Validation of reverse phase protein array for practical screening of potential biomarkers in serum and plasma: accurate detection of CA19-9 levels in pancreatic cancer

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

MCU controls melanoma progression through a redox‐controlled phenotype switch

Melanoma is the deadliest of skin cancers and has a high tendency to metastasize to distant organs. Calcium and metabolic signals contribute to melanoma invasiveness; however, the underlying molecular details are elusive. The MCU complex is a major route for calcium into the mitochondrial matrix but whether MCU affects melanoma pathobiology was not understood. Here, we show that MCU_A expression correlates with melanoma patient survival and is decreased in BRAF kinase inhibitor‐resistant melanomas. Knockdown (KD) of MCU_A suppresses melanoma cell growth and stimulates migration and invasion. In melanoma xenografts, MCU_{A_KD} reduces tumor volumes but promotes lung metastases. Proteomic analyses and protein microarrays identify pathways that link MCU_A and melanoma cell phenotype and suggest a major role for redox regulation. Antioxidants enhance melanoma cell migration, while prooxidants diminish the MCU_{A_KD}‐induced invasive phenotype. Furthermore, MCU_{A_KD} increases melanoma cell resistance to immunotherapies and ferroptosis. Collectively, we demonstrate that MCU_A controls melanoma aggressive behavior and therapeutic sensitivity. Manipulations of mitochondrial calcium and redox homeostasis, in combination with current therapies, should be considered in treating advanced melanoma.

The transcription factor PAX8 promotes angiogenesis in ovarian cancer through interaction with SOX17

PAX8 is a master transcription factor that is essential during embryogenesis and promotes neoplastic growth. It is expressed by the secretory cells lining the female reproductive tract, and its deletion during development results in atresia of reproductive tract organs. Nearly all ovarian carcinomas express PAX8, and its knockdown results in apoptosis of ovarian cancer cells. To explore the role of PAX8 in these tissues, we purified the PAX8 protein complex from nonmalignant fallopian tube cells and high-grade serous ovarian carcinoma cell lines. We found that PAX8 was a member of a large chromatin remodeling complex and preferentially interacted with SOX17, another developmental transcription factor. Depleting either PAX8 or SOX17 from cancer cells altered the expression of factors involved in angiogenesis and functionally disrupted tubule and capillary formation in cell culture and mouse models. PAX8 and SOX17 in ovarian cancer cells promoted the secretion of angiogenic factors by suppressing the expression of SERPINE1, which encodes a proteinase inhibitor with antiangiogenic effects. The findings reveal a non-cell-autonomous function of these transcription factors in regulating angiogenesis in ovarian cancer.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Reverse phase protein arrays in acute leukemia: investigative and methodological challenges

INTRODUCTION

Acute leukemia results from a series of mutational events that alter cell growth and proliferation. Mutations result in protein changes that orchestrate growth alterations characteristic of leukemia. Proteomics is a methodology appropriate for study of protein changes found in leukemia. The high-throughput reverse phase protein array (RPPA) technology is particularly well-suited for the assessment of protein changes in samples derived from clinical trials.

AREAS COVERED

This review discusses the technical, methodological, and analytical issues related to the successful development of acute leukemia RPPAs.

EXPERT COMMENTARY

To obtain representative protein sample lysates, samples should be prepared from freshly collected blood or bone marrow material. Variables such as sample shipment, transit time, and holding temperature only have minimal effects on protein expression. CellSave preservation tubes are preferred for cells collected after exposure to chemotherapy, and incorporation of standardized guidelines for antibody validation is recommended. A more systematic biological approach to analyze protein expression is desired, searching for recurrent patterns of protein expression that allow classification of patients into risk groups, or groups of patients that may be treated similarly. Comparing RPPA protein analysis between cell lines and primary samples shows that cell lines are not representative of patient proteomic patterns.

Uncovering the Depths of the Human Proteome: Antibody-based Technologies for Ultrasensitive Multiplexed Protein Detection and Quantification

Probing the human proteome in tissues and biofluids such as plasma is attractive for biomarker and drug target discovery. Recent breakthroughs in multiplex, antibody-based, proteomics technologies now enable the simultaneous quantification of thousands of proteins at as low as sub fg/ml concentrations with remarkable dynamic ranges of up to 10-log. We herein provide a comprehensive guide to the methodologies, performance, technical comparisons, advantages, and disadvantages of established and emerging technologies for the multiplexed ultrasensitive measurement of proteins. Gaining holistic knowledge on these innovations is crucial for choosing the right multiplexed proteomics tool for applications at hand to critically complement traditional proteomics methods. This can bring researchers closer than ever before to elucidating the intricate inner workings and cross talk that spans multitude of proteins in disease mechanisms.

Mammary-specific expression of Trim24 establishes a mouse model of human metaplastic breast cancer

Conditional overexpression of histone reader Tripartite motif containing protein 24 (TRIM24) in mouse mammary epithelia (Trim24COE) drives spontaneous development of mammary carcinosarcoma tumors, lacking ER, PR and HER2. Human carcinosarcomas or metaplastic breast cancers (MpBC) are a rare, chemorefractory subclass of triple-negative breast cancers (TNBC). Comparison of Trim24COE metaplastic carcinosarcoma morphology, TRIM24 protein levels and a derived Trim24COE gene signature reveals strong correlation with human MpBC tumors and MpBC patient-derived xenograft (PDX) models. Global and single-cell tumor profiling reveal Met as a direct oncogenic target of TRIM24, leading to aberrant PI3K/mTOR activation. Here, we find that pharmacological inhibition of these pathways in primary Trim24COE tumor cells and TRIM24-PROTAC treatment of MpBC TNBC PDX tumorspheres decreased cellular viability, suggesting potential in therapeutically targeting TRIM24 and its regulated pathways in TRIM24-expressing TNBC.

