Multiplexed Serum Biomarkers to Discriminate Nonviable and Ectopic Pregnancy.

OBJECTIVE: The use of multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies. In this study we assessed 24 markers with multiple machine learning-based methodologies to evaluate combinations of top candidates to develop a multiplexed prediction model for identification of 1) viability and 2) location of an early pregnancy. DESIGN: A nested case-control design evaluating the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location SUBJECTS: 218 individuals with a symptomatic (pain and/or bleeding) early pregnancy: 75 with an ongoing intrauterine gestation, 68 ectopic pregnancies, and 75 miscarriages. INTERVENTIONS: Serum values 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 identification of 1) a nonviable pregnancy (ongoing intrauterine pregnancy vs miscarriage or ectopic pregnancy) and 2) an ectopic pregnancy (ectopic pregnancy vs ongoing intrauterine pregnancy or miscarriage). MAIN OUTCOME MEASURES: The predicted classification by each model was compared to actual diagnosis and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), conclusive classification, and accuracy were calculated. RESULTS: Models using classification regression tree analysis using three markers (PSG3, CG-Alpha and PAPPA) were able to predict a maximum sensitivity 93.3%, a maximum specificity 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of three markers (sFLT, PSG3 and TFP12) 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 95.9%. The predictive ability of the biomarkers random forest produced similar test characteristics when using 11 predictive markers. 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 CG-Alpha, PAPPA and PSG3 can be used to predict viability and sFLT, TPFI2 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 ß-human chorionic gonadotropin (ß-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.

HRS phosphorylation drives immunosuppressive exosome secretion and restricts CD8+ T-cell infiltration into tumors.

The lack of tumor infiltration by CD8+ T cells is associated with poor patient response to anti-PD-1 therapy. Understanding how tumor infiltration is regulated is key to improving treatment efficacy. Here, we report that phosphorylation of HRS, a pivotal component of the ESCRT complex involved in exosome biogenesis, restricts tumor infiltration of cytolytic CD8+ T cells. Following ERK-mediated phosphorylation, HRS interacts with and mediates the selective loading of PD-L1 to exosomes, which inhibits the migration of CD8+ T cells into tumors. In tissue samples from patients with melanoma, CD8+ T cells are excluded from the regions where tumor cells contain high levels of phosphorylated HRS. In murine tumor models, overexpression of phosphorylated HRS increases resistance to anti-PD-1 treatment, whereas inhibition of HRS phosphorylation enhances treatment efficacy. Our study reveals a mechanism by which phosphorylation of HRS in tumor cells regulates anti-tumor immunity by inducing PD-L1+ immunosuppressive exosomes, and suggests HRS phosphorylation blockade as a potential strategy to improve the efficacy of cancer immunotherapy.

Proteome-defined changes in cellular pathways for decidua and trophoblast tissues associated with location and viability of early-stage pregnancy.

BACKGROUND: In early pregnancy, differentiating between a normal intrauterine pregnancy (IUP) and abnormal gestations including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge when ultrasound is not yet diagnostic. Clinical treatments for these outcomes are drastically different making early, accurate diagnosis imperative. Hence, a greater understanding of the biological mechanisms involved in these early pregnancy complications could lead to new molecular diagnostics. METHODS: Trophoblast and endometrial tissue was collected from consenting women having an IUP (n = 4), EPL (n = 4), or EP (n = 2). Samples were analyzed by LC-MS/MS followed by a label-free proteomics analysis in an exploratory study. For each tissue type, pairwise comparisons of different pregnancy outcomes (EPL vs. IUP and EP vs. IUP) were performed, and protein changes having a fold change ≥ 3 and a Student's t-test p-value ≤ 0.05 were defined as significant. Pathway and network classification tools were used to group significantly changing proteins based on their functional similarities. RESULTS: A total of 4792 and 4757 proteins were identified in decidua and trophoblast proteomes. For decidua, 125 protein levels (2.6% of the proteome) were significantly different between EP and IUP, whereas EPL and IUP decidua were more similar with only 68 (1.4%) differences. For trophoblasts, there were 66 (1.4%) differences between EPL and IUP. However, the largest group of 344 differences (7.2%) was observed between EP and IUP trophoblasts. In both tissues, proteins associated with ECM remodeling, cell adhesion and metabolic pathways showed decreases in EP specimens compared with IUP and EPL. In trophoblasts, EP showed elevation of inflammatory and immune response pathways. CONCLUSIONS: Overall, differences between an EP and IUP are greater than the changes observed when comparing ongoing IUP and nonviable intrauterine pregnancies (EPL) in both decidua and trophoblast proteomes. Furthermore, differences between EP and IUP were much higher in the trophoblast than in the decidua. This observation is true for the total number of protein changes as well as the extent of changes in upstream regulators and related pathways. This suggests that biomarkers and mechanisms of trophoblast function may be the best predictors of early pregnancy location and viability.

