Systematic discovery of ectopic pregnancy serum biomarkers using 3-D protein profiling coupled with label-free quantitation

Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy

OBJECTIVE

To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies.

DESIGN

A nested case-control design evaluated the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location.

SETTING

Three university hospitals.

PATIENTS

A total of 218 individuals with pain and/or bleeding in early pregnancy: 75 had an ongoing intrauterine gestation; 68 had ectopic pregnancies (EPs); and 75 had miscarriages.

INTERVENTIONS

Serum levels of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for the identification of a nonviable pregnancy (ongoing intrauterine pregnancy vs. miscarriage or EP) and an EP (EP vs. ongoing intrauterine pregnancy or miscarriage).

MAIN OUTCOME MEASURES

The predicted classification using each model was compared with the actual diagnosis, and sensitivity, specificity, positive predictive value, negative predictive value, conclusive classification, and accuracy were calculated.

RESULTS

Models using classification regression tree analysis using 3 (pregnancy-specific beta-1-glycoprotein 3 [PSG3], chorionic gonadotropin-alpha subunit, and pregnancy-associated plasma protein-A) biomarkers were able to predict a maximum sensitivity of 93.3% and a maximum specificity of 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of 3 (soluble fms-like tyrosine kinase-1, PSG3, and tissue factor pathway inhibitor 2) biomarkers achieved a maximum sensitivity of 98.5% and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously, the conclusive classification increased to 72.7% with an accuracy of 95.9%. The predictive ability of the biomarkers in the random forest produced similar test characteristics when using 11 predictive biomarkers.

CONCLUSION

We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers choriogonadotropin alpha, pregnancy-associated plasma protein-A, and PSG3 can be used to predict viability, and soluble fms-like tyrosine kinase-1, tissue factor pathway inhibitor 2, and PSG3 can be used to predict pregnancy location.

Evaluation of novel biomarkers for early pregnancy outcome prediction†

OBJECTIVE

To assess performance and discriminatory capacity of commercially available enzyme-linked immunosorbent assays of biomarkers for predicting first trimester pregnancy outcome in a multi-center cohort.

DESIGN

In a case-control study at three academic centers of women with pain and bleeding in early pregnancy, enzyme-linked immunosorbent assays of biomarkers were screened for assay performance. Performance was assessed via functional sensitivity, assay reportable range, recovery/linearity, and intra-assay precision (%Coefficient of Variation). Top candidates were analyzed for discriminatory capacity for viability and location among 210 women with tubal ectopic pregnancy, viable intrauterine pregnancy, or miscarriage. Assay discrimination was assessed by visual plots, area under the curve with 95% confidence intervals, and measures of central tendency with two-sample t-tests.

RESULTS

Of 25 biomarkers evaluated, 22 demonstrated good or acceptable assay performance. Transgelin-2, oviductal glycoprotein, and integrin-linked kinase were rejected due to poor performance. The best biomarkers for discrimination of pregnancy location were pregnancy-specific beta-1-glycoprotein 9, pregnancy-specific beta-1-glycoprotein 1, insulin-like growth factor binding protein 1, kisspeptin (KISS1), pregnancy-specific beta-1-glycoprotein 3, and beta parvin (PARVB). The best biomarkers for discrimination of pregnancy viability were pregnancy-specific beta-1-glycoprotein 9, pregnancy-specific beta-1-glycoprotein 3, EH domain-containing protein 3, KISS1, WAP four-disulfide core domain protein 2 (HE4), quiescin sulfhydryl oxidase 2, and pregnancy-specific beta-1-glycoprotein 1.

CONCLUSION

Performance of commercially available enzyme-linked immunosorbent assays was acceptable for a panel of novel biomarkers to predict early pregnancy outcome. Of these, six and seven candidates demonstrated good discriminatory capacity of pregnancy location and viability, respectively, when validated in a distinct external population. Four markers demonstrated good discrimination for both location and viability.

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

BACKGROUND

Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.

METHODS

Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.

RESULTS

A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.

CONCLUSIONS

Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

EPEK: Creation and analysis of an Ectopic Pregnancy Expression Knowledgebase

Ectopic pregnancy (EP) is one of the leading causes of maternal mortality, where the fertilized embryo grows outside of the uterus. Recent experiments on mice have uncovered the importance of genetic factors in the transport of embryos inside the uterus. In the past, efforts have been made to identify possible gene or protein markers in EP in humans through multiple expression studies. Although there exist comprehensive gene resources for other maternal health disorders, there is no specific resource that compiles the genes associated with EP from such expression studies. Here, we address that knowledge gap by creating a computational resource, Ectopic Pregnancy Expression Knowledgebase (EPEK), that involves manual compilation and curation of expression profiles of EP in humans from published articles. In EPEK, we compiled information on 314 differentially expressed genes, 17 metabolites, and 3 SNPs associated with EP. Computational analyses on the gene set from EPEK showed the implication of cellular signaling processes in EP. We also identified possible exosome markers that could be clinically relevant in the diagnosis of EP. In a nutshell, EPEK is the first and only dedicated resource on the expression profile of EP in humans. EPEK is accessible at https://cb.imsc.res.in/epek.

Validation of a multiple marker test for early pregnancy outcome prediction

PURPOSE

To validate the use of a multiple biomarker test panel for predicting first trimester pregnancy outcome in a multi-center cohort.

METHODS

A case-control study of women presenting with pain and bleeding in early pregnancy at 5-10 weeks gestational age was performed at three academic centers. Sera from women with ectopic pregnancy (EP), viable intrauterine pregnancy (IUP), and miscarriage (SAB) were analyzed via immunoassay for Activin A (AA), Progesterone (P4), A Disintegrin And Metalloprotease-12 (ADAM12), pregnancy-associated plasma protein A (PAPP-A), glycodelin (Glyc), and human chorionic gonadotropin (hCG). Biomarkers were assessed for reproducibility using medians, ranges, standard deviations, and area under receiver-operating characteristic curve (AUC) and accuracy in early pregnancy outcome classification compared to a previous derivation population.

RESULTS

In 192 pregnancies, the biomarkers demonstrated good reproducibility with similar medians, ranges, and AUCs when compared to the derivation population except glycodelin. Pregnancy location was conclusively classified in 53% (n = 94) of the whole study sample with 78% accuracy. Pregnancy viability was conclusively classified in 58% (n = 112) of the new sample with 89% accuracy. Results were similar with subsequent model revisions where glycodelin was excluded and in the subgroups of subjects with a hCG below 2000 mIU/mL and a gestational age less than 6 weeks.

CONCLUSION

The use of a panel of biomarkers to maximize test accuracy of a prediction of pregnancy location and prediction of pregnancy viability was reproducible and validated in an external population from which it was derived, but clinical utility is limited based on the test characteristics obtained.

