Reverse phase protein microarrays advance to use in clinical trials

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

ErbB2pY -1248 as a predictive biomarker for Parkinson's disease based on research with RPPA technology and in vivo verification

AIMS

This study aims to reveal a promising biomarker for Parkinson's disease (PD) based on research with reverse phase protein array (RPPA) technology for the first time and in vivo verification, which gains time for early intervention in PD, thus increasing the effectiveness of treatment and reducing disease morbidity.

METHODS AND RESULTS

We employed RPPA technology which can assess both total and post-translationally modified proteins to identify biomarker candidates of PD in a cellular PD model. As a result, the phosphorylation (pY-1248) of the epidermal growth factor receptor (EGFR) ErbB2 is a promising biomarker candidate for PD. In addition, lapatinib, an ErbB2 tyrosine kinase inhibitor, was used to verify this PD biomarker candidate in vivo. We found that lapatinib-attenuated dopaminergic neuron loss and PD-like behavior in the zebrafish PD model. Accordingly, the expression of ErbB2pY-1248 significantly increased in the MPTP-induced mouse PD model. Our results suggest that ErbB2pY-1248 is a predictive biomarker for PD.

CONCLUSIONS

In this study, we found that ErbB2pY-1248 is a predictive biomarker of PD by using RPPA technology and in vivo verification. It offers a new perspective on PD diagnosing and treatment, which will be essential in identifying individuals at risk of PD. In addition, this study provides new ideas for digging into biomarkers of other neurodegenerative diseases.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays

Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, immobilization techniques, and detection strategies. The aim of this review is to explore the development of biomolecule-based microarray applications since 2018 onwards. Here, we have covered a different array of printing strategies, substrate surface modification, biomolecule immobilization strategies, detection techniques, and biomolecule-based microarray applications. The period of 2018-2022 focused on using biomolecule-based microarrays for the identification of biomarkers, detection of viruses, differentiation of multiple pathogens, etc. A few potential future applications of microarrays could be for personalized medicine, vaccine candidate screening, toxin screening, pathogen identification, and posttranslational modifications.

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.

Biomarkers in Glycogen Storage Diseases: An Update

Glycogen storage diseases (GSDs) are a group of 19 hereditary diseases caused by a lack of one or more enzymes involved in the synthesis or degradation of glycogen and are characterized by deposits or abnormal types of glycogen in tissues. Their frequency is very low and they are considered rare diseases. Except for X-linked type IX, the different types are inherited in an autosomal recessive pattern. In this study we reviewed the literature from 1977 to 2020 concerning GSDs, biomarkers, and metabolic imbalances in the symptoms of some GSDs. Most of the reported studies were performed with very few patients. Classification of emerging biomarkers between different types of diseases (hepatics GSDs, McArdle and PDs and other possible biomarkers) was done for better understanding. Calprotectin for hepatics GSDs and urinary glucose tetrasaccharide for Pompe disease have been approved for clinical use, and most of the markers mentioned in this review only need clinical validation, as a final step for their routine use. Most of the possible biomarkers are implied in hepatocellular adenomas, cardiomyopathies, in malfunction of skeletal muscle, in growth retardation, neutropenia, osteopenia and bowel inflammation. However, a few markers have lost interest due to a great variability of results, which is the case of biotinidase, actin alpha 2, smooth muscle, aorta and fibroblast growth factor receptor 4. This is the first review published on emerging biomarkers with a potential application to GSDs.

Reverse Phase Protein Arrays

Reverse phase protein arrays (RPPA) are used to quantify proteins and protein posttranslational modifications in cellular lysates and body fluids. RPPA technology is suitable for biomarker discovery, protein pathway profiling, functional phenotype analysis, and drug discovery mechanism of action. The principles of RPPA technology are (a) immobilizing protein-containing specimens on a coated slide in discrete spots, (b) antibody recognition of proteins, (c) amplification chemistries to detect the protein-antibody complex, and (d) quantifying spot intensity. Construction of a RPPA begins with the robotic liquid transfer of protein-containing specimens from microtiter plates onto nitrocellulose-coated slides. The robotic arrayer deposits each sample as discrete spots in an array format. Specimens, controls, and calibrators are printed on each array, thus providing a complete calibrated assay on a single slide. Each RPPA slide is subsequently probed with catalyzed signal amplification chemistries and a single primary antibody, a secondary antibody, and either fluorescent or colorimetric dyes. The focus of this chapter is to describe RPPA detection and imaging using a colorimetric (diaminobenzidine (DAB)) detection strategy.

Profiling Autoantibody Responses to Devise Novel Diagnostic and Prognostic Markers Using High-Density Protein Microarrays

Protein microarrays are a diverse and high-throughput platform for screening biomolecular interactions, autoantigens, and protein expression profiles across tissues, etc. Autoantibodies produced against aberrant protein expression are often observed in malignancies which makes protein microarrays a powerful platform to elucidate biomarkers of translational interest. Early diagnosis of malignancies is an enduring clinical problem that has a direct impact on disease prognosis. Here, we provide an overview of a method employed to screen autoantibodies using patient sera in brain tumors. In case of brain malignancies, early diagnosis is particularly challenging and often requires highly invasive brain biopsies as a confirmatory test. This chapter summarizes the various considerations for applying a serum-based autoantibody biomarker discovery pipeline that could provide a minimally invasive initial diagnostic screen, potentiating classical diagnostic approaches.

