Reverse Phase Protein Array Platform Research Articles

High-throughput profiling of histone post-translational modifications and chromatin modifying proteins by reverse phase protein array

Epigenetic variation plays a significant role in normal development and human diseases including cancer, in part through post-translational modifications (PTMs) of histones. Identification and profiling of changes in histone PTMs, and in proteins regulating PTMs, are crucial to understanding diseases, and for discovery of epigenetic therapeutic agents. In this study, we have adapted and validated an antibody-based reverse phase protein array (RPPA) platform for profiling 20 histone PTMs and expression of 40 proteins that modify histones and other epigenomic regulators. The specificity of the RPPA assay for histone PTMs was validated with synthetic peptides corresponding to histone PTMs and by detection of histone PTM changes in response to inhibitors of histone modifier proteins in cell cultures. The useful application of the RPPA platform was demonstrated with two models: induction of pluripotent stem cells and a mouse mammary tumor progression model. Described here is a robust platform that includes a rapid microscale method for histone isolation and partially automated workflows for analysis of histone PTMs and histone modifiers that can be performed in a high-throughput manner with hundreds of samples. This RPPA platform has potential for translational applications through the discovery and validation of epigenetic states as therapeutic targets and biomarkers. SignificanceOur study has established an antibody-based reverse phase protein array platform for global profiling of a wide range of post-translational modifications of histones and histone modifier proteins. The high-throughput platform provides comprehensive analyses of epigenetics for biological research and disease studies and may serve as screening assay for diagnostic purpose or therapy development.

Reverse-Phase Protein Array: Technology, Application, Data Processing, and Integration.

Reverse-phase protein array (RPPA) is a high-throughput antibody-based targeted proteomics platform that can quantify hundreds of proteins in thousands of samples derived from tissue or cell lysates, serum, plasma, or other body fluids. Protein samples are robotically arrayed as microspots on nitrocellulose-coated glass slides. Each slide is probed with a specific antibody that can detect levels of total protein expression or post-translational modifications, such as phosphorylation as a measure of protein activity. Here we describe workflow protocols and software tools that we have developed and optimized for RPPA in a core facility setting that includes sample preparation, microarray mapping and printing of protein samples, antibody labeling, slide scanning, image analysis, data normalization and quality control, data reporting, statistical analysis, and management of data. Our RPPA platform currently analyzes ∼240 validated antibodies that primarily detect proteins in signaling pathways and cellular processes that are important in cancer biology. This is a robust technology that has proven to be of value for both validation and discovery proteomic research and integration with other omics data sets.

Reverse-Phase Protein Array: Technology, Application, Data Processing, and Integration.

Reverse-phase protein array (RPPA) is a high-throughput antibody-based targeted proteomics platform that can quantify hundreds of proteins in thousands of samples derived from tissue or cell lysates, serum, plasma, or other body fluids. Protein samples are robotically arrayed as microspots on nitrocellulose-coated glass slides. Each slide is probed with a specific antibody that can detect levels of total protein expression or post-translational modifications, such as phosphorylation as a measure of protein activity. Here we describe workflow protocols and software tools that we have developed and optimized for RPPA in a core facility setting that includes sample preparation, microarray mapping and printing of protein samples, antibody labeling, slide scanning, image analysis, data normalization and quality control, data reporting, statistical analysis, and management of data. Our RPPA platform currently analyzes ∼240 validated antibodies that primarily detect proteins in signaling pathways and cellular processes that are important in cancer biology. This is a robust technology that has proven to be of value for both validation and discovery proteomic research and integration with other omics data sets.

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.

Profiling Glycoproteins on Functionalized Reverse Phase Protein Array.