Reverse Phase Protein Array Reveals Correlation of Retinoic Acid Metabolism With Cardiomyopathy in Friedreich's Ataxia

Identifying biomarkers is important for assessment of disease progression, prediction of symptom development, and determination of treatment effectiveness. While unbiased analyses of differential gene expression using next-generation sequencing methods are now routinely conducted, proteomics studies are more challenging because of traditional methods predominantly being low throughput and offering a limited dynamic range for simultaneous detection of hundreds of proteins that drastically differ in their intracellular abundance. We utilized a sensitive and high-throughput proteomic technique, reverse phase protein array (RPPA), to attain protein expression profiles of primary fibroblasts obtained from patients with Friedreich's ataxia (FRDA) and unaffected controls (CTRLs). The RPPA was designed to detect 217 proteins or phosphorylated proteins by individual antibody, and the specificity of each antibody was validated prior to the experiment. Among 62 fibroblast samples (44 FRDA and 18 CTRLs) analyzed, 30 proteins/phosphoproteins were significantly changed in FRDA fibroblasts compared with CTRL cells (p < 0.05), mostly representing signaling molecules and metabolic enzymes. As expected, frataxin was significantly downregulated in FRDA samples, thus serving as an internal CTRL for assay integrity. Extensive bioinformatics analyses were conducted to correlate differentially expressed proteins with critical disease parameters (e.g., selected symptoms, age of onset, guanine-adenine-adenine sizes, frataxin levels, and Functional Assessment Rating Scale scores). Members of the integrin family of proteins specifically associated with hearing loss in FRDA. Also, RPPA data, combined with results of transcriptome profiling, uncovered defects in the retinoic acid metabolism pathway in FRDA samples. Moreover, expression of aldehyde dehydrogenase family 1 member A3 differed significantly between cardiomyopathy-positive and cardiomyopathy-negative FRDA cohorts, demonstrating that metabolites such as retinol, retinal, or retinoic acid could become potential predictive biomarkers of cardiac presentation in FRDA.

Development and Characterization of Novel Endoxifen-Resistant Breast Cancer Cell Lines Highlight Numerous Differences from Tamoxifen-Resistant Models

Despite the availability of drugs that target ERα-positive breast cancer, resistance commonly occurs, resulting in relapse, metastasis, and death. Tamoxifen remains the most commonly-prescribed endocrine therapy worldwide, and "tamoxifen resistance" has been extensively studied. However, little consideration has been given to the role of endoxifen, the most abundant active tamoxifen metabolite detected in patients, in driving resistance mechanisms. Endoxifen functions differently from the parent drug and other primary metabolites, including 4-hydroxy-tamoxifen (4HT). Many studies have shown that patients who extensively metabolize tamoxifen into endoxifen have superior outcomes relative to patients who do not, supporting a primary role for endoxifen in driving tamoxifen responses. Therefore, "tamoxifen resistance" may be better modeled by "endoxifen resistance" for some patients. Here, we report the development of novel endoxifen-resistant breast cancer cell lines and have extensively compared these models to 4HT and fulvestrant (ICI)-resistant models. Endoxifen-resistant cells were phenotypically and molecularly distinct from 4HT-resistant cells and more closely resembled ICI-resistant cells overall. Specifically, endoxifen resistance was associated with ERα and PR loss, estrogen insensitivity, unique gene signatures, and striking resistance to most FDA-approved second- and third-line therapies. Given these findings, and the importance of endoxifen in the efficacy of tamoxifen therapy, our data indicate that endoxifen-resistant models may be more clinically relevant than existing models and suggest that a better understanding of endoxifen resistance could substantially improve patient care. IMPLICATIONS: Here we report on the development and characterization of the first endoxifen-resistant models and demonstrate that endoxifen resistance may better model tamoxifen resistance in a subset of patients.

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies

Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.

Proteomic analysis of circulating extracellular vesicles identifies potential markers of breast cancer progression, recurrence, and response

Proteomic profiling of circulating small extracellular vesicles (sEVs) represents a promising, noninvasive approach for early detection and therapeutic monitoring of breast cancer (BC). We describe a relatively low-cost, fast, and reliable method to isolate sEVs from plasma of BC patients and analyze their protein content by semiquantitative proteomics. sEV-enriched fractions were isolated from plasma of healthy controls and BC patients at different disease stages before and after surgery. Proteomic analysis of sEV-enriched fractions using reverse phase protein array revealed a signature of seven proteins that differentiated BC patients from healthy individuals, of which FAK and fibronectin displayed high diagnostic accuracy. The size of sEVs was significantly reduced in advanced disease stage, concomitant with a stage-specific protein signature. Furthermore, we observed protein-based distinct clusters of healthy controls, chemotherapy-treated and untreated postsurgery samples, as well as a predictor of high risk of cancer relapse, suggesting that the applied methods warrant development for advanced diagnostics.