Antibody Printing Technologies

Antibody microarrays are routinely employed in the lab and in the clinic for studying protein expression, protein-protein, and protein-drug interactions. The microarray format reduces the size scale at which biological and biochemical interactions occur, leading to large reductions in reagent consumption and handling times while increasing overall experimental throughput. Specifically, antibody microarrays, as a platform, offer a number of different advantages over traditional techniques in the areas of drug discovery and diagnostics. While a number of different techniques and approaches have been developed for creating micro and nanoscale antibody arrays, issues relating to sensitivity, cost, and reproducibility persist. The aim of this review is to highlight current state-of the-art techniques and approaches for creating antibody arrays by providing latest accounts of the field while discussing potential future directions.

The effects of sample handling on proteomics assessed by reverse phase protein arrays (RPPA): Functional proteomic profiling in leukemia

Reverse phase protein arrays (RPPA) can assess protein expression and activation states in large numbers of samples (n > 1000) and evidence suggests feasibility in the setting of multi-institution clinical trials. Despite evidence in solid tumors, little is known about protein stability in leukemia. Proteins collected from leukemia cells in blood and bone marrow biopsies must be sufficiently stable for analysis. Using 58 leukemia samples, we initially assessed protein/phospho-protein integrity for the following preanalytical variables: 1) shipping vs local processing, 2) temperature (4 °C vs ambient temperature), 3) collection tube type (heparin vs Cell Save (CS) preservation tubes), 4) treatment effect (pre- vs post-chemotherapy) and 5) transit time. Next, we assessed 1515 samples from the Children's Oncology Group Phase 3 AML clinical trial (AAML1031, NCT01371981) for the effects of transit time and tube type. Protein expression from shipped blood samples was stable if processed in ≤72 h. While protein expression in pre-chemotherapy samples was stable in both heparin and CS tubes, post-chemotherapy samples were stable in only CS tubes. RPPA protein extremes is a successful quality control measure to identify and exclude poor quality samples. These data demonstrate that a majority of shipped proteins can be accurately assessed using RPPA. SIGNIFICANCE: RPPA can assess protein abundance and activation states in large numbers of samples using small amounts of material, making this method ideal for use in multi-institution clinical trials. However, there is little known about the effect of preanalytical handling variables on protein stability and the integrity of protein concentrations after sample collection and shipping. In this study, we used RPPA to assess preanalytical variables that could potentially affect protein concentrations. We found that the preanalytical variables of shipping, transit time, and temperature had minimal effects on RPPA protein concentration distributions in peripheral blood and bone marrow, demonstrating that these preanalytical variables could be successfully managed in a multi-site clinical trial setting.

Electronic cigarette vapour moderately stimulates pro-inflammatory signalling pathways and interleukin-6 production by human monocyte-derived dendritic cells

Dendritic cells (DCs) are professional antigen presenting cells that play a critical role in bridging innate and adaptive immunity. Numerous studies have shown that tobacco constituents present in conventional cigarettes affect the phenotype and function of DCs; however, no studies have examined the effects of vapour from E-cigarettes on human DCs. Here, the effects of E-cigarette vapour extract (ECVE) on the phenotype and function of DCs were investigated by creating an in vitro cell culture model using human monocyte-derived DCs (MoDCs). Immature DCs were generated from peripheral blood monocytes and mature DCs were then produced by treatment with LPS or Poly I:C for 24 h. For LPS-matured DCs, 3% ECVE treatment slightly suppressed HLA-DR and CD86 expression, whereas 1% ECVE treatment enhanced IL-6 production. The overall expression of 29 signalling molecules and other cytoplasmic proteins (mainly associated with DC activation) was significantly upregulated in immature DCs by 1% ECVE, and in LPS-treated DCs by 3% ECVE. In particular, the condition that induced IL-6 production also upregulated MAPK pathway activation. These findings indicate that E-cigarette vapour moderately affects human DCs, but the effects are less pronounced than those reported for tobacco smoke.

Metabolic reprogramming and disease progression in cancer patients

Genomics has contributed to the treatment of a fraction of cancer patients. However, there is a need to profile the proteins that define the phenotype of cancer and its pathogenesis. The reprogramming of metabolism is a major trait of the cancer phenotype with great potential for prognosis and targeted therapy. This review overviews the major changes reported in the steady-state levels of proteins of metabolism in primary carcinomas, paying attention to those enzymes that correlate with patients' survival. The upregulation of enzymes of glycolysis, pentose phosphate pathway, lipogenesis, glutaminolysis and the antioxidant defense is concurrent with the downregulation of mitochondrial proteins involved in oxidative phosphorylation, emphasizing the potential of mitochondrial metabolism as a promising therapeutic target in cancer. We stress that high-throughput quantitative expression profiling of differentially expressed proteins in large cohorts of carcinomas paired with normal tissues will accelerate translation of metabolism to a successful personalized medicine in cancer.

A new model isolates glioblastoma clonal interactions and reveals unexpected modes for regulating motility, proliferation, and drug resistance

Tumor clonal heterogeneity drives treatment resistance. But robust models are lacking that permit eavesdropping on the basic interaction network of tumor clones. We developed an in vitro, functional model of clonal cooperation using U87MG glioblastoma cells, which isolates fundamental clonal interactions. In this model pre-labeled clones are co-cultured to track changes in their individual motility, growth, and drug resistance behavior while mixed. This highly reproducible system allowed us to address a new class of fundamental questions about clonal interactions. We demonstrate that (i) a single clone can switch off the motility of the entire multiclonal U87MG cell line in 3D culture, (ii) maintenance of clonal heterogeneity is an intrinsic and influential cancer cell property, where clones coordinate growth rates to protect slow growing clones, and (iii) two drug sensitive clones can develop resistance de novo when cooperating. Furthermore, clonal communication for these specific types of interaction did not require diffusible factors, but appears to depend on cell-cell contact. This model constitutes a straightforward but highly reliable tool for isolating the complex clonal interactions that make up the fundamental "hive mind" of the tumor. It uniquely exposes clonal interactions for future pharmacological and biochemical studies.

Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting

BACKGROUND

Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance.

METHODS

Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured.

RESULTS

Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies.

CONCLUSIONS

Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.

Emerging proteomic biomarkers of X-linked muscular dystrophy

Introduction: Progressive skeletal muscle wasting is the manifesting symptom of Duchenne muscular dystrophy, an X-linked inherited disorder triggered by primary abnormalities in the DMD gene. The almost complete loss of dystrophin isoform Dp427 causes a multi-system pathology that features in addition to skeletal muscle weakness also late-onset cardio-respiratory deficiencies, impaired metabolism and abnormalities in the central nervous system. Areas covered: This review focuses on the mass spectrometry-based proteomic characterization of X-linked muscular dystrophy with special emphasis on the identification of novel biomarker candidates in skeletal muscle tissues, as well as non-muscle tissues and various biofluids. Individual sections focus on molecular and cellular aspects of the pathogenic changes in dystrophinopathy, proteomic workflows used in biomarker research, the proteomics of the dystrophin-glycoprotein complex and the potential usefulness of newly identified protein markers involved in fibre degeneration, fibrosis and inflammation. Expert opinion: The systematic application of large-scale proteomic surveys has identified a distinct cohort of both tissue- and biofluid-associated protein species with considerable potential for improving diagnostic, prognostic and therapy-monitoring procedures. Novel proteomic markers include components involved in fibre contraction, cellular signalling, ion homeostasis, cellular stress response, energy metabolism and the immune response, as well as maintenance of the cytoskeletal and extracellular matrix.

Proteomics for cancer drug design

Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.

Unlocking bone for proteomic analysis and FISH

Bone tissue is critically lagging behind soft tissues and biofluids in our effort to advance precision medicine. The main challenges have been accessibility and the requirement for deleterious decalcification processes that impact the fidelity of diagnostic histomorphology and hinder downstream analyses such as fluorescence in-situ hybridization (FISH). We have developed an alternative fixation chemistry that simultaneously fixes and decalcifies bone tissue. We compared tissue morphology, immunohistochemistry (IHC), cell signal phosphoprotein analysis, and FISH in 50 patient matched primary bone cancer cases that were either formalin fixed and decalcified, or theralin fixed with and without decalcification. Use of theralin improved tissue histomorphology, whereas overall IHC was comparable to formalin fixed, decalcified samples. Theralin-fixed samples showed a significant increase in protein and DNA extractability, supporting technologies such as laser-capture microdissection and reverse phase protein microarrays. Formalin-fixed bone samples suffered from a fixation artifact where protein quantification of β-actin directly correlated with fixation time. Theralin-fixed samples were not affected by this artifact. Moreover, theralin fixation enabled standard FISH staining in bone cancer samples, whereas no FISH staining was observed in formalin-fixed samples. We conclude that the use of theralin fixation unlocks the molecular archive within bone tissue allowing bone to enter the standard tissue analysis pipeline. This will have significant implications for bone cancer patients, in whom personalized medicine has yet to be implemented.

[Personalized oncology]

CLINICAL ISSUE

Innovative next generation sequencing (NGS) technologies and comprehensive sequencing investigations in large patient cohorts have paved the way for very promising personalized treatment strategies based on the molecular characteristics of individual tumors.

STANDARD TREATMENT

Targeted therapies, such as tyrosine kinase inhibitors, antibodies and modern immunotherapeutic approaches are well established as monotherapy and combination therapy for many hematological and oncological malignancies.

TREATMENT INNOVATIONS

A plethora of innovative therapies targeting various components of intracellular signaling cascades and effective mechanisms against oncogenes as well as the availability of NGS technologies enable personalized cancer treatment based on the molecular profiles of individual tumors and genetic stratification, within clinical trials.

DIAGNOSTIC WORK-UP

Comprehensive genetic approaches including cancer gene panel sequencing, whole exome, whole genome and transcriptome sequencing are carried out to a varying extent and particularly in the academic setting.

PERFORMANCE

Principally, a comprehensive characterization of tumors in addition to DNA and RNA sequencing that also incorporates epigenetic, metabolomic, and proteomic alterations would be desirable. A comprehensive clinical implementation of integrative, multidimensional genetic typing is, however, currently not possible.

ACHIEVEMENTS

It remains to be demonstrated whether these approaches will translate into significantly better outcomes for patients and whether they can be increasingly implemented in the routine diagnostic work-up.

PRACTICAL RECOMMENDATIONS

The selection of diagnostic tools in individual cases and the extent of genomic analyses in the clinical context, need to take the availability of methods as well as the present clinical situation into account.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Towards precision medicine: the role and potential of protein and peptide microarrays

Techniques to comprehensively analyze protein signatures are pivotal to unravel disease mechanisms, develop novel biomarkers and targeted therapies. In this frame, protein and peptide microarrays can play a major role in fuelling precision medicine.