Targeting UGCG Overcomes Resistance to Lysosomal Autophagy Inhibition

Lysosomal autophagy inhibition (LAI) with hydroxychloroquine or DC661 can enhance cancer therapy, but tumor regrowth is common. To elucidate LAI resistance, proteomics and immunoblotting demonstrated that LAI induced lipid metabolism enzymes in multiple cancer cell lines. Lipidomics showed that LAI increased cholesterol, sphingolipids, and glycosphingolipids. These changes were associated with striking levels of GM1+ membrane microdomains (GMM) in plasma membranes and lysosomes. Inhibition of cholesterol/sphingolipid metabolism proteins enhanced LAI cytotoxicity. Targeting UDP-glucose ceramide glucosyltransferase (UGCG) synergistically augmented LAI cytotoxicity. Although UGCG inhibition decreased LAI-induced GMM and augmented cell death, UGCG overexpression led to LAI resistance. Melanoma patients with high UGCG expression had significantly shorter disease-specific survival. The FDA-approved UGCG inhibitor eliglustat combined with LAI significantly inhibited tumor growth and improved survival in syngeneic tumors and a therapy-resistant patient-derived xenograft. These findings nominate UGCG as a new cancer target, and clinical trials testing UGCG inhibition in combination with LAI are warranted.

SIGNIFICANCE

We discovered UGCG-dependent lipid remodeling drives resistance to LAI. Targeting UGCG with a drug approved for a lysosomal storage disorder enhanced LAI antitumor activity without toxicity. LAI and UGCG inhibition could be tested clinically in multiple cancers. This article is highlighted in the In This Issue feature, p. 247.

Separation of Serum and Plasma Proteins for In-Depth Proteomic Analysis

There are probably no biological samples that did more to spur interest in proteomics than serum and plasma. The belief was that comparing the proteomes of these samples obtained from healthy and disease-affected individuals would lead to biomarkers that could be used to diagnose conditions such as cancer. While the continuing development of mass spectrometers with greater sensitivity and resolution has been invaluable, the invention of strategic strategies to separate circulatory proteins has been just as critical. Novel and creative separation techniques were required because serum and plasma probably have the greatest dynamic range of protein concentration of any biological sample. The concentrations of circulating proteins can range over twelve orders of magnitude, making it a challenge to identify low-abundance proteins where the bulk of the useful biomarkers are believed to exist. The major goals of this article are to (i) provide an historical perspective on the rapid development of serum and plasma proteomics; (ii) describe various separation techniques that have made obtaining an in-depth view of the proteome of these biological samples possible; and (iii) describe applications where serum and plasma proteomics have been employed to discover potential biomarkers for pathological conditions.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Comparative interactomes of HSF1 in stress and disease reveal a role for CTCF in HSF1-mediated gene regulation

Heat shock transcription factor 1 (HSF1) orchestrates cellular stress protection by activating or repressing gene transcription in response to protein misfolding, oncogenic cell proliferation, and other environmental stresses. HSF1 is tightly regulated via intramolecular repressive interactions, post-translational modifications, and protein-protein interactions. How these HSF1 regulatory protein interactions are altered in response to acute and chronic stress is largely unknown. To elucidate the profile of HSF1 protein interactions under normal growth and chronic and acutely stressful conditions, quantitative proteomics studies identified interacting proteins in the response to heat shock or in the presence of a poly-glutamine aggregation protein cell-based model of Huntington's disease. These studies identified distinct protein interaction partners of HSF1 as well as changes in the magnitude of shared interactions as a function of each stressful condition. Several novel HSF1-interacting proteins were identified that encompass a wide variety of cellular functions, including roles in DNA repair, mRNA processing, and regulation of RNA polymerase II. One HSF1 partner, CTCF, interacted with HSF1 in a stress-inducible manner and functions in repression of specific HSF1 target genes. Understanding how HSF1 regulates gene repression is a crucial question, given the dysregulation of HSF1 target genes in both cancer and neurodegeneration. These studies expand our understanding of HSF1-mediated gene repression and provide key insights into HSF1 regulation via protein-protein interactions.

Metabolomic identification of novel diagnostic biomarkers in ectopic pregnancy

INTRODUCTION

Ectopic pregnancy (EP) is a potentially life-threatening condition and early diagnosis still remains a challenge, causing a delay in management leading to tubal rupture.

OBJECTIVES

To identify putative plasma biomarkers for the detection of tubal EP and elucidate altered biochemical pathways in EP compared to intrauterine pregnancies.

METHODS

This case-control study included prospective recruitment of 39 tubal EP cases and 89 early intrauterine pregnancy controls. Plasma samples were biochemically profiled using proton nuclear magnetic resonance spectroscopy (¹H NMR). To avoid over-fitting, datasets were randomly divided into a discovery group (26 cases vs 60 controls) and a test group (13 cases and 29 controls). Logistic regression models were developed in the discovery group and validated in the independent test group. Area under the receiver operating characteristics curve (AUC), 95% confidence interval (CI), sensitivity, and specificity values were calculated.

RESULTS

In total 13 of 43 (30.3%) metabolite concentrations were significantly altered in EP plasma (p < 0.05). Metabolomic profiling yielded significant separation between EP and controls (p < 0.05). Independent validation of a two-metabolite model consisting of lactate and acetate, achieved an AUC (95% CI) = 0.935 (0.843-1.000) with a sensitivity of 92.3% and specificity of 96.6%. The second metabolite model (D-glucose, pyruvate, acetoacetate) performed well with an AUC (95% CI) = 0.822 (0.657-0.988) and a sensitivity of 84.6% and specificity of 86.2%.

CONCLUSION

We report novel metabolomic biomarkers with a high accuracy for the detection of EP. Accurate biomarkers could potentially result in improved early diagnosis of tubal EP cases.

Association of Low Maternal Pregnancy-associated Plasma Protein A with Adverse Perinatal Outcome

The aim is to provide an overall view of the association of low pregnancy-associated plasma protein A (PAPP-A) levels with adverse perinatal outcomes. The available literature in PubMed/Medline regarding PAPP-A and adverse pregnancy outcomes was searched for related articles, including terms such as “PAPP-A,” “intrauterine growth restriction (IUGR),” “small for gestational age (SGA),” “stillbirth,” “adverse outcome,” and others. The fifth percentile is supported by many recent studies to be PAPP-A’s cutoff for adverse outcome detection and the increased risk seems to be extremely high below 0.2 PAPP-A MoM (multiple of the median). Apart from chromosomal abnormalities, preeclampsia, intrauterine fetal demise, and pregnancy loss have been associated with maternal serum PAPP-A. For results below the first centile, PAPP-A has a strong positive predictive value for SGA and IUGR. Except for its vital role on the cleavage of insulin-like growth factor binding proteins (IGFBP), PAPP-A has proven to be a reliable marker for prenatal screening. Even though PAPP-A as a single predictor proved to be valuable for the prediction of some adverse perinatal outcomes, in some cases, a combination of PAPP-A to other maternal serum markers led to an increase in detection rates. PAPP-A is a promising maternal serum marker for pregnancy outcome prediction with more studies needed in order for its potentials to be fully understood and exploited.