Polarized human cholangiocytes release distinct populations of apical and basolateral small extracellular vesicles

Intercellular communication is critical for organismal homeostasis, and defects can contribute to human disease states. Polarized epithelial cells execute distinct signaling agendas via apical and basolateral surfaces to communicate with different cell types. Small extracellular vesicles (sEVs), including exosomes and small microvesicles, represent an understudied form of intercellular communication in polarized cells. Human cholangiocytes, epithelial cells lining bile ducts, were cultured as polarized epithelia in a Transwell system as a model with which to study polarized sEV communication. Characterization of isolated apically and basolaterally released EVs revealed enrichment in sEVs. However, differences in apical and basolateral sEV composition and numbers were observed. Genetic or pharmacological perturbation of cellular machinery involved in the biogenesis of intralumenal vesicles at endosomes (the source of exosomes) revealed general and domain-specific effects on sEV biogenesis/release. Additionally, analyses of signaling revealed distinct profiles of activation depending on sEV population, target cell, and the function of the endosomal sorting complex required for transport (ESCRT)-associated factor ALG-2–interacting protein X (ALIX) within the donor cells. These results support the conclusion that polarized cholangiocytes release distinct sEV pools to mediate communication via their apical and basolateral domains and suggest that defective ESCRT function may contribute to disease states through altered sEV signaling.

KRAB domain of ZFP568 disrupts TRIM28-mediated abnormal interactions in cancer cells

Interactions of KRAB (Krüppel-associated box)-associated protein KAP1 [also known as TRIM28 (tripartite motif containing protein 28)] with DNA-binding KRAB zinc finger (KRAB-ZF) proteins silence many transposable elements during embryogenesis. However, in some cancers, TRIM28 is upregulated and interacts with different partners, many of which are transcription regulators such as EZH2 in MCF7 cells, to form abnormal repressive or activating complexes that lead to misregulation of genes. We ask whether a KRAB domain-the TRIM28 interaction domain present in native binding partners of TRIM28 that mediate repression of transposable elements-could be used as a tool molecule to disrupt aberrant TRIM28 complexes. Expression of KRAB domain containing fragments from a KRAB-ZF protein (ZFP568) in MCF7 cells, without the DNA-binding zinc fingers, inhibited TRIM28-EZH2 interactions and caused degradation of both TRIM28 and EZH2 proteins as well as other components of the EZH2-associated polycomb repressor 2 complex. In consequence, the product of EZH2 enzymatic activity, trimethylation of histone H3 lysine 27 level, was significantly reduced. The expression of a synthetic KRAB domain significantly inhibits the growth of breast cancer cells (MCF7) but has no effect on normal (immortalized) human mammary epithelial cells (MCF10a). Further, we found that TRIM28 is a positive regulator of TRIM24 protein levels, as observed previously in prostate cancer cells, and expression of the KRAB domain also lowered TRIM24 protein. Importantly, reduction of TRIM24 levels, by treatment with either the KRAB domain or a small-molecule degrader targeted to TRIM24, is accompanied by an elevated level of tumor suppressor p53. Taken together, this study reveals a novel mechanism for a TRIM28-associated protein stability network and establishes TRIM28 as a potential therapeutic target in cancers where TRIM28 is elevated. Finally, we discuss a potential mechanism of KRAB-ZF gene expression controlled by a regulatory feedback loop of TRIM28-KRAB.

Proteomics advances for precision therapy in ovarian cancer

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior. Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with 'ovarian cancer' including 'proteomics', 'proteogenomic', 'reverse-phase protein array', 'mass spectrometry', and 'adaptive response', were used to search PubMed. Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.

Adaptive responses in a PARP inhibitor window of opportunity trial illustrate limited functional interlesional heterogeneity and potential combination therapy options

Poly (ADP-ribose) polymerase inhibitor (PARPi)-based combination therapies are demonstrating efficacy in patients, however, identifying the right combination for the right patient remains a critical challenge. Thus, it is urgent to develop approaches able to identify patients likely to benefit from specific combination therapies. Several groups, including ours, have demonstrated that targeting adaptive responses induced by PARPi increases depth and duration of response. In this study, we instituted a talazoparib (PARPi) monotherapy window of opportunity trial to identify informative adaptive responses in high grade serous ovarian cancer patients (HGSOC). Patients were treated for 7 to 14 days with PARPi monotherapy prior to surgery with tissue samples from multiple sites being collected pre- and post-treatment in each patient. Analysis of these samples demonstrated that individual patients displayed different adaptive responses with limited interlesional heterogeneity. Ability of combination therapies designed to interdict adaptive responses to decrease viability was validated using model systems. Thus, assessment of adaptive responses to PARPi provides an opportunity for patient-specific selection of combination therapies designed to interdict patient-specific adaptive responses to maximize patient benefit.

Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics

Breaking down the silos between disciplines to accelerate the pace of cancer research is a key paradigm for the Cancer Moonshot. Molecular analyses of cancer biology have tended to segregate between a focus on nucleic acids-DNA, RNA, and their modifications-and a focus on proteins and protein function. Proteogenomics represents a fusion of those two approaches, leveraging the strengths of each to provide a more integrated vision of the flow of information from DNA to RNA to protein and eventually function at the molecular level. Proteogenomic studies have been incorporated into multiple activities associated with the Cancer Moonshot, demonstrating substantial added value. Innovative study designs integrating genomic, transcriptomic, and proteomic data, particularly those using clinically relevant samples and involving clinical trials, are poised to provide new insights regarding cancer risk, progression, and response to therapy.