Phosphoprotein patterns predict trametinib responsiveness and optimal trametinib sensitisation strategies in melanoma

Malignant melanoma is a highly aggressive form of skin cancer responsible for the majority of skin cancer-related deaths. Recent insight into the heterogeneous nature of melanoma suggests more personalised treatments may be necessary to overcome drug resistance and improve patient care. To this end, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified. In this study, we applied multiplex phosphoproteomic profiling across a panel of 24 melanoma cell lines with different disease-relevant mutations, to predict responsiveness to MEK inhibitor trametinib. Supported by multivariate statistical analysis and multidimensional pattern recognition algorithms, the responsiveness of individual cell lines to trametinib could be predicted with high accuracy (83% correct predictions), independent of mutation status. We also successfully employed this approach to case specifically predict whether individual melanoma cell lines could be sensitised to trametinib. Our predictions identified that combining MEK inhibition with selective targeting of c-JUN and/or FAK, using siRNA-based depletion or pharmacological inhibitors, sensitised resistant cell lines and significantly enhanced treatment efficacy. Our study indicates that multiplex proteomic analyses coupled with pattern recognition approaches could assist in personalising trametinib-based treatment decisions in the future.

Different mitochondrial genetic defects exhibit the same protein signature of metabolism in skeletal muscle of PEO and MELAS patients: A role for oxidative stress

A major challenge in mitochondrial diseases (MDs) is the identification of biomarkers that could inform of the mechanisms involved in the phenotypic expression of genetic defects. Herein, we have investigated the protein signature of metabolism and of the antioxidant response in muscle biopsies of clinically and genetically diagnosed patients with Progressive External Ophthalmoplegia due to single large-scale (PEO-sD) or multiple (PEO-mD) deletions of mtDNA and Mitochondrial Encephalopathy Lactic Acidosis and Stroke-like episode (MELAS) syndrome, and healthy donors. A high-throughput immunoassay technique that quantitates the expression of relevant proteins of glycolysis, glycogenolysis, pentose phosphate pathway, oxidative phosphorylation, pyruvate and fatty acid oxidation, tricarboxylic acid cycle and the antioxidant response in two large independent and retrospectively collected cohorts of PEO-sD, PEO-mD and MELAS patients revealed that despite the heterogeneity of the genetic alterations, the three MDs showed the same metabolic signatures in both cohorts of patients, which were highly divergent from those of healthy individuals. Linear Discriminant Analysis and Support Vector Machine classifier provided a minimum of four biomarkers to discriminate healthy from pathological samples. Regardless of the induction of a large number of enzymes involved in ameliorating oxidative stress, the down-regulation of mitochondrial superoxide dismutase (SOD2) and catalase expression favored the accumulation of oxidative damage in patients' proteins. Down-regulation of SOD2 and catalase expression in MD patients is not due to relevant changes in the availability of their mRNAs, suggesting that oxidative stress regulates the expression of the two enzymes post-transcriptionally. We suggest that SOD2 and catalase could provide specific targets to improve the detoxification of reactive oxygen species that affects muscle proteins in these patients.

Shining a light on cell signaling in leukemia through proteomics: relevance for the clinic

INTRODUCTION

Although cure rates for acute leukemia have steadily improved over the past decades, leukemia remains a deadly disease. Enhanced risk stratification and new therapies are needed to improve outcome. Extensive genetic analyses have identified many mutations that contribute to the development of leukemia. However, most mutations occur infrequently and most gene alterations have been difficult to target. Most patients have more than one driver mutation in combination with secondary mutations, that result in a leukemic transformation via the alteration of proteins. The proteomics of acute leukemia could more directly identify proteins to facilitate risk stratification, predict chemoresistance and aid selection of therapy. Areas covered: This review discusses aberrantly expressed proteins identified by mass spectrometry and reverse phase protein arrays and their relationship to survival. In addition, we will discuss proteins in the context of functionally related protein groups. Expert commentary: Proteomics is a powerful tool to analyze protein abundance and functional alterations simultaneously for large numbers of patients. In the forthcoming years, validation of tools to quickly assess protein levels to enable routine rapid profiling of proteins with differential abundance and functional activation may be used as adjuncts to aid in therapy selection and to provide additional prognostic insights.

Moving From Mutation to Actionability

The diffusion of high-throughput next-generation sequencing technologies has sustained massive parallel sequencing of tumor tissue providing a deep insight into tumor biology and advancement of personalized medicine. A substantial number of targeted agents have been investigated in gynecologic cancer and some have received U.S. Food and Drug Administration approval, like PARP inhibitors in ovarian cancer, bevacizumab in ovarian and cervical cancers, and pembrolizumab in microsatellite-unstable or mismatch repair-deficient endometrial cancer. To improve effectiveness of targeted therapy, identification of predictive biomarkers able to guide the selection of the correct drug for the correct patient is crucial. Different limitations must be addressed to favor a more rapid implementation of a genotyping approach in treatment selection, such as the possibility to easily assess tumor heterogeneity and clonal evolution along the disease trajectory and the need for innovative trial designs like adaptive or basket trials incorporating molecular features as selection criteria. A deep dive into the genomic features of exceptional responders may also favor better understanding of tumor biology, mechanism of action of a specific target agent, and identification or predictive biomarkers for subsequent tailored studies.

Stromal TRIM28-associated signaling pathway modulation within the colorectal cancer microenvironment

BACKGROUND

Stromal gene expression patterns predict patient outcomes in colorectal cancer. TRIM28 is a transcriptional co-repressor that regulates an abundance of genes through the KRAB domain family of transcription factors. We have previously shown that stromal expression of TRIM28 is a marker of disease relapse and poor survival in colorectal cancer. Here, we perform differential epithelium-stroma proteomic network analyses to characterize signaling pathways associated with TRIM28 within the tumor microenvironment.

