The urinary metabolic profile of diethylene glycol methyl ether and triethylene glycol methyl ether in Sprague-Dawley rats and the role of the metabolite methoxyacetic acid in their toxicity.

Ethylene glycol ethers are a well-known series of solvents and hydraulic fluids derived from the reaction of ethylene oxide and monoalcohols. Use of methanol as the alcohol results in a series of mono, di and triethylene glycol methyl ethers. The first in the series, monoethylene glycol methyl ether (EGME or 2-methoxyethanol) is well characterised and metabolises in vivo to methoxyacetic acid (MAA), a known reproductive toxicant. Metabolism data is not available for the di and triethylene glycol ethers (DEGME and TEGME respectively). This study evaluated the metabolism of these two substances in male rats following single oral gavage doses of 500, 1000 and 2000 mg/kg for DEGME and 1000 mg/kg for TEGME. As for EGME, the dominant metabolite of each was the acid metabolite derived by oxidation of the terminal hydroxyl group. Elimination of these metabolites was rapid, with half-lives <4 h for each one. Both substances were also found to produce small amounts of MAA (~0.5% for TEGME and ~1.1% for DEGME at doses of 1000 mg/kg) through cleavage of the ether groups in the molecules. These small amounts of MAA produced can explain the effects seen at high doses in reproductive studies using DEGME and TEGME.

Proteomic characterization of microdissected breast tissue environment provides a protein-level overview of malignant transformation.

Both healthy and cancerous breast tissue is heterogeneous, which is a bottleneck for proteomics-based biomarker analysis, as it obscures the cellular origin of a measured protein. We therefore aimed at obtaining a protein-level interpretation of malignant transformation through global proteome analysis of a variety of laser capture microdissected cells originating from benign and malignant breast tissues. We compared proteomic differences between these tissues, both from cells of epithelial origin and the stromal environment, and performed string analysis. Differences in protein abundances corresponded with several hallmarks of cancer, including loss of cell adhesion, transformation to a migratory phenotype, and enhanced energy metabolism. Furthermore, despite enriching for (tumor) epithelial cells, many changes to the extracellular matrix were detected in microdissected cells of epithelial origin. The stromal compartment was heterogeneous and richer in the number of fibroblast and immune cells in malignant sections, compared to benign tissue sections. Furthermore, stroma could be clearly divided into reactive and nonreactive based on extracellular matrix disassembly proteins. We conclude that proteomics analysis of both microdissected epithelium and stroma gives an additional layer of information and more detailed insight into malignant transformation.

The advantage of laser-capture microdissection over whole tissue analysis in proteomic profiling studies.

Laser-capture microdissection (LCM) offers a reliable cell population enrichment tool and has been successfully coupled to MS analysis. Despite this, most proteomic studies employ whole tissue lysate (WTL) analysis in the discovery of disease biomarkers and in profiling analyses. Furthermore, the influence of tissue heterogeneity in WTL analysis, nor its impact in biomarker discovery studies have been completely elucidated. In order to address this, we compared previously obtained high resolution MS data from a cohort of 38 breast cancer tissues, of which both LCM enriched tumor epithelial cells and WTL samples were analyzed. Label-free quantification (LFQ) analysis through MaxQuant software showed a significantly higher number of identified and quantified proteins in LCM enriched samples (3404) compared to WTLs (2837). Furthermore, WTL samples displayed a higher amount of missing data compared to LCM both at peptide and protein levels (p-value < 0.001). 2D analysis on co-expressed proteins revealed discrepant expression of immune system and lipid metabolisms related proteins between LCM and WTL samples. We hereby show that LCM better dissected the biology of breast tumor epithelial cells, possibly due to lower interference from surrounding tissues and highly abundant proteins. All data have been deposited in the ProteomeXchange with the dataset identifier PXD002381 (http://proteomecentral.proteomexchange.org/dataset/PXD002381).

Targeted MS Assay Predicting Tamoxifen Resistance in Estrogen-Receptor-Positive Breast Cancer Tissues and Sera.

