suggesting a potential of MMP9 as a marker for risk level assessment. Taken together, our work illustrated the feasibility of accurate multiplexed measurements of low-abundance cancer-associated proteins in urine and provided a viable path forward for preclinical verification of candidate biomarkers for prostate cancer.

The oncogenic transcription factor ERG represses the transcription of the tumour suppressor gene PTEN in prostate cancer cells

The oncogene ETS-related gene (ERG) encodes a transcription factor with roles in the regulation of haematopoiesis, angiogenesis, vasculogenesis, inflammation, migration and invasion. The ERG oncogene is activated in >50% of prostate cancer cases, generally through a gene fusion with the androgen-responsive promoter of transmembrane protease serine 2. Phosphatase and tensin homologue (PTEN) is an important tumour suppressor gene that is often inactivated in cancer. ERG overexpression combined with PTEN inactivation or loss is often associated with aggressive prostate cancer. The present study aimed to determine whether or not ERG regulates PTEN transcription directly. ERG was demonstrated to bind to the PTEN promoter and repress its transcription. ERG overexpression reduced endogenous PTEN expression, whereas ERG knockdown increased PTEN expression. The ability of ERG to repress PTEN may contribute to a more cancer-permissive environment.

Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues

Mass spectrometry-based targeted proteomics (e.g., selected reaction monitoring, SRM) is emerging as an attractive alternative to immunoassays for protein quantification. Recently we have made significant progress in SRM sensitivity for enabling quantification of low nanograms per milliliter to sub-naograms per milliliter level proteins in nondepleted human blood plasma/serum without affinity enrichment. However, precise quantification of extremely low abundance proteins (e.g., ≤ 100 pg/mL in blood plasma/serum) using targeted proteomics approaches still remains challenging, especially for these samples without available antibodies for enrichment. To address this need, we have developed an antibody-independent deep-dive SRM (DD-SRM) approach that capitalizes on multidimensional high-resolution reversed-phase liquid chromatography (LC) separation for target peptide separation and enrichment combined with precise selection of target peptide fractions of interest, significantly improving SRM sensitivity by ∼5 orders of magnitude when compared to conventional LC-SRM. Application of DD-SRM to human serum and tissue provides precise quantification of endogenous proteins at the ∼10 pg/mL level in nondepleted serum and at <10 copies per cell level in tissue. Thus, DD-SRM holds great promise for precisely measuring extremely low abundance proteins or protein modifications, especially when high-quality antibodies are not available.

Advances in targeted proteomics and applications to biomedical research

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

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.

Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps

The concept of personalized medicine includes novel protein biomarkers that are expected to improve the early detection, diagnosis and therapy monitoring of malignant diseases. Tissues, biofluids, cell lines and xenograft models are the common sources of biomarker candidates that require verification of clinical value in independent patient cohorts. Targeted proteomics - based on selected reaction monitoring, or data extraction from data-independent acquisition based digital maps - now represents a promising mass spectrometry alternative to immunochemical methods. To date, it has been successfully used in a high number of studies answering clinical questions on solid malignancies: breast, colorectal, prostate, ovarian, endometrial, pancreatic, hepatocellular, lung, bladder and others. It plays an important role in functional proteomic experiments that include studying the role of post-translational modifications in cancer progression. This review summarizes verified biomarker candidates successfully quantified by targeted proteomics in this field and directs the readers who plan to design their own hypothesis-driven experiments to appropriate sources of methods and knowledge.

Applications of targeted proteomics in systems biology and translational medicine

Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM-MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.

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-independent targeted quantification of TMPRSS2-ERG fusion protein products in prostate cancer

Fusions between the transmembrane protease serine 2 (TMPRSS2) and ETS related gene (ERG) represent one of the most specific biomarkers that define a distinct molecular subtype of prostate cancer. Studies of TMPRSS2-ERG gene fusions have seldom been performed at the protein level, primarily due to the lack of high-quality antibodies suitable for quantitative studies. Herein, we applied a recently developed PRISM (high-pressure high-resolution separations with intelligent selection and multiplexing)-SRM (selected reaction monitoring) strategy for quantifying ERG protein in prostate cancer cell lines and tumors. The highly sensitive PRISM-SRM assays provided confident detection of 6 unique ERG peptides in both TMPRSS2-ERG positive cell lines and tissues, but not in cell lines or tissues lacking the TMPRSS2-ERG rearrangement, clearly indicating that ERG protein expression is significantly increased in the presence of the TMPRSS2-ERG gene fusion. Significantly, our results provide evidence that two distinct ERG protein isoforms are simultaneously expressed in TMPRSS2-ERG positive samples as evidenced by the concomitant detection of two mutually exclusive peptides in two patient tumors and in the VCaP prostate cancer cell line. Three peptides, shared across almost all fusion protein products, were determined to be the most abundant peptides, providing "signature" peptides for detection of ERG over-expression resulting from TMPRSS2-ERG gene fusion. The PRISM-SRM assays provide valuable tools for studying TMPRSS2-ERG gene fusion protein products in prostate cancer.