METHODS

Reverse phase protein arrays were generated from laser capture micro-dissected carcinoma and stromal cells from fresh frozen colorectal cancer tissues. Phosphorylation and total protein levels were measured for 30 cancer-related signaling pathway endpoints. Strength and direction of associations between signaling endpoints were identified using Spearman's rank-order correlation analysis and compared to TRIM28 levels. Expression status of TRIM28 in tumor epithelium and stromal fibroblasts was assessed using IHC in formalin fixed tissue and the epithelium to stroma protein expression ratio method.

RESULTS

We found distinct proteomic networks in the epithelial and stromal compartments which were linked to expression levels of TRIM28. Low levels of TRIM28 in tumor stroma (high epithelium: stroma ratio) were found in 10 out of 19 cases. Upon proteomic network analyses, these stromal high ratio cases revealed moderate signaling pathway similarity exemplified by 76 significant Spearman correlations (ρ ≥ 0.75, p ≤ 0.01). Furthermore, low levels of stromal TRIM28 correlated with elevated MDM2 levels in tumor epithelium (p = 0.01) and COX-2 levels in tumor stroma (p = 0.002). Low TRIM28 epithelium to stroma ratios were associated with elevated levels of caspases 3 and 7 in stroma (p = 0.041 and p = 0.036) and an increased signaling pathway similarity in stromal cells with 81 significant Spearman correlations (ρ ≥ 0.75, p ≤ 0.01).

CONCLUSIONS

By dissecting TRIM28-associated pathways in stromal fibroblasts and epithelial tumor cells, we performed comprehensive proteomic analyses of molecular networks within the tumor microenvironment. We found modulation of several signaling pathways associated with TRIM28, which may be attributed to the pleiotropic properties of TRIM28 through its translational suppression of the family of KRAB domain transcription factors in tumor stromal compartments.

Dual-Color, Multiplex Analysis of Protein Microarrays for Precision Medicine

Generating molecular information in a clinically relevant time frame is the first hurdle to truly integrating precision medicine in health care. Reverse phase protein microarrays are being utilized in clinical trials for quantifying posttranslationally modified signal transduction proteins and cellular signaling pathways, allowing direct comparison of the activation state of proteins from multiple specimens, or individual patient specimens, within the same array. This technology provides diagnostic and therapeutic information critical to precision medicine. To enhance accessibility of this technology, two hurdles must be overcome: data normalization and data acquisition. Herein we describe an unamplified, dual-color signal detection methodology for reverse phase protein microarrays that allows multiplex, within spot data normalization, reduces data acquisition time, simplifies automated spot detection, and provides a stable signal output. This method utilizes Quantum Nanocrystal fluorophore labels (Qdot) substituted for organic fluorophores coupled with an imager (ArrayCAM) that captures images of the microarray rather than sequentially scanning the array. Streamlining and standardizing the data analysis steps with ArrayCAM high-resolution, dual mode chromogenic/fluorescent array imaging overcomes the data acquisition hurdle. The spot location and analysis algorithm provides certain parameter settings that can be tailored to the particular microarray type (fluorescent vs. colorimetric), resulting in greater than 99 % spot location sensitivity. The described method demonstrates equivalent sensitivity for a non-amplified Qdot immunoassay when using automated vs. manual immunostaining procedures.

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.

Effects of low dose ionizing radiation on DNA damage-caused pathways by reverse-phase protein array and Bayesian networks

Ionizing radiation (IR) causing damages to Deoxyribonucleic acid (DNA) constitutes a broad range of base damage and double strand break, and thereby, it induces the operation of relevant signaling pathways such as DNA repair, cell cycle control, and cell apoptosis. The goal of this paper is to study how the exposure to low dose radiation affects the human body by observing the signaling pathway associated with Ataxia Telangiectasia mutated (ATM) using Reverse-Phase Protein Array (RPPA) and isogenic human Ataxia Telangiectasia (A-T) cells under different amounts and durations of IR exposure. In order to verify which proteins could be involved in a DNA damage-caused pathway, only proteins that highly interact with each other under IR are selected by using correlation coefficient. The pathway inference is derived from learning Bayesian networks in combination with prior knowledge such as Protein-Protein Interactions (PPIs) and signaling pathways from well-known databases. Learning Bayesian networks is based on a score and search scheme that provides the highest scored network structure given a score function, and the prior knowledge is included in the score function as a prior probability by using Dempster-Shafer theory (DST). In this way, the inferred network can be more likely to be similar to already discovered pathways and consistent with confirmed PPIs for more reliable inference. The experimental results show which proteins are involved in signaling pathways under IR, how the inferred pathways are different under low and high doses of IR, and how the selected proteins regulate each other in the inferred pathways. As our main contribution, overall results confirm that low dose IR could cause DNA damage and thereby induce and affect related signaling pathways such as apoptosis, cell cycle, and DNA repair.

Glucocorticoid resistance is reverted by LCK inhibition in pediatric T-cell acute lymphoblastic leukemia