We recently reported on the development of a 4-protein-based classifier (PDCD4, CGN, G3BP2, and OCIAD1) capable of predicting outcome to tamoxifen treatment in recurrent, estrogen-receptor-positive breast cancer based on high-resolution MS data. A precise and high-throughput assay to measure these proteins in a multiplexed, targeted fashion would be favorable to measure large numbers of patient samples to move these findings toward a clinical setting. By coupling immunoprecipitation to multiple reaction monitoring (MRM) MS and stable isotope dilution, we developed a high-precision assay to measure the 4-protein signature in 38 primary breast cancer whole tissue lysates (WTLs). Furthermore, we evaluated the presence and patient stratification capabilities of our signature in an independent set of 24 matched (pre- and post-therapy) sera. We compared the performance of immuno-MRM (iMRM) with direct MRM in the absence of fractionation and shotgun proteomics in combination with label-free quantification (LFQ) on both WTL and laser capture microdissected (LCM) tissues. Measurement of the 4-proteins by iMRM showed not only higher accuracy in measuring proteotypic peptides (Spearman r: 0.74 to 0.93) when compared with MRM (Spearman r: 0.0 to 0.76) but also significantly discriminated patient groups based on treatment outcome (hazard ratio [HR]: 10.96; 95% confidence interval [CI]: 4.33 to 27.76; Log-rank P < 0.001) when compared with LCM (HR: 2.85; 95% CI: 1.24 to 6.54; Log-rank P = 0.013) and WTL (HR: 1.16; 95% CI: 0.57 to 2.33; Log-rank P = 0.680) LFQ-based predictors. Serum sample analysis by iMRM confirmed the detection of the four proteins in these samples. We hereby report that iMRM outperformed regular MRM, confirmed our previous high-resolution MS results in tumor tissues, and has shown that the 4-protein signature is measurable in serum samples.

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker.

Shotgun proteomics on tissue specimens extracted with Acid guanidinium-thiocyanate-phenol-chloroform.

Protein-containing organic fractions of acid guanidinium thiocyanate-phenol-chloroform-extracted tissues are an interesting source of proteins as this method is widely used for RNA extraction for gene expression analysis. However, due to difficulties in redissolving pelleted proteins from the organic phase, protein analysis has only been limitedly reported. Current shotgun mass spectrometry-based methods, however, require minute amounts of sample, and methods have been developed that allow SDS to be removed from an extraction buffer prior to protein digestion. The limited volume of starting material needed for shotgun proteomics facilitates redissolving proteins in SDS-containing buffers, allowing proteins to be readily extracted. Here we describe a protocol for an SDS-DTT-based extraction of proteins from the organic fraction of acid guanidinium-thiocyanate-phenol-chloroform-extracted tissues that remain after RNA isolation for shotgun MS analysis.

Integrative analysis of genomics and proteomics data on clinical breast cancer tissue specimens extracted with acid guanidinium thiocyanate-phenol-chloroform.

Acid guanidinium thiocyanate, phenol, and chloroform extraction (AGPC) is a commonly used procedure to extract RNA from fresh frozen tissues and cell lines. In addition, DNA and proteins can be recovered, which makes AGPC an attractive source for integrative analysis on tissues of which little material is available, such as clinical specimens. Despite this potential, AGPC has only scarcely been used for proteomic analysis, mainly due to difficulties in extracting proteins. We have used a quantitative mass spectrometry method to show that proteins can readily be recovered from AGPC extracted tissues with high recovery and repeatability, which allows this method to be used for global proteomic profiling. Protein expression data for a selected number of clinically relevant markers, of which transcript and protein levels are known to be correlated, were in agreement with genomic and transcriptomic data obtained from the same AGPC lysate. Furthermore, global proteomic profiling successfully discriminated breast tumor tissues according to their clinical subtype. Lastly, a reference gene set of differentially expressed transcripts was strongly enriched in the differentially abundant proteins in our cohort. AGPC lysates are therefore well suited for comparative protein and integrative analyses.

Global proteomic characterization of microdissected estrogen receptor positive breast tumors.

We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS.