Proteome Analysis Using Gel-LC-MS/MS

This article describes processing of protein samples using 1D SDS gels prior to protease digestion for proteomics workflows that subsequently utilize reversed-phase nanocapillary ultra-high-pressure liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The resulting LC-MS/MS data are used to identify peptides and thereby infer proteins present in samples ranging from simple mixtures to very complex proteomes. Bottom-up proteome studies usually involve quantitative comparisons across several or many samples. For either situation, 1D SDS gels represent a simple, widely available technique that can be used to either fractionate complex proteomes or rapidly clean up low microgram samples with minimal losses. After gel separation and staining/destaining, appropriate gel slices are excised, and in-gel reduction, alkylation, and protease digestion are performed. Digests are then processed for LC-MS/MS analysis. Protocols are described for either sample fractionation with high-throughput processing of many samples or simple cleanup without fractionation. An optional strategy is to conduct in-solution reduction and alkylation prior to running gels, which is advantageous when a large number of samples will be separated into large numbers of fractions. Optimization of trypsin digestion parameters and comparison to in-solution protease digestion are also described. © 2019 by John Wiley & Sons, Inc.

The Notch Pathway in Breast Cancer Progression

Objective

Notch signaling pathway is a vital parameter of the mammalian vascular system. In this review, the authors summarize the current knowledge about the impact of the Notch signaling pathway in breast cancer progression and the therapeutic role of Notch's inhibition.

Methods

The available literature in MEDLINE, PubMed, and Scopus, regarding the role of the Notch pathway in breast cancer progression was searched for related articles from about 1973 to 2017 including terms such as “Notch,” “Breast Cancer,” and “Angiogenesis.” Results. Notch signaling controls the differentiation of breast epithelial cells during normal development. Studies confirm that the Notch pathway has a major participation in breast cancer progression through overexpression and/or abnormal genetic type expression of the notch receptors and ligands that determine angiogenesis. The cross-talk of Notch and estrogens, the effect of Notch in breast cancer stem cells formation, and the dependable Notch overexpression during breast tumorigenesis have been studied enough and undoubtedly linked to breast cancer development. The already applied therapeutic inhibition of Notch for breast cancer can drastically change the course of the disease.

Conclusion

Current data prove that Notch pathway has a major participation and multiple roles during breast tumor progression. Inhibition of Notch receptors and ligands provides innovative therapeutic results and could become the therapy of choice in the next few years, even though further research is needed to reach safe conclusions.

Developing a deeper insight into reproductive biomarkers

The development of biomarkers of reproductive medicine is still in its infancy because many black boxes are still present in reproductive medicine. Novel approaches to human infertility diagnostics and treatment must be developed because reproductive medicine has lagged behind in the implementation of biomarkers in clinical medicine. Despite the dearth of the available literature, the current rapid pace of publications suggests that this gap will soon be filled therefore; this review is a précis of the research that has been done so far and will provide a basis for the development of biomarkers in reproductive medicine.

Stromal cyclin D1 promotes heterotypic immune signaling and breast cancer growth

The cyclin D1 gene encodes the regulatory subunit of a holoenzyme that drives cell autonomous cell cycle progression and proliferation. Herein we show cyclin D1 abundance is increased >30-fold in the stromal fibroblasts of patients with invasive breast cancer, associated with poor outcome. Cyclin D1 transformed hTERT human fibroblast to a cancer-associated fibroblast phenotype. Stromal fibroblast expression of cyclin D1 (cyclin D1^Stroma) in vivo, enhanced breast epithelial cancer tumor growth, restrained apoptosis, and increased autophagy. Cyclin D1^Stroma had profound effects on the breast tumor microenvironment increasing the recruitment of F4/80⁺ and CD11b⁺ macrophages and increasing angiogenesis. Cyclin D1^Stroma induced secretion of factors that promoted expansion of stem cells (breast stem-like cells, embryonic stem cells and bone marrow derived stem cells). Cyclin D1^Stroma resulted in increased secretion of proinflammatory cytokines (CCL2, CCL7, CCL11, CXCL1, CXCL5, CXCL9, CXCL12), CSF (CSF1, GM-CSF1) and osteopontin (OPN) (30-fold). OPN was induced by cyclin D1 in fibroblasts, breast epithelial cells and in the murine transgenic mammary gland and OPN was sufficient to induce stem cell expansion. These results demonstrate that cyclin D1^Stroma drives tumor microenvironment heterocellular signaling, promoting several key hallmarks of cancer.

Efficient Quantitative Comparisons of Plasma Proteomes Using Label-Free Analysis with MaxQuant

Mass spectrometry (MS)-based quantitation of plasma proteomes is challenging due to the extremely wide dynamic range and molecular heterogeneity of plasma samples. However, recent advances in technology, MS instrumentation, and bioinformatics have enabled in-depth quantitative analyses of very complex proteomes, including plasma. Specifically, recent improvements in both label-based and label-free quantitation strategies have allowed highly accurate quantitative comparisons of expansive proteome datasets. Here we present a method for in-depth label-free analysis of human plasma samples using MaxQuant.

Predicting first trimester pregnancy outcome: derivation of a multiple marker test

OBJECTIVE

To predict first trimester pregnancy outcome using biomarkers in a multicenter cohort.

DESIGN

Case-control study.

SETTING

Three academic centers.

PATIENT(S)

Women with pain and bleeding in early pregnancy.

INTERVENTION(S)

Sera from women who were 5-12 weeks' gestational age with ectopic pregnancy (EP), viable intrauterine pregnancy (IUP), and miscarriage/spontaneous abortion (SAB) was analyzed by ELISA and immunoassay for activin A, inhibin A, P, A Disintegrin And Metalloprotease-12, pregnancy-associated plasma protein A (PAPP-A), pregnancy specific B1-glycoprotein (SP1), placental-like growth factor, vascular endothelial growth factor, glycodelin (Glyc), and hCG. Classification trees were developed to optimize sensitivity/specificity for pregnancy location and viability.

MAIN OUTCOME MEASURE(S)

Area under receiver operating characteristic curve, sensitivity, specificity, and accuracy of first trimester pregnancy outcome.

RESULT(S)

In 230 pregnancies, the combination of trees to maximize sensitivity and specificity resulted in 73% specificity (95% confidence interval (CI) 0.65-0.80) and 31% sensitivity (95% CI 0.21-0.43) for viability. Similar methods had 21% sensitivity (95% CI 0.12-0.32) and 33% specificity (95% CI 0.26-0.41) for location. Activin A, Glyc, and A Disintegrin And Metalloprotease-12 definitively classified pregnancy location in 29% of the sample with 100% accuracy for EP. Progesterone and PAPP-A classified the viability in 61% of the sample with 94% accuracy.

CONCLUSION(S)

Multiple marker panels can distinguish pregnancy location and viability in a subset of women at risk for early pregnancy complications. This strategy of combining markers to maximize sensitivity and specificity results in high accuracy in a subset of subjects. Activin A, ADAM12, and Glyc are the most promising markers for pregnancy location; P and PAPP-A for viability.