Pediatric T-acute lymphoblastic leukemia (T-ALL) patients often display resistance to glucocorticoid (GC) treatment. These patients, classified as prednisone poor responders (PPR), have poorer outcome than do the other pediatric T-ALL patients receiving a high-risk adapted therapy. Because glucocorticoids are administered to ALL patients during all the different phases of therapy, GC resistance represents an important challenge to improving the outcome for these patients. Mechanisms underlying resistance are not yet fully unraveled; thus our research focused on the identification of deregulated signaling pathways to point out new targeted approaches. We first identified, by reverse-phase protein arrays, the lymphocyte cell-specific protein-tyrosine kinase (LCK) as aberrantly activated in PPR patients. We showed that LCK inhibitors, such as dasatinib, bosutinib, nintedanib, and WH-4-023, are able to induce cell death in GC-resistant T-ALL cells, and remarkably, cotreatment with dexamethasone is able to reverse GC resistance, even at therapeutic drug concentrations. This was confirmed by specific LCK gene silencing and ex vivo combined treatment of cells from PPR patient-derived xenografts. Moreover, we observed that LCK hyperactivation in PPR patients upregulates the calcineurin/nuclear factor of activated T cells signaling triggering to interleukin-4 (IL-4) overexpression. GC-sensitive cells cultured with IL-4 display an increased resistance to dexamethasone, whereas the inhibition of IL-4 signaling could increase GC-induced apoptosis in resistant cells. Treatment with dexamethasone and dasatinib also impaired engraftment of leukemia cells in vivo. Our results suggest a quickly actionable approach to supporting conventional therapies and overcoming GC resistance in pediatric T-ALL patients.

Signaling pathway profiling using reverse-phase protein array and its clinical applications

INTRODUCTION

Increased accessibility to next-generation sequencing within the last decade has led to a paradigm shift in cancer treatment from one-size-fits-all medicine to precision medicine providing therapeutic strategies tailored to the requirements of individual patients. However, the effect of even the most successful agent yet tested is only transient, and durable efficacy has yet to be achieved. Genome- and transcriptome-based approaches cannot fully predict the diversity of protein expression patterns or post-translational modifications that directly contribute to cancer pathogenesis and physiology. This underscores the need for concordant proteomic analysis in the next stage of precision medicine. Areas covered: This review begins with an overview of the recent advances and trends in precision medicine that currently rely on genomics, and highlights the utility of antibody-based reverse-phase protein array (RPPA) technology as a proteomic tool in this context. Expert commentary: RPPA is well suited for pharmacodynamics analysis in view of its ability to precisely map signaling status using limited amounts of clinical samples. In addition, the cost-effectiveness and rapid turn-around time of the RPPA platform offer a substantial advantage over existing molecular profiling technologies in clinical settings.

Precision oncology based on omics data: The NCT Heidelberg experience

Precision oncology implies the ability to predict which patients will likely respond to specific cancer therapies based on increasingly accurate, high-resolution molecular diagnostics as well as the functional and mechanistic understanding of individual tumors. While molecular stratification of patients can be achieved through different means, a promising approach is next-generation sequencing of tumor DNA and RNA, which can reveal genomic alterations that have immediate clinical implications. Furthermore, certain genetic alterations are shared across multiple histologic entities, raising the fundamental question of whether tumors should be treated by molecular profile and not tissue of origin. We here describe MASTER (Molecularly Aided Stratification for Tumor Eradication Research), a clinically applicable platform for prospective, biology-driven stratification of younger adults with advanced-stage cancer across all histologies and patients with rare tumors. We illustrate how a standardized workflow for selection and consenting of patients, sample processing, whole-exome/genome and RNA sequencing, bioinformatic analysis, rigorous validation of potentially actionable findings, and data evaluation by a dedicated molecular tumor board enables categorization of patients into different intervention baskets and formulation of evidence-based recommendations for clinical management. Critical next steps will be to increase the number of patients that can be offered comprehensive molecular analysis through collaborations and partnering, to explore ways in which additional technologies can aid in patient stratification and individualization of treatment, to stimulate clinically guided exploratory research projects, and to gradually move away from assessing the therapeutic activity of targeted interventions on a case-by-case basis toward controlled clinical trials of genomics-guided treatments.

Proteomic Analysis Reveals Autophagy as Pro-Survival Pathway Elicited by Long-Term Exposure with 5-Azacitidine in High-Risk Myelodysplasia

Azacytidine (5-AZA) is the standard first-choice treatment for high-risk myelodysplasia (MDS) patients. However, the clinical outcome for those patients who interrupt treatment or whose disease failed to respond is very poor. In order to identify the cellular pathways that are modified by long-term exposure to 5-AZA, we evaluated key proteins associated with the autophagy pathway by reverse-phase microarray (RPPA). Comparing bone marrow mononucleated cells (BMMCs) obtained from 20 newly-diagnosed patients and after four 5-AZA cycles we found an increased autophagy signaling. We then evaluated ex-vivo the effect of the combination of 5-AZA with autophagy inhibitors chloroquine (CQ) and leupeptin. Since 5-AZA and CQ showed synergism due to an increase of basal autophagy after 5-AZA exposure, we adopted a sequential treatment treating BMMCs with 5 μM 5-AZA for 72 h followed by 10 μM CQ for 24 h and found increased apoptosis, associated to a reduction of G2M phase and increase in G0-G1 phase. Long-term exposure to 5-AZA induced the reduction of the autophagic marker SQSTM1/p62, reversible by CQ or leupeptin exposure. In conclusion, we identified autophagy as a compensatory pathway occurring in MDS-BM after long-term exposure to 5-AZA and we provided evidences that a sequential treatment of 5-AZA followed by CQ could improve 5-AZA efficacy, providing novel insight for tailored therapy in MDS patients progressing after 5-AZA therapy.