Reverse phase protein array (RPPA), a high-throughput, parallel immunoassay in a dot-blot format, is a powerful tool to quantitatively profile protein expression in multiple samples simultaneously using small amounts of material. Despite its success, analysis of post-translationally modified (PTM) proteins has been limited in RPPA assays, primarily due to relatively low availability of antibodies specific to proteins of PTMs, e.g., glycosylation. Moreover, the high matrix complexity, with tens of thousands of proteins in cell lysates or tissue extracts and the low abundance of proteins with PTMs, makes it extremely challenging to detect these proteins with PTMs. Therefore, there is an urgent need to fill this gap, which would greatly contribute to the analysis of a specific PTM by RPPA. In this chapter, we introduce a novel RPPA platform, termed polymer-based reverse phase glycoprotein array (polyGPA), to measure the variation of glycosylation patterns on a three-dimensionally functionalized RPPA. Without the need of specific antibody towards glycosylation, polyGPA represents a highly sensitive strategy to analyze protein glycosylation in multiple complex biological samples in parallel.

Implementing Reverse Phase Protein Array Profiling as a Sensitive Method for the Early Pre-Clinical Detection of Off-Target Toxicities Associated with Sunitinib Malate.

The tyrosine kinase inhibitor (TKI) sunitinib is a multi-targeted agent approved across multiple cancer indications. Nevertheless, since approval, data has emerged to describe a worrisome side effect profile including hypertension, hand-foot syndrome, fatigue, diarrhea, mucositis, proteinuria, and (rarely) congestive heart failure. It has been hypothesized that the observed multi-parameter toxicity profile is related to "on-target" kinase inhibition in "off-target" tissues. To interrogate off-target effects in pre-clinical studies, a reverse phase protein array (RPPA) approach is employed. Mice are treated with sunitinib (40mgkg-1 ) for 4 weeks, following which critical organs are removed. The Zeptosens RPPA platform is employed for protein expression analysis. Differentially expressed proteins associated with damage and/or stress are found in the majority of organs from treated animals. Proteins differentially expressed in the heart are associated with myocardial hypertrophy, ischaemia/reperfusion, and hypoxia. However, hypertrophy is not evidenced on histology. Mild proteinuria is observed; however, no changes in renal glomerular structure are visible via electron microscopy. In skin, proteins associated with cutaneous inflammation, keratinocyte hyper-proliferation, and increased inflammatory response are differentially expressed. It is posited that pre-clinical implementation of a combined histopathological/RPPA approach provides a sensitive method to mechanistically elucidate the early manifestation of TKI on-target/organ off-target toxicities.

Reproducibility and Crossplatform Validation of Reverse-Phase Protein Array Data.

Reverse-phase protein array (RPPA) technology is a high-throughput antibody- and microarray-based approach for the rapid profiling of levels of proteins and protein posttranslational modifications in biological specimens. The technology consumes small amounts of samples, can sensitively detect low-abundance proteins and posttranslational modifications, enables measurements of multiple signaling pathways in parallel, has the capacity to analyze large sample numbers, and offers robust interexperimental reproducibility. These features of RPPA experiments have motivated and enabled the use of RPPA technology in various biomedical, translational, and clinical applications, including the delineation of molecular mechanisms of disease, profiling of druggable signaling pathway activation, and search for new prognostic markers. Owing to the complexity of many of these applications, such as developing multiplex protein assays for diagnostic laboratories or integrating posttranslational modification-level data using large-scale proteogenomic approaches, robust and well-validated data are essential. There are many distinct components of an RPPA workflow, and numerous possible technical setups and analysis parameter options exist. The differences between RPPA platform setups around the world offer opportunities to assess and minimize interplatform variation. Crossplatform validation may also aid in the evaluation of robust, platform-independent protein markers of disease and response to therapy.

Utility of Reverse-Phase Protein Array for Refining Precision Oncology.

Despite the early successes of targeted therapies and continuous improvements in next-generation sequencing technology over the last two decades, genomics-driven precision oncology has helped only a minority of cancer patients; thus treatment regimens are still not matched to the vast majority of cancer patients. It has become apparent that genomic profiling in itself is limited with respect to optimal selection of patients for targeted therapy. Proteomics-based approaches (in contrast to genomics-based and transcriptomics-based approaches) capture biological processes (e.g., diversity of protein expression patterns and post-translational modifications) directly contributing to cancer pathogenesis. This encourages incorporation of concordant proteomic analyses into the next stage of precision oncology. Reverse-phase protein array (RPPA) is well suited to pharmacodynamic analysis due to its ability to precisely map signaling status using limited amounts of clinical sample. In addition, the cost-effectiveness and rapid turnaround time of the RPPA platform offer a substantial advantage over existing molecular profiling technologies in a clinical setting. In this chapter, we begin by reviewing the current status of genomics-driven precision oncology, along with its limitations and challenges. Finally, we discuss the utility of RPPA technology as a means of improving precision oncology.