The Primary Effect on the Proteome of ARID1A-mutated Ovarian Clear Cell Carcinoma is Downregulation of the Mevalonate Pathway at the Post-transcriptional Level

Inactivating mutations in ARID1A, which encodes a subunit of the SWI/SNF chromatin-remodeling complex, are found in over half of ovarian clear cell carcinoma cases and more broadly across most types of cancers. To identify ARID1A-dependent changes in intracellular signaling pathways, we performed proteome analyses of isogenic ovarian clear cell carcinoma cell lines with or without ARID1A expression. Knockout of ARID1A in an ovarian clear cell carcinoma cell line with wild-type ARID1A, OVCA429, primarily resulted in downregulation of the mevalonate pathway, an important metabolic pathway involved in isoprenoid synthesis, cholesterol synthesis, and other downstream pathways. In a complementary experiment, expression of wild-type ARID1A in an ovarian clear cell carcinoma cell line containing mutated ARID1A, OVISE, affected the mevalonate pathway in a reciprocal manner. A striking aspect of these analyses was that, although only 5% of the detected proteome showed significant abundance changes, most proteins in the mevalonate pathway were coordinately affected by ARID1A status. There were generally corresponding changes when comparing the proteomics data to our previously published microarray data for ectopic expression of ARID1A in the OVISE cell line. However, ARID1A-dependent changes were not detected for genes within the mevalonate pathway. This discrepancy suggests that the mevalonate pathway is not regulated directly by ARID1A-mediated transcription and may be regulated post-transcriptionally. We conclude that ARID1A status indirectly influences the mevalonate pathway and probably influences other processes including glycogen metabolism and 14-3-3-mediated signaling. Further, our findings demonstrate that changes in mRNA levels are sometimes poor indicators of signaling pathways affected by gene manipulations in cancer cells.

Baseline Immunoglobulin E Levels as a Marker of Doxorubicin- and Trastuzumab-Associated Cardiac Dysfunction

RATIONALE

There is a critical need to develop robust, mechanistic strategies to identify patients at increased risk of cancer therapeutics-related cardiac dysfunction (CTRCD).

OBJECTIVE

We aimed to discover new biomarkers associated with doxorubicin- and trastuzumab-induced CTRCD using high-throughput proteomic profiling.

METHODS AND RESULTS

Plasma, echocardiograms, and clinical outcomes were collected at standardized intervals in breast cancer patients undergoing doxorubicin and trastuzumab cancer therapy. Thirty-one longitudinal plasma samples from 3 cases with CTRCD and 4 age- and cancer-matched controls without CTRCD were processed and analyzed using label-free liquid chromatography-mass spectrometry. From these analyses, 862 proteins were identified from case/control pairs 1 and 2 and 1360 proteins from case/control pair 3. Proteins with a >1.5-fold change in cases compared with controls with a P<0.05 either at the time of CTRCD diagnosis or across all time points were considered candidate diagnostic or predictive biomarkers, respectively. The protein that demonstrated the largest differences between cases and controls was immunoglobulin E, with higher levels detected at baseline and across all time points in controls without CTRCD as compared with matched CTRCD cases (P<0.05). Similarly, in a validation study of 35 participants treated with doxorubicin and trastuzumab, high baseline immunoglobulin E levels were associated with a significantly lower risk of CTRCD (P=0.018).

CONCLUSIONS

In patients receiving doxorubicin and trastuzumab, high baseline immunoglobulin E levels are associated with a lower risk of CTRCD. These novel findings suggest a new paradigm in cardio-oncology, implicating the immune system as a potential mediator of doxorubicin- and trastuzumab-induced cardiac dysfunction.

Identification of secreted proteins that reflect autophagy dynamics within tumor cells

Macroautophagy, a catabolic process of cellular self-digestion, is an important tumor cell survival mechanism and a potential target in antineoplastic therapies. Recent discoveries have implicated autophagy in the cellular secretory process, but potential roles of autophagy-mediated secretion in modifying the tumor microenvironment are poorly understood. Furthermore, efforts to inhibit autophagy in clinical trials have been hampered by suboptimal methods to quantitatively measure tumor autophagy levels. Here, we leveraged the autophagy-based involvement in cellular secretion to identify shed proteins associated with autophagy levels in melanoma. The secretome of low-autophagy WM793 melanoma cells was compared to its highly autophagic metastatic derivative, 1205Lu in physiological 3-dimensional cell culture using quantitative proteomics. These comparisons identified candidate autophagy biomarkers IL1B (interleukin 1, β), CXCL8 (chemokine (C-X-C motif) ligand 8), LIF (leukemia inhibitory factor), FAM3C (family with sequence similarity 3, member C), and DKK3 (dickkopf WNT signaling pathway inhibitor 3) with known roles in inflammation and tumorigenesis, and these proteins were subsequently shown to be elevated in supernatants of an independent panel of high-autophagy melanoma cell lines. Secretion levels of these proteins increased when low-autophagy melanoma cells were treated with the autophagy-inducing tat-BECN1 (Beclin 1) peptide and decreased when ATG7 (autophagy-related 7) was silenced in high-autophagy cells, thereby supporting a mechanistic link between these secreted proteins and autophagy. In addition, serum from metastatic melanoma patients with high tumor autophagy levels exhibited higher levels of these proteins than serum from patients with low-autophagy tumors. These results suggest that autophagy-related secretion affects the tumor microenvironment and measurement of autophagy-associated secreted proteins in plasma and possibly in tumors can serve as surrogates for intracellular autophagy dynamics in tumor cells.

Development of an improved fractionation of the human plasma proteome by a combination of abundant proteins depletion and multi-lectin affinity chromatography

Aim: Current analytical tools lack the required capacity to reduce the complexity of the plasma proteome and identify low-level proteins of clinical interest. Hence, the need to develop a fractionation approach to provide adequate throughput for a clinical study and minimize the loss and improve the detection of low abundance proteins. Materials & methods: We present the development of an analytical platform that combines the depletion of 12 high abundance proteins and multi-lectin affinity chromatography (12P-M-LAC) fractionation. Results & conclusion: We validated the highly specific, stable and robust 12P-M-LAC platform using human plasma. An improved enrichment of low abundance proteins and glycoproteins with minimum sample loss was achieved demonstrating the suitability of this platform in future biomarker discovery studies.