Identification of diagnostic upper gastrointestinal cancer tissue type-specific urinary biomarkers

Several potential urinary biomarkers exhibiting an association with upper gastrointestinal tumour growth have been previously identified, of which S100A6, S100A9, rabenosyn-5 and programmed cell death 6-interacting protein (PDCD6IP) were further validated and found to be upregulated in malignant tumours. The cancer cohort from our previous study was subclassified to assess whether distinct molecular markers can be identified for each individual cancer type using a similar approach. Urine samples from patients with cancers of the stomach, oesophagus, oesophagogastric junction or pancreas were analysed by surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry using both CM10 and IMAC30 (Cu²⁺-complexed) chip types and LC-MS/MS-based mass spectrometry after chromatographic enrichment. This was followed by protein identification, pattern matching and validation by western blotting. We found 8 m/z peaks with statistical significance for the four cancer types investigated, of which m/z 2447 and 2577 were identified by pattern matching as fragments of cathepsin-B (CTSB) and cystatin-B (CSTB); both molecules are indicative of pancreatic cancer. Additionally, we observed a potential association of upregulated α-1-antichymotrypsin with pancreatic and gastric cancers, of PDCD6IP, vitelline membrane outer layer protein 1 homolog (VMO1) and triosephosphate isomerase (TPI1) with oesophagogastric junctional cancers, and of complement C4-A, prostatic acid phosphatase, azurocidin and histone-H1 with oesophageal cancer. Furthermore, the potential pancreatic cancer biomarkers CSTB and CTSB were validated independently by western blotting. Therefore, the present study identified two new potential urinary biomarkers that appear to be associated with pancreatic cancer. This may provide a simple, non-invasive screening test for use in the clinical setting.

High Precision RPPA: Concept, Features, and Application Performance of the Integrated Zeptosens Platform

An integrated reverse phase protein array (RPPA) platform shall allow the precise monitoring of expression level and changes of proteins and their functional states in a highly parallel manner even when samples exhibit a complex matrix like in tumor tissues and are available only in very limited amounts. Ideally the full workflow from sample preparation to data visualization shall be covered.This book chapter describes the key elements of the integrated Zeptosens RPPA platform. It addresses critical platform and process design requirements, considerations, and elements as well as critical process steps and quality aspects. Sophisticated instrumentation, high sensitivity readout, and dedicated chip and assay handling equipment act in concert with streamlined protocols, optimal reagents, and dedicated lab equipment in the hands of trained users to achieve an outstanding overall performance of the realized system. Based on results from comprehensive signaling protein and pathway profiling studies targeted for preclinical drug efficacy testing and development, it gives an overview of application performance by means of coefficients of variation (CVs) that can be achieved for assay signals from technical and biological sample replicates with this state-of-the-art integrated RPPA platform and process.The Zeptosens RPPA platform has proven to provide valuable biological information with a high level of confidence and has shown its validity in generating sound mechanistic as well as prognostic and predictive information when analyzing cell and tissue materials on the functional protein level.

Harmonization of pipeline for preclinical multicenter plasma protein and miRNA biomarker discovery in a rat model of post-traumatic epileptogenesis

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is an international, multicenter, multidisciplinary study aimed at preventing epileptogenesis (EpiBioS4Rx: https://epibios.loni.usc.edu/). One of the study's major objectives is the discovery of diagnostic, prognostic, and predictive plasma protein and microRNA (miRNA) biomarkers that are sensitive, specific, and translatable to the human condition. Epilepsy due to structural brain abnormalities, secondary to neurological insults such as traumatic brain injury (TBI), currently represents ∼50% of all epilepsy cases. In the preclinical EpiBioS4Rx study, TBI was induced in adult male Sprague Dawley rats using a standardized protocol for lateral fluid-percussion injury. Whole blood was collected from the tail vein at baseline and 2, 9 and 30 days post-injury and processed for plasma separation. Biomaterial properties, sample preparation and integrity, and choice of analysis platform can significantly impact measured marker levels and, in turn, interpretation with respect to injury and/or other variables. We present here the results of procedural harmonization for the first 320 rats included in the EpiBioS4Rx study study, from three international research centers, and preliminary proteomic and miRNA analyses. We also discuss experimental considerations for establishing rigorous quality controls with the goal of harmonizing operating procedures across study sites, and delivering high-quality specimens for preclinical biomarker discovery in a rat model of post-traumatic epilepsy (PTE).

Quantitative screening of serum protein biomarkers by reverse phase protein arrays

Screening biomarkers in serum samples for different diseases has always been of great interest because it presents an early, reliable, and, most importantly, noninvasive means of diagnosis and prognosis. Reverse phase protein arrays (RPPAs) are a high-throughput platform that can measure single or limited sets of proteins from thousands of patients' samples in parallel. They have been widely used for detection of signaling molecules involved in diseases, especially cancers, and related regulation pathways in cell lysates. However, this approach has been difficult to adapt to serum samples. Previously, we developed a sensitive method called the enhanced protein array to quantitatively measure serum protein levels from large numbers of patient samples. Here, we further refine the technology on several fronts: 1. simplifying the experimental procedure; 2. optimizing multiple parameters to make the assay more robust, including the support matrix, signal reporting method, background control, and antibody validation; and 3. establishing a method for more accurate quantification. Using this technology, we quantitatively measured the expression levels of 10 proteins: alpha-fetoprotein (AFP), beta 2 microglobulin (B2M), Carcinoma Antigen 15-3(CA15-3), Carcinoembryonic antigen (CEA), golgi protein 73 (GP73), Growth differentiation factor 15 (GDF15), Human Epididymis Protein 4 (HE4), Insulin Like Growth Factor Binding Protein 2 (IGFBP2), osteopontin (OPN) and Beta-type platelet-derived growth factor receptor (PDGFRB) from serum samples of 132 hepatocellular carcinoma (HCC) patients and 78 healthy volunteers. We found that 6 protein expression levels are significantly increased in HCC patients. Statistical and bioinformatical analysis has revealed decent accuracy rates of individual proteins, ranging from 0.617 (B2M) to 0.908 (AFP) as diagnostic biomarkers to distinguish HCC from healthy controls. The combination of these 6 proteins as a specific HCC signature yielded a higher accuracy of 0.923 using linear discriminant analysis (LDA), logistic regression (LR), random forest (RF) and support vector machine (SVM) predictive model analyses. Our work reveals promise for using reverse phase protein arrays for biomarker discovery and validation in serum samples.

Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics

For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.

Reverse Phase Protein Microarrays

While genes and RNA encode information about cellular status, proteins are considered the engine of the cellular machine, as they are the effective elements that drive all cellular functions including proliferation, migration, differentiation, and apoptosis. Consequently, investigations of the cellular protein network are considered a fundamental tool for understanding cellular functions.Alteration of the cellular homeostasis driven by elaborate intra- and extracellular interactions has become one of the most studied fields in the era of personalized medicine and targeted therapy. Increasing interest has been focused on developing and improving proteomic technologies that are suitable for analysis of clinical samples. In this context, reverse-phase protein microarrays (RPPA) is a sensitive, quantitative, high-throughput immunoassay for protein analyses of tissue samples, cells, and body fluids.RPPA is well suited for broad proteomic profiling and is capable of capturing protein activation as well as biochemical reactions such as phosphorylation, glycosylation, ubiquitination, protein cleavage, and conformational alterations across hundreds of samples using a limited amount of biological material. For these reasons, RPPA represents a valid tool for protein analyses and generates data that help elucidate the functional signaling architecture through protein-protein interaction and protein activation mapping for the identification of critical nodes for individualized or combinatorial targeted therapy.

Integrating network reconstruction with mechanistic modeling to predict cancer therapies

Signal transduction networks are often rewired in cancer cells. Identifying these alterations will enable more effective cancer treatment. We developed a computational framework that can identify, reconstruct, and mechanistically model these rewired networks from noisy and incomplete perturbation response data and then predict potential targets for intervention. As a proof of principle, we analyzed a perturbation data set targeting epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF1R) pathways in a panel of colorectal cancer cells. Our computational approach predicted cell line-specific network rewiring. In particular, feedback inhibition of insulin receptor substrate 1 (IRS1) by the kinase p70S6K was predicted to confer resistance to EGFR inhibition, suggesting that disrupting this feedback may restore sensitivity to EGFR inhibitors in colorectal cancer cells. We experimentally validated this prediction with colorectal cancer cell lines in culture and in a zebrafish (Danio rerio) xenograft model.

Characterization of protein expression levels with label-free detected reverse phase protein arrays

In reverse-phase protein arrays (RPPA), one immobilizes complex samples (e.g., cellular lysate, tissue lysate or serum etc.) on solid supports and performs parallel reactions of antibodies with immobilized protein targets from the complex samples. In this work, we describe a label-free detection of RPPA that enables quantification of RPPA data and thus facilitates comparison of studies performed on different samples and on different solid supports. We applied this detection platform to characterization of phosphoserine aminotransferase (PSAT) expression levels in Acanthamoeba lysates treated with artemether and the results were confirmed by Western blot studies.

Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer

The majority of the targeted therapeutic agents in clinical use target proteins and protein function. Although DNA and RNA analyses have been used extensively to identify novel targets and patients likely to benefit from targeted therapies, these are indirect measures of the levels and functions of most therapeutic targets. More importantly, DNA and RNA analysis is ill-suited for determining the pharmacodynamic effects of target inhibition. Assessing changes in protein levels and function is the most efficient way to evaluate the mechanisms underlying sensitivity and resistance to targeted agents. Understanding these mechanisms is necessary to identify patients likely to benefit from treatment and to develop rational drug combinations to prevent or bypass therapeutic resistance. There is an urgent need for a robust approach to assess protein levels and protein function in model systems and across patient samples. While "shot gun" mass spectrometry can provide in-depth analysis of proteins across a limited number of samples, and emerging approaches such as multiple reaction monitoring have the potential to analyze candidate markers, mass spectrometry has not entered into general use because of the high cost, requirement of extensive analysis and support, and relatively large amount of material needed for analysis. Rather, antibody-based technologies, including immunohistochemistry, radioimmunoassays, enzyme-linked immunosorbent assays (ELISAs), and more recently protein arrays, remain the most common approaches for multiplexed protein analysis. Reverse-phase protein array (RPPA) technology has emerged as a robust, sensitive, cost-effective approach to the analysis of large numbers of samples for quantitative assessment of key members of functional pathways that are affected by tumor-targeting therapeutics. The RPPA platform is a powerful approach for identifying and validating targets, classifying tumor subsets, assessing pharmacodynamics, and identifying prognostic and predictive markers, adaptive responses and rational drug combinations in model systems and patient samples. Its greatest utility has been realized through integration with other analytic platforms such as DNA sequencing, transcriptional profiling, epigenomics, mass spectrometry, and metabolomics. The power of the technology is becoming apparent through its use in pathology laboratories and integration into trial design and implementation.

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

Serum dickkopf-1 is a novel serological biomarker for the diagnosis and prognosis of pancreatic cancer

Purpose

To identify whether Dickkopf-1 (DKK1) could be a potential biomarker for early detection and prognosis in patients with pancreatic cancer (PC).

Methods

Serum was collected from 140 patients with pancreatic adenocarcinoma and 92 control patients without pancreatic adenocarcinoma. Serological levels of DKK1 were examined by enzyme-linked immunosorbent assay (ELISA). The sensitivity and specificity was compared with carbohydrate antigen 19-9 (CA19-9). A 2-year follow-up was monitored to evaluate the correlation between DKK1 serum levels and overall survival. The expression of DKK1 in PC tumor tissues was also evaluated using immunohistochemistry staining.