Harnessing the power of proteomics for identification of oncogenic, druggable signalling pathways in cancer

INTRODUCTION

Genomic and transcriptomic profiling of tumours has revolutionised our understanding of cancer. However, the majority of tumours possess multiple mutations, and determining which oncogene, or even which pathway, to target is difficult. Proteomics is emerging as a powerful approach to identify the functionally important pathways driving these cancers, and how they can be targeted therapeutically. Areas covered: The authors provide a technical overview of mass spectrometry based approaches for proteomic profiling, and review the current and emerging strategies available for the identification of dysregulated networks, pathways, and drug targets in cancer cells, with a key focus on the ability to profile cancer kinomes. The potential applications of mass spectrometry in the clinic are also highlighted. Expert opinion: The addition of proteomic information to genomic platforms - 'proteogenomics' - is providing unparalleled insight in cancer cell biology. Application of improved mass spectrometry technology and methodology, in particular the ability to analyse post-translational modifications (the PTMome), is providing a more complete picture of the dysregulated networks in cancer, and uncovering novel therapeutic targets. While the application of proteomics to discovery research will continue to rise, improved workflow standardisation and reproducibility is required before mass spectrometry can enter routine clinical use.

Pyruvate kinase M2 and the mitochondrial ATPase Inhibitory Factor 1 provide novel biomarkers of dermatomyositis: a metabolic link to oncogenesis

BACKGROUND

Metabolic alterations play a role in the development of inflammatory myopathies (IMs). Herein, we have investigated through a multiplex assay whether proteins of energy metabolism could provide biomarkers of IMs.

METHODS

A cohort of thirty-two muscle biopsies and forty plasma samples comprising polymyositis (PM), dermatomyositis (DM) and sporadic inclusion body myositis (sIBM) and control donors was interrogated with monoclonal antibodies against proteins of energy metabolism using reverse phase protein microarrays (RPPA).

RESULTS

When compared to controls the expression of the proteins is not significantly affected in the muscle of PM patients. However, the expression of β-actin is significantly increased in DM and sIBM in consistence with muscle and fiber regeneration. Concurrently, the expression of some proteins involved in glucose metabolism displayed a significant reduction in muscle of sIBM suggesting a repression of glycolytic metabolism in these patients. In contrasts to these findings, the expression of the glycolytic pyruvate kinase isoform M2 (PKM2) and of the mitochondrial ATPase Inhibitor Factor 1 (IF1) and Hsp60 were significantly augmented in DM when compared to other IMs in accordance with a metabolic shift prone to cancer development. PKM2 alone or in combination with other biomarkers allowed the discrimination of control and IMs with very high (>95%) sensitivity and specificity. Unfortunately, plasma levels of PKM2 were not significantly altered in DM patients to recommend its use as a non-invasive biomarker of the disease.

CONCLUSIONS

Expression of proteins of energy metabolism in muscle enabled discrimination of patients with IMs. RPPA identified the glycolysis promoting PKM2 and IF1 proteins as specific biomarkers of dermatomyositis, providing a biochemical link of this IM with oncogenesis.

Protein Arrays I: Antibody Arrays

Antibody arrays represent one of the very early protein array systems where antibodies are used to capture and detect target proteins in a high-throughput platform. The development of high-quality antibodies, nanomaterial-based novel detection probes, as well as innovative imaging technologies and computational tools has tremendously improved the sensitivity, specificity, and robustness of antibody arrays during the past decade. In this protocol we will incorporate the most updated innovations and developments of antibody arrays into the step-by-step experimental procedures. This includes antibody printing, sample preparation, array detection, as well as imaging and data analysis. Antibody array could be used for cytokine profiling or mapping of phosphorylation, glycosylation, or other post-translational modifications of target proteins.

The current state of the art of quantitative phosphoproteomics and its applications to diabetes research

Protein phosphorylation is a fundamental regulatory mechanism in many cellular processes and aberrant perturbation of phosphorylation has been implicated in various human diseases. Kinases and their cognate inhibitors have been considered as hotspots for drug development. Therefore, the emerging tools, which enable a system-wide quantitative profiling of phosphoproteome, would offer a powerful impetus in unveiling novel signaling pathways, drug targets and/or biomarkers for diseases of interest. This review highlights recent advances in phosphoproteomics, the current state of the art of the technologies and the challenges and future perspectives of this research area. Finally, some exemplary applications of phosphoproteomics in diabetes research are underscored.

Proteomic profiling predicts drug response to novel targeted anticancer therapeutics

Most recently approved anti-cancer drugs by the US FDA are targeted therapeutic agents and this represents an important trend for future anticancer therapy. Unlike conventional chemotherapy that rarely considers individual differences, it is crucial for targeted therapies to identify the beneficial subgroup of patients for the treatment. Currently, genomics and transcriptomics are the major 'omic' analytics used in studies of drug response prediction. However, proteomic profiling excels both in its advantages of directly detecting an instantaneous dynamic of the whole proteome, which contains most current diagnostic markers and therapeutic targets. Moreover, proteomic profiling improves understanding of the mechanism for drug resistance and helps finding optimal combination therapy. This article reviews the recent success of applications of proteomic analytics in predicting the response to targeted anticancer therapeutics, and discusses the potential avenues and pitfalls of proteomic platforms and techniques used most in the field.

Protein microarray applications: Autoantibody detection and posttranslational modification

The discovery of DNA microarrays was a major milestone in genomics; however, it could not adequately predict the structure or dynamics of underlying protein entities, which are the ultimate effector molecules in a cell. Protein microarrays allow simultaneous study of thousands of proteins/peptides, and various advancements in array technologies have made this platform suitable for several diagnostic and functional studies. Antibody arrays enable researchers to quantify the abundance of target proteins in biological fluids and assess PTMs by using the antibodies. Protein microarrays have been used to assess protein-protein interactions, protein-ligand interactions, and autoantibody profiling in various disease conditions. Here, we summarize different microarray platforms with focus on its biological and clinical applications in autoantibody profiling and PTM studies. We also enumerate the potential of tissue microarrays to validate findings from protein arrays as well as other approaches, highlighting their significance in proteomics.