Molecular diagnostics and therapeutics for ectopic pregnancy

Ectopic pregnancies are a serious gynaecological emergency that can be fatal. As such, prompt diagnosis and safe timely treatment is essential. Here, we review the literature on the development of molecularly targeted diagnostics and therapeutics for ectopic pregnancy. A blood-based biomarker that accurately identifies an ectopic pregnancy could be used to offer early diagnostic certainty in cases where ultrasound cannot determine the location of the embryo ('a pregnancy of unknown location'). Molecules examined so far can be broadly grouped into biological themes of relevance to reproduction: (i) Fallopian tube (dys)function, (ii) embryo/trophoblast growth, (iii) corpus luteum function, (iv) inflammation, (v) uterine function and (vi) angiogenesis. While a sensitive and specific biomarker for ectopic pregnancy has yet to be identified, it is possible that improvements in platform technologies or a multi-modal biomarker approach may yield an accurate diagnostic biomarker test. Furthermore, with the advent of better imaging technology, the need for a blood-based biomarker test may be superseded by improvements in ultrasound or magnetic resonance imaging technology. There have been some recent preclinical studies describing molecularly targeted therapeutic approaches for ectopic pregnancy. Notably, bench-to-bedside studies have examined the use of combination gefitinib (orally available epidermal growth factor receptor inhibitor) and methotrexate. Preclinical studies suggest that combination gefitinib and methotrexate is highly effective in inducing placental cell death, and is significantly more effective than methotrexate alone. In early human trials, encouraging preliminary efficacy data have shown that combination gefitinib and methotrexate can rapidly resolve tubal ectopic pregnancies, and large extra-tubal ectopic pregnancies. If a large clinical randomized controlled trial confirms these findings, combination gefitinib and methotrexate could become a new medical treatment option for ectopic pregnancy.

Dysferlin and other non-red cell proteins accumulate in the red cell membrane of Diamond-Blackfan Anemia patients

Diamond Blackfan Anemia (DBA) is a congenital anemia usually caused by diverse mutations in ribosomal proteins. Although the genetics of DBA are well characterized, the mechanisms that lead to macrocytic anemia remain unclear. We systematically analyzed the proteomes of red blood cell membranes from multiple DBA patients to determine whether abnormalities in protein translation or erythropoiesis contribute to the observed macrocytosis or alterations in the mature red blood cell membrane. In depth proteome analysis of red cell membranes enabled highly reproducible identification and quantitative comparisons of 1100 or more proteins. These comparisons revealed clear differences between red cell membrane proteomes in DBA patients and healthy controls that were consistent across DBA patients with different ribosomal gene mutations. Proteins exhibiting changes in abundance included those known to be increased in DBA such as fetal hemoglobin and a number of proteins not normally found in mature red cell membranes, including proteins involved in the major histocompatibility complex class I pathway. Most striking was the presence of dysferlin in the red blood cell membranes of DBA patients but absent in healthy controls. Immunoblot validation using red cell membranes isolated from additional DBA patients and healthy controls confirmed a distinct membrane protein signature specific to patients with DBA.

Enhanced identification of zero-length chemical cross-links using label-free quantitation and high-resolution fragment ion spectra

Chemical cross-linking coupled to mass spectrometry provides structural information that is useful for probing protein conformations and providing experimental support for molecular models. "Zero-length" cross-links have greater value for these applications than longer cross-links because they provide more stringent distance constraints. However, this method is less commonly utilized because it cannot take advantage of isotopic labels, MS-labile bonds, or enrichment tags to facilitate identification. In this study, we combined label-free precursor ion quantitation and targeted tandem mass spectrometry with a new software tool, Zero-length Cross-link Miner (ZXMiner), to form a multitiered analysis strategy. A major, critical objective was to simultaneously achieve very high accuracy with essentially no false-positive cross-link identifications while maintaining a good depth of analysis. Our strategy was optimized on several proteins with known crystal structures. Comparison of ZXMiner to several existing cross-link analysis software showed that other algorithms detected less true positive cross-links and were far less accurate. Although prior use of zero-length cross-linking was typically restricted to small proteins, ZXMiner and the associated strategy enable facile analysis of very large protein complexes. This was demonstrated by identification of zero-length cross-links using purified 526 kDa spectrin heterodimers and intact red cell membranes and membrane skeletons.

Comparative secretome analysis of epithelial and mesenchymal subpopulations of head and neck squamous cell carcinoma identifies S100A4 as a potential therapeutic target

Epithelial-mesenchymal transition (EMT) is a key contributor in tumor progression and metastasis. EMT produces cellular heterogeneity within head and neck squamous cell carcinomas (HNSCC) by creating a phenotypically distinct mesenchymal subpopulation that is resistant to conventional therapies. In this study, we systematically characterized differences in the secretomes of E-cadherin high epithelial-like and E-cadherin low mesenchymal-like subpopulations using unbiased and targeted proteomics. A total 1765 proteins showed significant changes with 177 elevated in the epithelial subpopulation and 173 elevated in the mesenchymal cells. Key nodes in affected networks included NFκB, Akt, and ERK, and most implicated cellular components involved various aspects of the extracellular matrix. In particular, large changes were observed in multiple collagens with most affected collagens at much higher abundance levels in the mesenchymal subpopulation. These cells also exhibited a secretome profile resembling that of cancer-associated fibroblastic cells (CAF). S100A4, a commonly used marker for cancer-associated fibroblastic cells, was elevated more than 20-fold in the mesenchymal cells and this increase was further verified at the transcriptome level. S100A4 is a known mediator of EMT, leading to metastasis and EMT has been proposed as a potential source of cancer-associated fibroblastic cells in solid tumors. S100A4 knockdown by small interfering RNA led to decreased expression, secretion and activity of matrix metalloproteinase 2, as verified by quantitative PCR, multiple reaction monitoring and zymography analyses, and reduced invasion in collagen-embedded spheroids. Further confirmation in three-dimensional organotypic reconstructs showed less invasion and advanced differentiation in the S100A4 RNA interference samples. Orthotopic metastasis model, developed to validate the findings in vivo, demonstrated a decrease in spontaneous metastasis and augmented differentiation in the primary tumor in siS100A4 xenografts. These results demonstrate the value of secretome profiling to evaluate phenotypic conversion and identify potential novel therapeutic targets such as S100A4.

The common hereditary elliptocytosis-associated α-spectrin L260P mutation perturbs erythrocyte membranes by stabilizing spectrin in the closed dimer conformation

Hereditary elliptocytosis (HE) and hereditary pyropoikilocytosis (HPP) are common disorders of erythrocyte shape primarily because of mutations in spectrin. The most common HE/HPP mutations are located distant from the critical αβ-spectrin tetramerization site, yet still interfere with formation of spectrin tetramers and destabilize the membrane by unknown mechanisms. To address this question, we studied the common HE-associated mutation, αL260P, in the context of a fully functional mini-spectrin. The mutation exhibited wild-type tetramer binding in univalent binding assays, but reduced binding affinity in bivalent-binding assays. Biophysical analyses demonstrated the mutation-containing domain was only modestly structurally destabilized and helical content was not significantly changed. Gel filtration analysis of the αL260P mini-spectrin indicated more compact structures for dimers and tetramers compared with wild-type. Chemical crosslinking showed structural changes in the mutant mini-spectrin dimer were primarily restricted to the vicinity of the αL260P mutation and indicated large conformational rearrangements of this region. These data indicate the mutation increased the stability of the closed dimer state, thereby reducing tetramer assembly and resulting in membrane destabilization. These results reveal a novel mechanism of erythrocyte membrane destabilization that could contribute to development of therapeutic interventions for mutations in membrane proteins containing spectrin-type domains associated with inherited disease.