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.

Protein Detection in Gels Using Fixation.

The most commonly used types of gels for separating proteins are SDS gels, either in a 1-D format or as the second dimension of various 2-D separations, and the most common methods of visualizing proteins in these gels use protein binding dyes after fixing the proteins in the gel matrix. In recent years, there has been a continuing trend away from preparing staining solutions in the laboratory to using commercially available kits, which are convenient, save time, have defined shelf lives, and may provide greater reproducibility than stains formulated in research laboratories. In general, when using commercial kits, satisfactory results can be readily obtained by following the manufacturer's protocols. This unit reviews commonly used fixation-based stains and provides a number of manual formulations with staining protocols for those who prefer such staining methods. © 2018 by John Wiley & Sons, Inc.

In-Depth, Reproducible Analysis of Human Plasma Using IgY 14 and SuperMix Immunodepletion.

Identification of cancer and other disease biomarkers in human plasma has been exceptionally challenging due to the complex nature of plasma and the presence of a moderate number of high- and medium-abundance proteins which mask low-abundance proteins of interest. As a result, immunoaffinity depletion formats combining multiple antibodies to target the most abundant plasma proteins have become the first stage in most plasma proteome discovery schemes. This protocol describes the use of tandem IgY 14 and SuperMix immunoaffinity depletion to reproducibly remove >99% of total plasma protein. This greatly increases the depth of analysis of human plasma proteomes. Depleted plasma samples can then be analyzed in a single high-resolution LC-MS/MS run on a Q Exactive Plus mass spectrometer, followed by label-free quantitation. If greater depth of analysis is desired, the depleted plasma can be further fractionated by separating the sample for a short distance on a 1D SDS gel and cutting the gel into uniform slices prior to trypsin digestion. Alternatively, the depleted plasma can be reduced, alkylated, and digested with trypsin followed by high-pH reversed-phase HPLC separation.

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.

Quantitative Comparisons of Large Numbers of Human Plasma Samples Using TMT10plex Labeling.

One strategy for improving the throughput of human plasma proteomic discovery analysis while maintaining good depth of analysis is to multiplex using isobaric tags. At present, the greatest multiplexing that is commercially available uses the TMT10plex kit. As an example of this approach, we describe efficient shotgun discovery proteomics of large numbers of human plasma to identify potential biomarkers. In the analysis strategy, a common pooled reference was used to enable comparisons across multiple experiments. Duplicate samples showed excellent overall reproducibility across different TMT experiments. Data filters that improved the quality of individual peptide and protein quantitation included using a filter for purity of the targeted precursor ion in the isolation window and using only unique peptides.

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.

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.

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.

Plasma biomarker discovery using 3D protein profiling coupled with label-free quantitation.