Abstract LB-040: Establishment of a predictive patient-derived xenograft model for renal cell carcinoma

Abstract Renal Cell Carcinoma (RCC) is classically considered a difficult malignancy to diagnose and treat. Although the surgery is a resolving approach for many local tumors, the metastatic disease generally is characterized by poor outcomes. The lack of valid preclinical cancer models has hampered the discovery of valuable diagnostic, prognostic and predictive biomarkers to develop effective therapeutic options. In order to create preclinical models, we established an orthotopic patient derived xenograft (PDX) murine model using an enriched stem cell like-heterogeneous bulk obtained from surgery patients’ specimens. To predict effective conventional and innovative therapeutic treatment, the stem cell like-heterogeneous bulks were analyzed before injection for key total and phosphorylated protein expression by Reverse Phase Protein Array (RPPA). METHODS: Using specific enzymatic dissociation and culture conditions, we first isolated enriched stem cell like-heterogeneous bulks from fresh surgery specimens derived from 30 patients who underwent nephrectomy. After one week, bulks were orthotopically injected in immunocompromised mice (NSG mice). Cells were infected with lentiviral vector (Tween-Luc) and in vivo monitored by IVIS imaging system. The percentage of engrafting bulks, sizes and distal spreading capacity were evaluated. Several xenografts were further dissociated, serially inoculated and propagated for up to eighth generations. Bulks were analyzed before injection by RPPA. This technique performs the detection of total and phosphoproteins allowing the analysis of hundreds of proteins. This RPPA platform includes specific antibodies for key proteins belonging or involved in targeted therapy signaling cascade. RESULTS: Approximately 67% of the implanted samples engrafted. We observed a correlation between the engraftment success and the aggressiveness of the parental tumor. Moreover, the PDXs obtained from more aggressive tumors showed increased size. Hematoxylin and Eosin staining showed that PDX displayed similar histological architectures to parental tumors. Furthermore, immunohistochemical analysis demonstrated that PDX retained CD10 and PAX 8 expression, two typical RCC biomarkers, at similar level than parental tumors. Finally, we obtained maps highlighting the specific activated proteins which are key candidates of targeted therapy pathways by RPPA. CONCLUSIONS: Our established in vitro and PDX models recapitulate tumor histology and molecular characteristic creating new source for ameliorating diagnosis, prognosis and therapy sensitiveness prediction. Furthermore, we were able to propagate PDXs from the same patient for up to eight generations. Since in average tumor uptake is 4-5 weeks, it would be theoretically possible to test in parallel therapeutics in murine models while the corresponding metastatic patient is under therapy. The ability to generate large PDX animal cohorts is very promising and could be exploited to obtain valuable preclinical platforms for innovative drug testing. Moreover, the possibility to map a wide variety of intracellular pathways and biological functions in enriched stem cell like-heterogeneous bulks enable us to explore protein-protein interaction networks with potential impact on drug activity and to identify deregulated circuits related to pharmacological inhibition. Citation Format: Simona Di Martino, Gabriele De Luca, Ludovica Grassi, Giulia Federici, Laura De Salvo, Anna Laura Di Pace, Antonio Addario, Giovanni Muto, Manuela Costantini, Mauro Biffoni, Michele Signore, Steno Sentinelli, Michele Milella, Michele Gallucci, Désirée Bonci, Ruggero De Maria. Establishment of a predictive patient-derived xenograft model for renal cell carcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-040.