Comparative proteome analysis revealing an 11-protein signature for aggressive triple-negative breast cancer.

BACKGROUND: Clinical outcome of patients with triple-negative breast cancer (TNBC) is highly variable. This study aims to identify and validate a prognostic protein signature for TNBC patients to reduce unnecessary adjuvant systemic therapy. METHODS: Frozen primary tumors were collected from 126 lymph node-negative and adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling in two series: an in-house training (n = 63) and a multicenter test (n = 63) set. Patients who remained free of distant metastasis for a minimum of 5 years after surgery were defined as having good prognosis. Cox regression analysis was performed to develop a prognostic signature, which was independently validated. All statistical tests were two-sided. RESULTS: An 11-protein signature was developed in the training set (median follow-up for good-prognosis patients = 117 months) and subsequently validated in the test set (median follow-up for good-prognosis patients = 108 months) showing 89.5% sensitivity (95% confidence interval [CI] = 69.2% to 98.1%), 70.5% specificity (95% CI = 61.7% to 74.2%), 56.7% positive predictive value (95% CI = 43.8% to 62.1%), and 93.9% negative predictive value (95% CI = 82.3% to 98.9%) for poor-prognosis patients. The predicted poor-prognosis patients had higher risk to develop distant metastasis than the predicted good-prognosis patients in univariate (hazard ratio [HR] = 13.15; 95% CI = 3.03 to 57.07; P = .001) and multivariable (HR = 12.45; 95% CI = 2.67 to 58.11; P = .001) analysis. Furthermore, the predicted poor-prognosis group had statistically significantly more breast cancer-specific mortality. Using our signature as guidance, more than 60% of patients would have been exempted from unnecessary adjuvant chemotherapy compared with conventional prognostic guidelines. CONCLUSIONS: We report the first validated proteomic signature to assess the natural course of clinical TNBC.

Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue.

Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ∼10,000 peptides corresponding to ∼1,800 proteins from sub-microgram amounts of protein extracted from ∼4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor α positive or negative (ER+/-) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers.

Optimized nLC-MS workflow for laser capture microdissected breast cancer tissue.

Reliable sample preparation is of utmost importance for comparative proteome analysis, particularly when investigating minute amounts of clinical specimens, such as laser capture microdissected tumor tissue. In this study, we present an optimized nanoLC-MS workflow specifically for the analysis of laser capture microdissected breast cancer tissue. Analytical performance of different laser capture microdissection (LCM) functions available on the PALM system, time dependent trypsin digestion efficiency, effect of sample preparation and digestion time on peptide modification, semi-tryptic peptides and missed cleavages were evaluated. Our results show that microdissection from uncoated glass slides results in protein degradation; that protease and phosphatase inhibitors do not result in detectable improvement in number of peptides or semi-tryptic peptides; and that digestion time longer than four hours drastically reduces the number of missed cleavages, but also increases the number of unexpectedly modified peptides. Overalkylation was the most dominant side-reaction, which significantly increased overnight (P=0.05). The latter effect could almost completely be reverted by the use of a quenching agent (P=0.001). Taken together, our results show that it is of importance to carefully control sample handling steps so that reliable protein identification and quantitation can be performed within comparative proteomics studies using LCM. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Laser capture microdissection applications in breast cancer proteomics.

Breast cancer tissues are characterized by cellular heterogeneity, representing a mixture of, e.g., healthy epithelial ducts, invasive or in situ tumor cells, surrounding stroma, infiltrating immune cells, blood vessels, and capillaries. As a consequence, protein extracts from whole tissue lysates also represent a variety of cell types present in the tissues under examination. This, however, seriously hampers the analysis of tumor cell-specific signals, which is of interest when performing biomarker discovery-type of studies. Therefore, laser capture microdissection is a perfect tool to isolate a relatively pure population of cells of interest, such as tumor cells. In this chapter, we describe the use of the PALM MicroBeam system for laser microdissection and pressure catapulting. Protocols are provided for sectioning, staining, microdissection, sample preparation, and mass spectrometric analysis of snap frozen breast cancer tissue.