Results

Serum levels of DKK1 and CA19-9 were elevated in PC patients in the early-stage cases. These levels increased with the advancement of clinical stage. There was significant difference in DKK1 serum levels between early and advanced PC stages. Receiver operating characteristic curve (ROCC) analysis showed that DKK1 was significantly better than CA19-9 in differentiating patients with PC from the controls (area under the curve (AUC) 0.919 versus 0.853, respectively), especially in distinguishing early-stage cancer from chronic pancreatitis (CP). The expression of DKK1 in PC tissues correlated with its expression in serum samples. The overall survival rate was 24.4% in the group with higher DKK1 levels and was found to be significantly different from the group with lower DKK1 levels (33.3%).

Conclusion

DKK1 may be a novel diagnostic/prognostic biomarker for PC.

Immunocapture strategies in translational proteomics

Aiming at clinical studies of human diseases, antibody-assisted assays have been applied to biomarker discovery and toward a streamlined translation from patient profiling to assays supporting personalized treatments. In recent years, integrated strategies to couple and combine antibodies with mass spectrometry-based proteomic efforts have emerged, allowing for novel possibilities in basic and clinical research. Described in this review are some of the field's current and emerging immunocapture approaches from an affinity proteomics perspective. Discussed are some of their advantages, pitfalls and opportunities for the next phase in clinical and translational proteomics.

Assessment of a panel of tumor markers for the differential diagnosis of benign and malignant effusions by well-based reverse phase protein array

Background

The differential diagnosis of benign and malignant effusion is often hampered by low cell content or insufficiently preserved tumor cells. In this study, we evaluated the combined diagnostic value of six tumor markers measured by well-based reverse-phase protein array (RPPA) for diagnosis of malignant effusion.

Methods

A total of 114 patients (46 with malignant effusions, 32 with probable malignant effusions, and 36 with benign effusions) were enrolled. Expressional levels of MUC1, EMA, Pan-CK, HSP90, TGF-β and CA125 were determined by well-based RPPA.

Results

Median relative expression of MUC1, Pan-CK and EMA were significantly higher in malignant effusion than those in probable malignant or benign (p < 0.001, p = 0.003, p < 0.001, respectively), whereas the level of TGF-β in malignant effusions were significantly lower than that in the other groups (p = 0.005). For predicting malignancy, EMA presented the best areas under the curve of 0.728 followed by MUC1 of 0.701. The sensitivity of 52.0% for MUC1 and 48.0% for EMA were not better than cytology. However, sensitivity, negative predictive value, and accuracy of the tumor marker panel were better than cytology by 14.7%, 7.5%, and 6.1%, respectively.

Conclusions

Tumor marker panel measured by well-based RPPA showed values in the differential diagnosis between benign and malignant effusions. Further large scale studies need to be performed to evaluate the utility of this panel of markers.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1433424467160224

Electronic supplementary material

The online version of this article (doi:10.1186/s13000-015-0290-4) contains supplementary material, which is available to authorized users.

Serial analysis of 38 proteins during the progression of human breast tumor in mice using an antibody colocalization microarray

Proteins in serum or plasma hold great potential for use in disease diagnosis and monitoring. However, the correlation between tumor burden and protein biomarker concentration has not been established. Here, using an antibody colocalization microarray, the protein concentration in serum was measured and compared with the size of mammary xenograft tumors in 11 individual mice from the time of injection; seven blood samples were collected from each tumor-bearing mouse as well as control mice on a weekly basis. The profiles of 38 proteins detected in sera from these animals were analyzed by clustering, and we identified 10 proteins with the greatest relative increase in serum concentration that correlated with growth of the primary mammary tumor. To evaluate the diagnosis of cancer based on these proteins using either an absolute threshold (i.e. a concentration cutoff) or self-referenced differential threshold based on the increase in concentration before cell injection, receiver operating characteristic curves were produced for 10 proteins with increased concentration, and the area under curve was calculated for each time point based on a single protein or on a panel of proteins, in each case showing a rapid increase of the area under curve. Next, the sensitivity and specificity of individual and optimal protein panels were calculated, showing high accuracy as early as week 2. These results provide a foundation for studies of tumor growth through measuring serial changes of protein concentration in animal models.

An 8 minute colorimetric paper-based reverse phase vertical flow serum microarray for screening of hyper IgE syndrome

A reverse phase serum array with the capacity of simultaneous detection in 113 samples was developed and optimized for a vertical flow 8-minute colorimetric assay detecting IgE.

Development of a robust classifier for quality control of reverse-phase protein arrays

MOTIVATION

High-throughput reverse-phase protein array (RPPA) technology allows for the parallel measurement of protein expression levels in approximately 1000 samples. However, the many steps required in the complex protocol (sample lysate preparation, slide printing, hybridization, washing and amplified detection) may create substantial variability in data quality. We are not aware of any other quality control algorithm that is tuned to the special characteristics of RPPAs.

RESULTS

We have developed a novel classifier for quality control of RPPA experiments using a generalized linear model and logistic function. The outcome of the classifier, ranging from 0 to 1, is defined as the probability that a slide is of good quality. After training, we tested the classifier using two independent validation datasets. We conclude that the classifier can distinguish RPPA slides of good quality from those of poor quality sufficiently well such that normalization schemes, protein expression patterns and advanced biological analyses will not be drastically impacted by erroneous measurements or systematic variations.