Glycoproteomic strategies: From discovery to clinical application of cancer carbohydrate biomarkers

Carbohydrate antigens are the most frequently and traditionally used biomarkers for cancer, such as CA19-9, CA125, DUPAN-II, AFP-L3, and many others. The diagnostic potential of them was simply based on the cancer-specific alterations of glycan structures on particular glycoproteins in serum/plasma. In spite of the facts that glycosylation disorders are feasible for cancer biomarkers and glycomic analysis technologies to explore them have been rapidly developed, it remains difficult to sensitively screen glycan structure changes on cancer-associated glycoproteins from clinical specimens. Moreover, a lot of additional issues should be appropriately addressed for the clinical application of newly identified glycosylation biomarkers, including analytical throughput, quantitative confirmation of structural changes, and biological explanation for the alterations. In the last decade, significant improvement of mass spectrometric techniques is being made in the aspects of both hardware spec and preanalytical purification procedures for glycoprotein analysis. Here we review potential approaches to perform comprehensive analysis of glycoproteomic biomarker screening from serum/plasma and to realize high-throughput validation of site-specific oligosaccharide variations. The power and problems of mass spectrometric applications on the clinical use of carbohydrate biomarkers are also discussed in this review.

G protein-coupled receptor kinase 2 (GRK2) is localized to centrosomes and mediates epidermal growth factor-promoted centrosomal separation

G protein-coupled receptor kinases (GRKs) play a central role in regulating receptor signaling, but recent studies suggest a broader role in modulating normal cellular functions. For example, GRK5 has been shown to localize to centrosomes and regulate microtubule nucleation and cell cycle progression. Here we demonstrate that GRK2 is also localized to centrosomes, although it has no role in centrosome duplication or microtubule nucleation. Of interest, knockdown of GRK2 inhibits epidermal growth factor receptor (EGFR)-mediated separation of duplicated centrosomes. This EGFR/GRK2-mediated process depends on the protein kinases mammalian STE20-like kinase 2 (Mst2) and Nek2A but does not involve polo-like kinase 1. In vitro analysis and dominant-negative approaches reveal that GRK2 directly phosphorylates and activates Mst2. Collectively these findings demonstrate that GRK2 is localized to centrosomes and plays a central role in mitogen-promoted centrosome separation most likely via its ability to phosphorylate Mst2.

Shotgun proteomics identifies serum fibronectin as a candidate diagnostic biomarker for inclusion in future multiplex tests for ectopic pregnancy

Ectopic pregnancy (EP) is difficult to diagnose early and accurately. Women often present at emergency departments in early pregnancy with a ‘pregnancy of unknown location’ (PUL), and diagnosis and exclusion of EP is challenging due to a lack of reliable biomarkers. The objective of this study was to identify novel diagnostic biomarkers for EP. Shotgun proteomics, incorporating combinatorial-ligand library pre-fractionation, was used to interrogate pooled sera (n = 40) from women undergoing surgery for EP, termination of viable intrauterine pregnancy and management of non-viable intrauterine pregnancy. Western blot was used to validate results in individual sera. ELISAs were developed to interrogate sera from women with PUL (n = 120). Sera were collected at time of first symptomatic presentation and categorized according to pregnancy outcome. The main outcome measures were differences between groups and area under the receiver operating curve (ROC). Proteomics identified six biomarker candidates. Western blot detected significant differences in levels of two of these candidates. ELISA of sera from second cohort revealed that these differences were only significant for one of these candidates, fibronectin. ROC analysis of ability of fibronectin to discriminate EP from other pregnancy outcomes suggested that fibronectin has diagnostic potential (ROC 0.6439; 95% CI 0.5090 to 0.7788; P>0.05), becoming significant when ‘ambiguous’ medically managed PUL excluded from analysis (ROC 0.6538; 95% CI 0.5158 to 0.7918; P<0.05). Fibronectin may make a useful adjunct to future multiplex EP diagnostic tests.

Protein isoform-specific validation defines multiple chloride intracellular channel and tropomyosin isoforms as serological biomarkers of ovarian cancer

New serological biomarkers for early detection and clinical management of ovarian cancer are urgently needed, and many candidates have been reported. A major challenge frequently encountered when validating candidates in patients is establishing quantitative assays that distinguish between highly homologous proteins. The current study tested whether multiple members of two recently discovered ovarian cancer biomarker protein families, chloride intracellular channel (CLIC) proteins and tropomyosins (TPM), were detectable in ovarian cancer patient sera. A multiplexed, label-free multiple reaction monitoring (MRM) assay was established to target peptides specific to all detected CLIC and TPM family members, and their serum levels were quantitated for ovarian cancer patients and non-cancer controls. In addition to CLIC1 and TPM1, which were the proteins initially discovered in a xenograft mouse model, CLIC4, TPM2, TPM3, and TPM4 were present in ovarian cancer patient sera at significantly elevated levels compared with controls. Some of the additional biomarkers identified in this homolog-centric verification and validation approach may be superior to the previously identified biomarkers at discriminating between ovarian cancer and non-cancer patients. This demonstrates the importance of considering all potential protein homologs and using quantitative assays for cancer biomarker validation with well-defined isoform specificity.

BIOLOGICAL SIGNIFICANCE

This manuscript addresses the importance of distinguishing between protein homologs and isoforms when identifying and validating cancer biomarkers in plasma or serum. Specifically, it describes the use of targeted in-depth LC-MS/MS analysis to determine the members of two protein families, chloride intracellular channel (CLIC) and tropomyosin (TPM) proteins that are detectable in sera of ovarian cancer patients. It then establishes a multiplexed isoform- and homology-specific MRM assay to quantify all observed gene products in these two protein families as well as many of the closely related tropomyosin isoforms. Using this assay, levels of all detected CLICs and TPMs were quantified in ovarian cancer patient and control subject sera. These results demonstrate that in addition to the previously known CLIC1, multiple tropomyosins and CLIC4 are promising new ovarian cancer biomarkers. Based on these initial validation studies, these new ovarian cancer biomarkers appear to be superior to most previously known ovarian cancer biomarkers.

Identification of multiple novel protein biomarkers shed by human serous ovarian tumors into the blood of immunocompromised mice and verified in patient sera

The most cancer-specific biomarkers in blood are likely to be proteins shed directly by the tumor rather than less specific inflammatory or other host responses. The use of xenograft mouse models together with in-depth proteome analysis for identification of human proteins in the mouse blood is an under-utilized strategy that can clearly identify proteins shed by the tumor. In the current study, 268 human proteins shed into mouse blood from human OVCAR-3 serous tumors were identified based upon human vs. mouse species differences using a four-dimensional plasma proteome fractionation strategy. A multi-step prioritization and verification strategy was subsequently developed to efficiently select some of the most promising biomarkers from this large number of candidates. A key step was parallel analysis of human proteins detected in the tumor supernatant, because substantially greater sequence coverage for many of the human proteins initially detected in the xenograft mouse plasma confirmed assignments as tumor-derived human proteins. Verification of candidate biomarkers in patient sera was facilitated by in-depth, label-free quantitative comparisons of serum pools from patients with ovarian cancer and benign ovarian tumors. The only proteins that advanced to multiple reaction monitoring (MRM) assay development were those that exhibited increases in ovarian cancer patients compared with benign tumor controls. MRM assays were facilely developed for all 11 novel biomarker candidates selected by this process and analysis of larger pools of patient sera suggested that all 11 proteins are promising candidate biomarkers that should be further evaluated on individual patient blood samples.