Deciphering the complexity of the cancer proteome for diagnostic applications

The proteome is the most functional component encoded in the genome, yet most features of the proteome that are deregulated in cancer cannot be predicted from genomic analysis alone. These include post-translational modifications (PTMs), sub-cellular localization, networks and circuitry, formation of complexes, and functional activity, all of which could play a role or be affected as part of tumorigenesis. Thus, there is a substantial opportunity to elucidate protein alterations in cancer and to translate knowledge into diagnostics and therapeutics. The progress made in mining the cancer proteome for diagnostic applications and the path forward are herein reviewed.

Normalization of Reverse Phase Protein Microarray Data: Choosing the Best Normalization Analyte

Reverse phase protein microarray (RPMA) are a relatively recent but widely used approach to measure a large number of proteins, in their original and posttranslational modified forms, in a small clinical sample. Data normalization is fundamental for this technology, to correct for the sample-to-sample variability in the many possible confounding factors: extracellular proteins, red blood cells, different number of cells in the sample. To address this need, we adopted gene microarray algorithms to tailor the RPMA processing and analysis to the specific study set. Using geNorm and NormFinder algorithms, we screened seven normalization analytes (ssDNA, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), α/β-tubulin, mitochondrial ribosomal protein L11 (MRPL11), ribosomal protein L13a (RPL13a), β-actin, and total protein) across different sample sets, including cell lines, blood contaminated tissues, and tissues subjected to laser capture microdissection (LCM), to identify the analyte with the lowest variability. Specific normalization analytes were found to be advantageous for different classes of samples, with ssDNA being the optimal analyte to normalize blood contaminated samples.

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.

Reverse phase protein arrays: mapping the path towards personalized medicine

Reverse phase protein array (RPPA) technology evolved from the advent of miniaturized immunoassays and gene microarray technology. Reverse phase protein arrays provide either a low throughput or high throughput methodology for quantifying proteins and their post-translationally modified forms in both cellular and non-cellular samples. As the demand for patient tailored therapies increases so does the need for precise and sensitive technology to accurately profile the molecular circuitry driving an individual patient's disease. RPPAs are currently utilized in clinical trials for profiling and comparing the functional state of protein signaling pathways, either temporally within tumors, between patients, or within the same patients before/after treatment. RPPAs are generally employed for quantifying large numbers of samples on one array, under identical experimental conditions. However, the goal of personalized cancer medicine is to design therapies based on the molecular portrait of a patient's tumor, which in turn result in more efficacious treatments with less toxicity. Therefore, RPPAs are also being validated for low throughput assays of individual patient samples. This review explores RPPA technology in the cancer research field, concentrating on its role as a fundamental tool for deciphering protein signaling networks and its emerging role in personalized medicine.

Reverse phase protein arrays in signaling pathways: a data integration perspective

The reverse phase protein array (RPPA) data platform provides expression data for a prespecified set of proteins, across a set of tissue or cell line samples. Being able to measure either total proteins or posttranslationally modified proteins, even ones present at lower abundances, RPPA represents an excellent way to capture the state of key signaling transduction pathways in normal or diseased cells. RPPA data can be combined with those of other molecular profiling platforms, in order to obtain a more complete molecular picture of the cell. This review offers perspective on the use of RPPA as a component of integrative molecular analysis, using recent case examples from The Cancer Genome Altas consortium, showing how RPPA may provide additional insight into cancer besides what other data platforms may provide. There also exists a clear need for effective visualization approaches to RPPA-based proteomic results; this was highlighted by the recent challenge, put forth by the HPN-DREAM consortium, to develop visualization methods for a highly complex RPPA dataset involving many cancer cell lines, stimuli, and inhibitors applied over time course. In this review, we put forth a number of general guidelines for effective visualization of complex molecular datasets, namely, showing the data, ordering data elements deliberately, enabling generalization, focusing on relevant specifics, and putting things into context. We give examples of how these principles can be utilized in visualizing the intrinsic subtypes of breast cancer and in meaningfully displaying the entire HPN-DREAM RPPA dataset within a single page.

Capillary nano-immunoassays: advancing quantitative proteomics analysis, biomarker assessment, and molecular diagnostics

There is an emerging demand for the use of molecular profiling to facilitate biomarker identification and development, and to stratify patients for more efficient treatment decisions with reduced adverse effects. In the past decade, great strides have been made to advance genomic, transcriptomic and proteomic approaches to address these demands. While there has been much progress with these large scale approaches, profiling at the protein level still faces challenges due to limitations in clinical sample size, poor reproducibility, unreliable quantitation, and lack of assay robustness. A novel automated capillary nano-immunoassay (CNIA) technology has been developed. This technology offers precise and accurate measurement of proteins and their post-translational modifications using either charge-based or size-based separation formats. The system not only uses ultralow nanogram levels of protein but also allows multi-analyte analysis using a parallel single-analyte format for increased sensitivity and specificity. The high sensitivity and excellent reproducibility of this technology make it particularly powerful for analysis of clinical samples. Furthermore, the system can distinguish and detect specific protein post-translational modifications that conventional Western blot and other immunoassays cannot easily capture. This review will summarize and evaluate the latest progress to optimize the CNIA system for comprehensive, quantitative protein and signaling event characterization. It will also discuss how the technology has been successfully applied in both discovery research and clinical studies, for signaling pathway dissection, proteomic biomarker assessment, targeted treatment evaluation and quantitative proteomic analysis. Lastly, a comparison of this novel system with other conventional immuno-assay platforms is performed.