In-depth quantitative profiling of human plasma samples for biomarker discovery remains quite challenging. One promising alternative to chemical derivatization with stable isotope labels for quantitative comparisons is direct, label-free, quantitative comparison of raw LC-MS data. But, in order to achieve high-sensitivity detection of low-abundance proteins, plasma proteins must be extensively pre-fractionated, and results from LC-MS runs of all fractions must be integrated efficiently in order to avoid misidentification of variations in fractionation from sample to sample as "apparent" biomarkers. This protocol describes a powerful 3D protein profiling method for comprehensive analysis of human serum or plasma proteomes, which combines abundant protein depletion and high-sensitivity GeLC-MS/MS with label-free quantitation of candidate biomarkers.

In-depth analysis of a plasma or serum proteome using a 4D protein profiling method.

Comprehensive proteomic analysis of human plasma or serum has been a major strategy used to identify biomarkers that serve as indicators of disease. However, such in-depth proteomic analyses are challenging due to the complexity and extremely large dynamic range of protein concentrations in plasma. Therefore, reduction in sample complexity through multidimensional pre-fractionation strategies is critical, particularly for the detection of low-abundance proteins that have the potential to be the most specific disease biomarkers. We describe here a 4D protein profiling method that we developed for comprehensive proteomic analyses of both plasma and serum. Our method consists of abundant protein depletion coupled with separation strategies - microscale solution isoelectrofocusing and 1D SDS-PAGE - followed by reversed-phase separation of tryptic peptides prior to LC-MS/MS. Using this profiling strategy, we routinely identify a large number of proteins over nine orders of magnitude, including a substantial number of proteins at the low ng/mL or lower levels from approximately 300 µL of plasma sample.

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

Ectopic pregnancy (EP) and normal intrauterine pregnancy (IUP) serum proteomes were quantitatively compared to systematically identify candidate biomarkers. A 3-D biomarker discovery strategy consisting of abundant protein immunodepletion, SDS gels, LC-MS/MS, and label-free quantitation of MS signal intensities identified 70 candidate biomarkers with differences between groups greater than 2.5-fold. Further statistical analyses of peptide quantities were used to select the most promising 12 biomarkers for further study, which included known EP biomarkers, novel EP biomarkers (ADAM12 and ISM2), and five specific isoforms of the pregnancy specific beta-1-glycoprotein family. Technical replicates showed good reproducibility and protein intensities from the label-free discovery analysis compared favorably with reported abundance levels of several known reference serum proteins over at least 3 orders of magnitude. Similarly, relative abundances of candidate biomarkers from the label-free discovery analysis were consistent with relative abundances from pilot validation assays performed for five of the 12 most promising biomarkers using label-free multiple reaction monitoring of both the patient serum pools used for discovery and the individual samples that constituted these pools. These results demonstrate robust, reproducible, in-depth 3-D serum proteome discovery, and subsequent pilot-scale validation studies can be achieved readily using label-free quantitation strategies.

A systems biology analysis of metastatic melanoma using in-depth three-dimensional protein profiling.

Melanoma is an excellent model to study molecular mechanisms of tumor progression because melanoma usually develops through a series of architecturally and phenotypically distinct stages that are progressively more aggressive, culminating in highly metastatic cells. In this study, we used an in-depth, 3-D protein level, comparative proteome analysis of two genetically, very closely related melanoma cell lines with low- and high-metastatic potentials to identify proteins and key pathways involved in tumor progression. This proteome comparison utilized fluorescent tagging of cell lysates followed by microscale solution IEF prefractionation and subsequent analysis of each fraction on narrow-range 2-D gels. LC-MS/MS analysis of gel spots exhibiting significant abundance changes identified 110 unique proteins. The majority of observed abundance changes closely correlate with biological processes central to cancer progression, such as cell death and growth and tumorigenesis. In addition, the vast majority of protein changes mapped to six cellular networks, which included known oncogenes (JNK, c-myc, and N-myc) and tumor suppressor genes (p53 and transforming growth factor-ß) as critical components. These six networks showed substantial connectivity, and most of the major biological functions associated with these pathways are involved in tumor progression. These results provide novel insights into cellular pathways implicated in melanoma metastasis.