A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as ‘cancer hallmarks’. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody–related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

A systems biology approach reveals common metastatic pathways in osteosarcoma

BackgroundOsteosarcoma (OS) is the most common malignant bone tumor in children and adolescents. The survival rate of patients with metastatic disease remains very dismal. Nevertheless, metastasis is a complex process and a single-level analysis is not likely to identify its key biological determinants. In this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mRNA and protein expression data in two distinct human metastatic OS models.ResultsmRNA expression microarray and N-linked glycoproteomic analyses were performed on two commonly used isogenic pairs of human metastatic OS cell lines, namely HOS/143B and SaOS-2/LM7. Pathway analysis of the differentially regulated genes and glycoproteins separately revealed pathways associated to metastasis including cell cycle regulation, immune response, and epithelial-to-mesenchymal-transition. However, no common significant pathway was found at both genomic and proteomic levels between the two metastatic models, suggesting a very different biological nature of the cell lines. To address this issue, we used a topological significance analysis based on a “shortest-path” algorithm to identify topological nodes, which uncovered additional biological information with respect to the genomic and glycoproteomic profiles but remained hidden from the direct analyses. Pathway analysis of the significant topological nodes revealed a striking concordance between the models and identified significant common pathways, including “Cytoskeleton remodeling/TGF/WNT”, “Cytoskeleton remodeling/Cytoskeleton remodeling”, and “Cell adhesion/Chemokines and adhesion”. Of these, the “Cytoskeleton remodeling/TGF/WNT” was the top ranked common pathway from the topological analysis of the genomic and proteomic profiles in the two metastatic models. The up-regulation of proteins in the “Cytoskeleton remodeling/TGF/WNT” pathway in the SaOS-2/LM7 and HOS/143B models was further validated using an orthogonal Reverse Phase Protein Array platform.ConclusionsIn this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in OS. The use of the topological analysis revealed hidden biological pathways that are known to play critical roles in metastasis. Wnt signaling has been previously implicated in OS and other tumors, and inhibitors of Wnt signaling pathways are available for clinical testing. Further characterization of this common pathway and other topological pathways identified from this study may lead to a novel therapeutic strategy for the treatment of metastatic OS.

Abstract 3400: A systems biology approach reveals common metastatic pathways in osteosarcoma

Abstract Background Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents. The survival rate of patients with metastatic disease remains very dismal. Nevertheless, metastasis is a complex process and a single-level analysis will not likely identify its key biological determinants. In this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mRNA and protein expression data in two distinct human metastatic OS models. Results mRNA expression microarray and N-linked glycoproteomic analyses were performed on two commonly used isogenic pairs of human metastatic OS cell lines, namely HOS/143B and SaOS-2/LM7. Pathway analysis of the up-regulated genes and glycoproteins separately revealed pathways associated to metastasis including RAS-related G-proteins signaling, cell cycle regulation, epithelial-to-mesenchymal-transition, cell adhesion, and WNT signaling. However, no common significant pathway was found between the two metastatic models, suggesting a very different biological nature of the cell lines. To address this issue, we used a topological significance analysis based on a “shortest path” algorithm to identify topological nodes that remained hidden from the independent transcriptomic and glycoproteomic analyses. Pathway analysis of the significant topological nodes revealed a striking concordance between the models and identified important common pathways, including “Cytoskeleton remodeling/TGF/WNT,” “Development/WNT signaling,” “Cytoskeleton remodeling/Cytoskeleton remodeling,” and “Cell adhesion/Chemokines and adhesion”. Of these, the “Cytoskeleton remodeling/TGF/WNT” was the top ranked common pathway from the topological analysis of the two cell line models. The up-regulation of proteins in the “Cytoskeleton remodeling/TGF/WNT” pathway in the SaOS-2/LM7 and HOS/143B models was further validated using a Reverse Phase Protein Array platform. Conclusions In this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in OS. The use of the topological analysis revealed hidden biological networks that are known to play critical roles in metastasis. Since WNT signaling has been previously implicated in OS and other tumors, further characterization of this common pathway and other topological pathways may lead to a novel therapeutic strategy for curing metastatic OS. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3400. doi:1538-7445.AM2012-3400