Comparison of the diagnostic values of circulating steroid hormones, VEGF-A, PIGF, and ADAM12 in women with ectopic pregnancy

Background

Several peripheral proteins that might be useful for detecting the presence of ectopic pregnancy (EP) have been evaluated, but none have been proven entirely useful in the clinic. We investigated the presence and the possible changes in circulating molecules that distinguish between normal intrauterine pregnancy (IUP) and tubal ectopic pregnancy.

Methods

Non-pregnant women during the menstrual cycle, women with IUP, and women with tubal EP after informed consent. Serum levels of 17β-estradiol (E2), progesterone (P4), testosterone (T), beta-human chorionic gonadotropin (β-hCG), vascular endothelial growth factor-A (VEGF-A), placental growth factor (PIGF), and a distintegrin and metalloprotease protein 12 (ADAM12) were analyzed. Receiver operating characteristic analysis was used to assess the diagnostic discrimination of EP and gestational age-matched IUP.

Results

E2, P4, PIGF, and ADAM12 levels increased and β-hCG decreased throughout IUP. E2 and VEGF-A levels were significantly different between women with tubal EP and IUP. However, using a serum β-hCG cut-off of less than 1000 mIU/mL, P4 was significantly lower in women with tubal EP compared to IUP. Although E2 was inversely correlated with VEGF-A in women in the early stages of IUP, E2 was not correlated with VEGF-A in women with EP prior to tubal surgery. There were no significant differences in either PIGF or ADAM12 alone between women with tubal EP or IUP. Although no significant correlations were seen between E2 and PIGF or P4 and ADAM12 in women in the early stages of IUP, E2 was positively correlated with PIGF and P4 was positively correlated with ADAM12 in women with EP prior to tubal surgery. Our studies defined associations but not causality.

Conclusions

Individual measurements of serum E2 or VEGF-A levels are strongly related to early pregnancy outcomes for women with IUP and EP, and pregnancy-associated E2 and VEGF-A levels provide diagnostic accuracy for the presence of tubal EP. This study demonstrates that correlation analysis of E2/VEGF-A and E2/PIGF serum levels may be able to distinguish a tubal EP from a normal IUP.

Biomarkers for ectopic pregnancy and pregnancy of unknown location

Early pregnancy failure is the most common complication of pregnancy, and 1% to 2% of all pregnancies will be ectopic. As one of the leading causes of maternal morbidity and mortality, diagnosing ectopic pregnancy and determining the fate of a pregnancy of unknown location are of great clinical concern. Several serum and plasma biomarkers for ectopic pregnancy have been investigated independently and in combination. The following is a review of the state of biomarker discovery and development for ectopic pregnancy and pregnancy of unknown location.

Assessment of two immunodepletion methods: off-target effects and variations in immunodepletion efficiency may confound plasma proteomics

Immunodepletion of abundant plasma proteins increases the depth of proteome penetration by mass spectrometry. However, the nature and extent of immunodepletion and the effect of off-target depletion on the quantitative comparison of the residual proteins have not been critically addressed. We performed mass spectrometry label-free quantitation to determine which proteins were immunodepleted and by how much. Two immunodepletion resins were compared: Qproteome (Qiagen) which removes albumin+immunoglobulins and Seppro IgY14+SuperMix (Sigma-Aldrich) which removes 14 target proteins plus a number of unidentified proteins. Plasma collected by P100 proteomic plasma collection tubes (BD) from 20 human subjects was individually immunodepleted to minimize potential variability, prior to pooling. The abundant proteins were quantified better when using only albumin+immunoglobulins removal (Qproteome), while lower abundance proteins were evaluated better using exhaustive immunodepletion (Seppro IgY14+SuperMix). The latter resin removed at least 155 proteins, 38% of the plasma proteome in protein number and 94% of plasma protein in mass. The depth of immunodepletion likely accounts for the effectiveness of this resin in revealing low abundance proteins. However, the more profound immunodepletion achieved with the IgY14+SuperMix may lead to false-positive fold-changes between comparison groups if the reproducibility and efficiency of the depletion of a given protein are not considered.

Serum biomarkers for detecting ectopic pregnancy

Unless an ectopic pregnancy is visible by ultrasound, diagnosis can be a challenge. Differentiating ectopic pregnancies from intrauterine pregnancies can be impossible without intervention or follow-up. This poses a clinical dilemma to the practitioner given the inherent danger to the mother of tubal rupture of an ectopic pregnancy versus the fear of intervening in the case of a desired pregnancy without certainty of diagnosis. Early diagnostic modalities are clearly lacking, and serum biomarkers are currently being investigated as a solution to need for a rapid and accurate test for ectopic pregnancy.

A label-free proteome analysis strategy for identifying quantitative changes in erythrocyte membranes induced by red cell disorders

Red blood cells have been extensively studied but many questions regarding membrane properties and pathophysiology remain unanswered. Proteome analysis of red cell membranes is complicated by a very wide dynamic range of protein concentrations as well as the presence of proteins that are very large, very hydrophobic, or heterogeneously glycosylated. This study investigated the removal of other blood cell types, red cell membrane extraction, differing degrees of fractionation using 1-D SDS gels, and label-free quantitative methods to determine optimized conditions for proteomic comparisons of clinical blood samples. The results showed that fractionation of red cell membranes on 1-D SDS gels was more efficient than low-ionic-strength extractions followed by 1-D gel fractionation. When gel lanes were sliced into 30 uniform slices, a good depth of analysis that included the identification of most well-characterized, low-abundance red cell membrane proteins including those present at 500 to 10,000 copies per cell was obtained. Furthermore, the size separation enabled detection of changes due to proteolysis or in vivo protein crosslinking. A combination of Rosetta Elucidator quantitation and subsequent statistical analysis enabled the robust detection of protein differences that could be used to address unresolved questions in red cell disorders. This article is part of a Special Issue entitled: Integrated omics.

Systematic comparison of fractionation methods for in-depth analysis of plasma proteomes

Discovery and validation of plasma biomarkers are quite challenging because of the high complexity and wide dynamic range of the plasma proteome. Current plasma protein profiling strategies usually use major protein immunodepletion and nanoLC-MS/MS as the first and final analytical steps, respectively, but additional fractionation is needed to detect and quantify low-abundance disease biomarkers. In this study, the performances of 1-D SDS-PAGE, peptide isoelectrofocusing, and peptide high pH reverse-phase chromatography for fractionation of immunodepleted human plasma were systematically compared by evaluating protein coverage, peptide resolution, and capacity to detect known low-abundance proteins. Trade-offs between increasing the number of fractions to improve proteome coverage and resulting decreases in throughput also were assessed. High pH reverse-phase HPLC exhibited the highest peptide resolution and yielded the best depth of analysis with detection of the largest number of known low-abundance proteins for a given level of fractionation. Another advantage of using high pH reverse-phase fractionation rather than 1-D SDS gels is that all fractionation steps except for abundant protein depletion occur at the peptide level, making this strategy more compatible with quantitative biomarker validation methods such as stable isotope dilution multiple reaction monitoring.