AVAILABILITY AND IMPLEMENTATION

The classifier, implemented in the "SuperCurve" R package, can be freely downloaded at http://bioinformatics.mdanderson.org/main/OOMPA:Overview or http://r-forge.r-project.org/projects/supercurve/. The data used to develop and validate the classifier are available at http://bioinformatics.mdanderson.org/MOAR.

Realizing the promise of reverse phase protein arrays for clinical, translational, and basic research: a workshop report: the RPPA (Reverse Phase Protein Array) society

Reverse phase protein array (RPPA) technology introduced a miniaturized "antigen-down" or "dot-blot" immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a well-accepted reference standard exists) abundance of total protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories. Advances in RPPA technology now offer scientists the opportunity to quantify protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: preservation and optimization of pre-analytical sample quality, application of validated high-affinity and specific antibody (or other protein affinity) detection reagents, dedicated informatics solutions to ensure accurate and robust quantification of protein analytes, and quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments. In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.

Semi-quantitative measurement of specific proteins in human cumulus cells using reverse phase protein array

BACKGROUND

The ability to predict the developmental and implantation ability of embryos remains a major goal in human assisted-reproductive technology (ART) and most ART laboratories use morphological criteria to evaluate the oocyte competence despite the poor predictive value of this analysis. Transcriptomic and proteomic approaches on somatic cells surrounding the oocyte (granulosa cells, cumulus cells [CCs]) have been proposed for the identification of biomarkers of oocyte competence. We propose to use a Reverse Phase Protein Array (RPPA) approach to investigate new potential biomarkers of oocyte competence in human CCs at the protein level, an approach that is already used in cancer research to identify biomarkers in clinical diagnostics.

METHODS

Antibodies targeting proteins of interest were validated for their utilisation in RPPA by measuring siRNA-mediated knockdown efficiency in HEK293 cells in parallel with Western blotting (WB) and RPPA from the same lysates. The proteins of interests were measured by RPPA across 13 individual human CCs from four patients undergoing intracytoplasmic sperm injection procedure.

RESULTS

The knockdown efficiency of VCL, RGS2 and SRC were measured in HEK293 cells by WB and by RPPA and were acceptable for VCL and SRC proteins. The antibodies targeting these proteins were used for their detection in human CCs by RPPA. The detection of protein VCL, SRC and ERK2 (by using an antibody already validated for RPPA) was then carried out on individual CCs and signals were detected for each individual sample. After normalisation by VCL, we showed that the level of expression of ERK2 was almost the same across the 13 individual CCs while the level of expression of SRC was different between the 13 individual CCs of the four patients and between the CCs from one individual patient.

CONCLUSIONS

The exquisite sensitivity of RPPA allowed detection of specific proteins in individual CCs. Although the validation of antibodies for RPPA is labour intensive, RRPA is a sensitive and quantitative technique allowing the detection of specific proteins from very small quantities of biological samples. RPPA may be of great interest in clinical diagnostics to predict the oocyte competence prior to transfer of the embryo using robust protein biomarkers expressed by CCs.

Utility, reliability and reproducibility of immunoassay multiplex kits

Multiplex technologies are becoming increasingly important in biomarker studies as they enable patterns of biomolecules to be examined, which provide a more comprehensive depiction of disease than individual biomarkers. They are crucial in deciphering these patterns, but it is essential that they are endorsed for reliability, reproducibility and precision. Here we outline the theoretical basis of a variety of multiplex technologies: Bead-based multiplex immunoassays (i.e. Cytometric Bead Arrays, Luminex™ and Bio-Plex Pro™), microtitre plate-based arrays (i.e. Mesoscale Discovery (MSD) and Quantsys BioSciences QPlex), Slide-based Arrays (i.e. FastQuant™) and reverse phase protein arrays. Their utility, reliability and reproducibility are discussed.

Biomarker Discovery of Pancreatic and Gastrointestinal Cancer by 2DICAL: 2-Dimensional Image-Converted Analysis of Liquid Chromatography and Mass Spectrometry

Biomarkers tested by blood sample are of great use to clinicians as they provide useful information to aid an early and accurate diagnosis. Comprehensive “omics” studies are expected to facilitate the identification of such new biomarkers, and much research is being performed in this area. Our proteomics analysis system of 2-dimensional image-converted analysis of liquid chromatography and mass spectrometry (2DICAL) has successfully identified several new blood biomarkers from the clinical blood samples of pancreatic and colorectal cancer patients.

Combining imaging and pathway profiling: an alternative approach to cancer drug discovery

Conventional drug discovery strategies are typically 'target centric' based on the selection of lead compounds with optimised 'on-target' potency and selectivity profiles. However, high-attrition rates are often the result of compensatory or redundant cancer mechanisms and the fact that tumours do not find it difficult to escape inhibition of a single pathway. In this article, we highlight two emerging and complimentary technologies; namely phenotypic imaging and post-translational pathway profiling, which when combined with relevant disease models can provide pharmacodiagnostic and drug combination strategies that predict and counteract inherent and adaptive drug resistance. The implementation of such approaches at early stages of the drug discovery process enables more informed decisions on candidate drug selection and how to maximise and predict efficacy before clinical development.

Reno: regularized non-parametric analysis of protein lysate array data

MOTIVATION

The reverse-phase protein lysate arrays have been used to quantify the relative expression levels of a protein in a number of cellular samples simultaneously. To avoid quantification bias due to mis-specification of commonly used parametric models, a nonparametric approach based on monotone response curves may be used. The existing methods, however, aggregate the protein concentration levels of replicates of each sample, and therefore fail to account for within-sample variability.

