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

ABSTRACT: 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.

SUBMITTER: Coarfa C

PROVIDER: S-EPMC7861052 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Spatial normalization of reverse phase protein array data.

Project description:Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.

| S-EPMC4264691 | biostudies-literature

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

Project description:Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

| S-EPMC7603376 | biostudies-literature

NormaCurve: a SuperCurve-based method that simultaneously quantifies and normalizes reverse phase protein array data.

Project description:MotivationReverse phase protein array (RPPA) is a powerful dot-blot technology that allows studying protein expression levels as well as post-translational modifications in a large number of samples simultaneously. Yet, correct interpretation of RPPA data has remained a major challenge for its broad-scale application and its translation into clinical research. Satisfying quantification tools are available to assess a relative protein expression level from a serial dilution curve. However, appropriate tools allowing the normalization of the data for external sources of variation are currently missing.ResultsHere we propose a new method, called NormaCurve, that allows simultaneous quantification and normalization of RPPA data. For this, we modified the quantification method SuperCurve in order to include normalization for (i) background fluorescence, (ii) variation in the total amount of spotted protein and (iii) spatial bias on the arrays. Using a spike-in design with a purified protein, we test the capacity of different models to properly estimate normalized relative expression levels. The best performing model, NormaCurve, takes into account a negative control array without primary antibody, an array stained with a total protein stain and spatial covariates. We show that this normalization is reproducible and we discuss the number of serial dilutions and the number of replicates that are required to obtain robust data. We thus provide a ready-to-use method for reliable and reproducible normalization of RPPA data, which should facilitate the interpretation and the development of this promising technology.AvailabilityThe raw data, the scripts and the normacurve package are available at the following web site: http://microarrays.curie.fr.

| S-EPMC3386279 | biostudies-literature

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data.

Project description:SummaryReverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting.Availability and implementationThe standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE).Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9665860 | biostudies-literature

Reverse phase protein array based protein profiling of breast cancer specimens

2014-02-18 | GSE47066 | GEO

Reverse phase protein array based protein profiling of breast cancer specimens

Project description:Expression levels of proteins and phosphoproteins, covering major cancer signaling pathways with a special focus on breast cancer biology, were obtained for a series of 109 breast cancer tumor specimens with positive estrogen receptor status. Tumor specimens from patients diagnosed with primary invasive breast carcinoma were collected at the time of surgery between 2008 and 2010 at the Department of Gynecology and Obstetrics / National Center for Tumor Diseases Heidelberg. None of the patients had received neoadjuvant therapy. Institutional Review Board approval was received as ethics vote no. S039/2008 and informed consent was obtained from all patients. Tumor specimens were processed within 20 min after surgery. Samples were stored snap frozen at -80M-BM-0C until further use. Only tumor samples with > 70% tumour cells and positive estrogen receptor status (immunoreactive score M-bM-^IM-% 3) as assessed by routine immunohistochemistry were selected for this study (n = 109). Tumor lysates were printed on a series of nitrocellulose coated glass slides and probed with 128 different primary antibodies directed against proteins and phosphoproteins of interest. Primary antibodies were selected to recognize proteins involved in major cancer signaling pathways with a special focus on breast cancer biology.

2014-02-18 | E-GEOD-47066 | biostudies-arrayexpress

Reverse phase protein array based protein profiling of breast cancer specimens (2)

2014-02-18 | GSE50861 | GEO

A comprehensive comparison of normalization methods for loading control and variance stabilization of reverse-phase protein array data.

Project description:Loading control (LC) and variance stabilization of reverse-phase protein array (RPPA) data have been challenging mainly due to the small number of proteins in an experiment and the lack of reliable inherent control markers. In this study, we compare eight different normalization methods for LC and variance stabilization. The invariant marker set concept was first applied to the normalization of high-throughput gene expression data. A set of "invariant" markers are selected to create a virtual reference sample. Then all the samples are normalized to the virtual reference. We propose a variant of this method in the context of RPPA data normalization and compare it with seven other normalization methods previously reported in the literature. The invariant marker set method performs well with respect to LC, variance stabilization and association with the immunohistochemistry/florescence in situ hybridization data for three key markers in breast tumor samples, while the other methods have inferior performance. The proposed method is a promising approach for improving the quality of RPPA data.

| S-EPMC4213190 | biostudies-literature

The impact of ultraviolet- and infrared-based laser microdissection technology on phosphoprotein detection in the laser microdissection-reverse phase protein array workflow.

Project description:Reversible protein phosphorylation represents a key mechanism by which signals are transduced in eukaryotic cells. Dysregulated phosphorylation is also a hallmark of carcinogenesis and represents key drug targets in the precision medicine space. Thus, methods that preserve phosphoprotein integrity in the context of clinical tissue analyses are crucially important in cancer research. Here we investigated the impact of UV laser microdissection (UV LMD) and IR laser capture microdissection (IR LCM) on phosphoprotein abundance of key cancer signaling protein targets assessed by reverse-phase protein microarray (RPPA). Tumor epithelial cells from consecutive thin sections obtained from four high-grade serous ovarian cancers were harvested using either UV LMD or IR LCM methods. Phosphoprotein abundances for ten phosphoproteins that represent important drug targets were assessed by RPPA and revealed no significant differences in phosphoprotein integrity from those obtained using higher-energy UV versus the lower-energy IR laser methods.

| S-EPMC7061469 | biostudies-literature

Using Reverse Phase Protein Array (RPPA) to Identify and Target Adaptive Resistance.

Project description:Tumor cells and the tumor ecosystem rapidly evolve in response to therapy. This tumor evolution results in the rapid emergence of drug resistance that limits the magnitude and duration of response to therapy including chemotherapy, targeted therapy, and immunotherapy. Thus, there is an urgent need to understand and interdict tumor evolution to improve patient benefit to therapy. Reverse phase protein array (RPPA) provides a powerful tool to evaluate and develop approaches to target the processes underlying one form of tumor evolution: adaptive evolution. Tumor cells and the tumor microenvironment rapidly evolve through rewiring of protein networks to bypass the effects of therapy. In this review, we present the concepts underlying adaptive resistance and use of RPPA in understanding resistance mechanisms and identification of effective drug combinations. We further demonstrate that this novel information is resulting in biomarker-driven trials aimed at targeting adaptive resistance and improving patient outcomes.

| S-EPMC7382818 | biostudies-literature

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