Mechanisms of disease: the endocrinology of ectopic pregnancy

Ectopic pregnancy is defined as a pregnancy implanted outside the uterus, and >98% implant in the Fallopian tube. It has a major clinical and socioeconomic impact worldwide. The diagnosis of ectopic pregnancy is often difficult and resource intensive owing to a lack of accurate biomarkers, and there is a need for improved medical management of ectopic pregnancy using new or adjuvant treatments. The aetiology of ectopic pregnancy is uncertain, but tubal implantation is probably due to retention of the embryo in the Fallopian tube owing to impaired embryo-tubal transport and alterations in the tubal microenvironment. This comprehensive review of the literature supporting current understanding of the endocrinology of Fallopian tube biology and tubal implantation focuses on genes expressed in the Fallopian tube regulated by oestrogen and progesterone and discusses their potential functions. It concludes with a discussion of how advances in this field are enabling the development of novel biomarkers and could lead to the identification of potential new treatments for ectopic pregnancy.

Laboratory models for studying ectopic pregnancy

PURPOSE OF REVIEW

Understanding the cause of tubal ectopic pregnancy (tEP) remains incomplete. We aim to summarize the latest advances in laboratory models of tEP that we believe will, ultimately, contribute to improving the diagnosis and management of the condition.

RECENT FINDINGS

Progress in proteome prefractionation and multidimensional protein identification technology has proved particularly effective in identifying novel biomarkers of tEP. These, and related global proteomic and genomic approaches, have as yet to be fully exploited in this context but do have substantial potential to inform future hypothesis-driven studies. The majority of data generated since 2009 to explain the cause of tEP continues to derive from descriptive human ex-vivo studies. In-vitro models of fallopian tube ciliary and smooth muscle function have improved to a limited degree, on the back of continuing advances in imaging and data acquisition. We believe that the recent development of a primary human fallopian tube epithelium culture system represents the most significant recent advance in laboratory models for studying ectopic pregnancy. There remain no good animal models of tEP.

SUMMARY

The establishment of a viable animal model of tEP remains the key obstacle to a complete understanding of the cause of the condition.

A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer

Proteomics discovery of novel cancer serum biomarkers is hindered by the great complexity of serum, patient-to-patient variability, and triggering by the tumor of an acute-phase inflammatory reaction. This host response alters many serum protein levels in cancer patients, but these changes have low specificity as they can be triggered by diverse causes. We addressed these hurdles by utilizing a xenograft mouse model coupled with an in-depth 4-D protein profiling method to identify human proteins in the mouse serum. This strategy ensures that identified putative biomarkers are shed by the tumor, and detection of low-abundance proteins shed by the tumor is enhanced because the mouse blood volume is more than a thousand times smaller than that of a human. Using TOV-112D ovarian tumors, more than 200 human proteins were identified in the mouse serum, including novel candidate biomarkers and proteins previously reported to be elevated in either ovarian tumors or the blood of ovarian cancer patients. Subsequent quantitation of selected putative biomarkers in human sera using label-free multiple reaction monitoring (MRM) mass spectrometry (MS) showed that chloride intracellular channel 1, the mature form of cathepsin D, and peroxiredoxin 6 were elevated significantly in sera from ovarian carcinoma patients.

Rapid verification of candidate serological biomarkers using gel-based, label-free multiple reaction monitoring

Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS) has emerged as a promising platform for verification of serological candidate biomarkers. However, cost and time needed to synthesize and evaluate stable isotope peptides, optimize spike-in assays, and generate standard curves quickly becomes unattractive when testing many candidate biomarkers. In this study, we demonstrate that label-free multiplexed MRM-MS coupled with major protein depletion and 1D gel separation is a time-efficient, cost-effective initial biomarker verification strategy requiring less than 100 μL of serum. Furthermore, SDS gel fractionation can resolve different molecular weight forms of targeted proteins with potential diagnostic value. Because fractionation is at the protein level, consistency of peptide quantitation profiles across fractions permits rapid detection of quantitation problems for specific peptides from a given protein. Despite the lack of internal standards, the entire workflow can be highly reproducible, and long-term reproducibility of relative protein abundance can be obtained using different mass spectrometers and LC methods with external reference standards. Quantitation down to ~200 pg/mL could be achieved using this workflow. Hence, the label-free GeLC-MRM workflow enables rapid, sensitive, and economical initial screening of large numbers of candidate biomarkers prior to setting up SID-MRM assays or immunoassays for the most promising candidate biomarkers.

Widespread RNA and DNA sequence differences in the human transcriptome

The transmission of information from DNA to RNA is a critical process. We compared RNA sequences from human B cells of 27 individuals to the corresponding DNA sequences from the same individuals and uncovered more than 10,000 exonic sites where the RNA sequences do not match that of the DNA. All 12 possible categories of discordances were observed. These differences were nonrandom as many sites were found in multiple individuals and in different cell types, including primary skin cells and brain tissues. Using mass spectrometry, we detected peptides that are translated from the discordant RNA sequences and thus do not correspond exactly to the DNA sequences. These widespread RNA-DNA differences in the human transcriptome provide a yet unexplored aspect of genome variation.

A disintegrin and metalloprotease protein-12 as a novel marker for the diagnosis of ectopic pregnancy

OBJECTIVE

To evaluate the performance of a novel biomarker, a disintegrin and metalloprotease-12 (ADAM-12), to differentiate an ectopic pregnancy (EP) from normal intrauterine pregnancies (IUPs).

DESIGN

Case-control study.

SETTING

Three urban academic centers.

PATIENT(S)

Women who were seen in the emergency department with pain or bleeding in the first trimester of pregnancy.

INTERVENTION(S)

Sera from women with diagnosed EP or IUP were evaluated via proteomics and an ADAM-12 dissociation-enhanced lanthanide fluoroimmunoassay.

MAIN OUTCOME MEASURE(S)

Differences between groups, area under the receiver operating curve, sensitivity, and specificity.

RESULT(S)

Via a proteomics evaluation, we found a statistically significant decrease in ADAM-12 in the sera of patients with EP, which we confirmed in a larger group of 199 patients (median IUP 18.6 ng/mL versus median EP 2.5 ng/mL with good discrimination between the groups as assessed by receiver operating characteristics [area under the curve = 0.82]). At a low cut-point, the sensitivity was 70% and specificity 84%, but, at a higher cut-point optimizing sensitivity, the ADAM-12 test demonstrated a sensitivity of 97%.

CONCLUSION(S)

ADAM-12 is a promising marker for the diagnosis of EP in women with symptoms in the first trimester, validating the proteomics findings. Further studies in additional patient populations and in combination with other biomarkers are needed.