RESULTS

We propose a method of regularization on protein concentration estimation at the level of individual dilution series to account for within-sample or within-group variability. We use an efficient algorithm to optimize an approximate objective function, with a data-adaptive approach to choose the level of shrinkage. Simulation results show that the proposed method quantifies protein concentration levels well. We show through the analysis of protein lysate array data from cell lines of different cancer groups that accounting for within-sample variability leads to better statistical analysis.

AVAILABILITY

Code written in statistical programming language R is available at: http://odin.mdacc.tmc.edu/~jhhu/Reno

cMET and phospho-cMET protein levels in breast cancers and survival outcomes

PURPOSE

To evaluate cMET (mesenchymal-epithelial transition factor gene) and phospho-cMET (p-cMET) levels in breast cancer subtypes and its impact on survival outcomes.

EXPERIMENTAL DESIGN

We measured protein levels of cMET and p-cMET in 257 breast cancers using reverse phase protein array. Regression tree method and Martingale residual plots were applied to find best cutoff point for high and low levels. Kaplan-Meier survival curves were used to estimate relapse-free (RFS) and overall (OS) survival. Cox proportional hazards models were fit to determine associations of cMET/p-cMET with outcomes after adjustment for other characteristics.

RESULTS

Median age was 51 years. There were 140 (54.5%) hormone receptor (HR) positive, 53 (20.6%) HER2 positive, and 64 (24.9%) triple-negative tumors. Using selected cutoffs, 181 (70.4%) and 123 (47.9%) cancers had high levels of cMET and p-cMET, respectively. There were no significant differences in mean expression of cMET (P < 0.128) and p-cMET (P < 0.088) by breast cancer subtype. Dichotomized cMET and p-cMET level was a significant prognostic factor for RFS [HR: 2.44, 95% confidence interval (CI): 1.34-4.44, P = 0.003 and HR: 1.64, 95% CI: 1.04-2.60, P = 0.033] and OS (HR: 3.18, 95% CI: 1.43-7.11, P = 0.003 and HR: 1.92, 95% CI: 1.08-3.44, P = 0.025). Within breast cancer subtypes, high cMET levels were associated with worse RFS (P = 0.014) and OS (P = 0.006) in HR-positive tumors, and high p-cMET levels were associated with worse RFS (P = 0.019) and OS (P = 0.014) in HER2-positive breast cancers. In multivariable analysis, patients with high cMET had a significantly higher risk of recurrence (HR: 2.06, 95% CI: 1.08-3.94, P = 0.028) and death (HR: 2.81, 95% CI: 1.19-6.64, P = 0.019). High p-cMET level was associated with higher risk of recurrence (HR: 1.79, 95% CI: 1.08-2.95.77, P = 0.020).

CONCLUSIONS

High levels of cMET and p-cMET were seen in all breast cancer subtypes and correlated with poor prognosis.

Reverse-phase protein microarrays

Cancer is the consequence of intra- and extracellular signaling network deregulation that derives from alteration of genetic and proteomic cellular homeostasis. Mapping the individual molecular circuitry of a patient's tumor cells is the starting point for rational personalized therapy.While genes and RNA encode information about cellular status, proteins are considered the engine of the cellular machine, as they are the effective elements that drive cellular functions, such as proliferation, migration, differentiation, and apoptosis. Consequently, investigations of the cellular protein network are considered a fundamental tool to understand cellular functions. In the last decades, increasing interest has been focused on the improvement of new technologies for proteomic analysis. In this context, reverse-phase protein microarrays (RPMAs) have been developed to study and analyze posttranslational modifications that are responsible for principal cell functions and activities. This innovative technology allows the investigation of protein activation as a consequence of protein-protein interaction or biochemical reactions, such as phosphorylation, glycosylation, ubiquitination, protein cleavage, and conformational alterations.Intracellular balance is carefully conserved by constant rearrangements of proteins through the activity of a series of kinases and phosphatases. Therefore, knowledge of the key cellular signaling cascades reveal information regarding the cellular processes driving a tumor's growth (such as cellular survival, proliferation, invasion, and cell death) and response to treatment.Alteration to cellular homeostasis, driven by elaborate intra- and extracellular interactions, has become one of the most studied fields in the era of personalized medicine and targeted therapy. RPMA technology is a valid tool that can be applied to protein analysis of several diseases for the potential to generate protein interaction and activation maps that lead to the identification of critical nodes for individualized or combinatorial target therapy.

The Role of Proteomics in the Diagnosis and Treatment of Women's Cancers: Current Trends in Technology and Future Opportunities

Technological and scientific innovations over the last decade have greatly contributed to improved diagnostics, predictive models, and prognosis among cancers affecting women. In fact, an explosion of information in these areas has almost assured future generations that outcomes in cancer will continue to improve. Herein we discuss the current status of breast, cervical, and ovarian cancers as it relates to screening, disease diagnosis, and treatment options. Among the differences in these cancers, it is striking that breast cancer has multiple predictive tests based upon tumor biomarkers and sophisticated, individualized options for prescription therapeutics while ovarian cancer lacks these tools. In addition, cervical cancer leads the way in innovative, cancer-preventative vaccines and multiple screening options to prevent disease progression. For each of these malignancies, emerging proteomic technologies based upon mass spectrometry, stable isotope labeling with amino acids, high-throughput ELISA, tissue or protein microarray techniques, and click chemistry in the pursuit of activity-based profiling can pioneer the next generation of discovery. We will discuss six of the latest techniques to understand proteomics in cancer and highlight research utilizing these techniques with the goal of improvement in the management of women's cancers.