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

Parallel Analyses by Mass Spectrometry (MS) and Reverse Phase Protein Array (RPPA) Reveal Complementary Proteomic Profiles in Triple-Negative Breast Cancer (TNBC) Patient Tissues and Cell Cultures

High-plex proteomic technologies have made substantial contributions to mechanism studies and biomarker discovery in complex diseases, particularly cancer. Despite technological advancements, inherent limitations in individual proteomic approaches persist, impeding the achievement of comprehensive quantitative insights into the proteome. In this study, we employed two widely used proteomic technologies, mass spectrometry (MS) and reverse phase protein array (RPPA) to analyze identical samples, aiming to systematically assess the outcomes and performance of the different technologies. Additionally, we sought to establish an integrated workflow by combining these two proteomic approaches to augment the coverage of protein targets for discovery purposes. We used 14 fresh frozen tissue samples from triple-negative breast cancer (TNBC: seven tumors versus seven adjacent non-cancerous tissues) and cell line samples to evaluate both technologies and implement this dual-proteomic strategy. Using a single-step protein denaturation and extraction protocol, protein samples were subjected to reverse-phase liquid chromatography (LC) followed by electrospray ionization (ESI)-mediated MS/MS for proteomic profiling. Concurrently, identical sample aliquots were analyzed by RPPA for profiling of over 300 proteins and phosphoproteins that are in key signaling pathways or druggable targets in cancer. Both proteomic methods demonstrated the expected ability to differentiate samples by groups, revealing distinct proteomic patterns under various experimental conditions, albeit with minimal overlap in identified targets. Mechanism-based analysis uncovered divergent biological processes identified with the two proteomic technologies, capitalizing on their complementary exploratory potential.

Stromal Reprogramming through Dual PDGFRα/β Blockade Boosts the Efficacy of Anti-PD-1 Immunotherapy in Fibrotic Tumors

Excess stroma and cancer-associated fibroblasts (CAF) enhance cancer progression and facilitate immune evasion. Insights into the mechanisms by which the stroma manipulates the immune microenvironment could help improve cancer treatment. Here, we aimed to elucidate potential approaches for stromal reprogramming and improved cancer immunotherapy. Platelet-derived growth factor C (PDGFC) and D expression were significantly associated with a poor prognosis in patients with gastric cancer, and PDGF receptor beta (PDGFRβ) was predominantly expressed in diffuse-type gastric cancer stroma. CAFs stimulated with PDGFs exhibited markedly increased expression of CXCL1, CXCL3, CXCL5, and CXCL8, which are involved in polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) recruitment. Fibrotic gastric cancer xenograft tumors exhibited increased PMN-MDSC accumulation and decreased lymphocyte infiltration, as well as resistance to anti-PD-1. Single-cell RNA sequencing and spatial transcriptomics revealed that PDGFRα/β blockade reversed the immunosuppressive microenvironment through stromal modification. Finally, combining PDGFRα/β blockade and anti-PD-1 treatment synergistically suppressed the growth of fibrotic tumors. These findings highlight the impact of stromal reprogramming on immune reactivation and the potential for combined immunotherapy for patients with fibrotic cancer.

SIGNIFICANCE

Stromal targeting with PDGFRα/β dual blockade reverses the immunosuppressive microenvironment and enhances the efficacy of immune checkpoint inhibitors in fibrotic cancer. See related commentary by Tauriello, p. 655.

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.

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

Analytical Platforms 1: Use of Cultured Cells and Fluorescent Read-Out Coupled to NormaCurve Normalization in RPPA

The analytic platform described in this chapter uses proteins extracted from cultured cells as an infinite source of material to set up, validate, and quality control an RPPA platform. Readout of the arrays uses near-infrared fluorescence labeling and data normalization is performed using the bioinformatics package NormaCurve.In the first part, we will describe the advantages, drawbacks, and different applications of cell line material for RPPA. In the second part, we will describe how the staining protocol, the method of readout, and the normalization method applied afterward are interconnected and should be considered together. Finally, we will describe the NormaCurve package, which is freely available, and its requirements for implementation.Four protocols are provided in this chapter: (1) Protein lysis of cell lines using a homemade Laemmli buffer, (2) RPPA staining for fluorescent readout including a signal amplification step, (3) total protein staining in the visible spectrum for normalization purposes, and (4) total protein staining in the near-infrared spectrum for normalization purposes.

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

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

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.

INBIA: a boosting methodology for proteomic network inference

Background

The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing.

Results

We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis.

Conclusion

Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2183-5) contains supplementary material, which is available to authorized users.

Implementation of a Multiplex and Quantitative Proteomics Platform for Assessing Protein Lysates Using DNA-Barcoded Antibodies

Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and "time to result" are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed "batch effects" which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.

Credentialing Individual Samples for Proteogenomic Analysis

An integrated analysis of DNA, RNA and protein, so called proteogenomic studies, has the potential to greatly increase our understanding of both normal physiology and disease development. However, such studies are challenged by a lack of a systematic approach to credential individual samples resulting in the introduction of noise into the system that limits the ability to identify important biological signals. Indeed, a recent proteogenomic CPTAC study identified 26% of samples as unsatisfactory, resulting in a marked increase in cost and loss of information content. Based on a large-scale analysis of RNA-seq and proteomic data generated by reverse phase protein arrays (RPPA) and by mass spectrometry, we propose a protein-mRNA correlation-based (PMC) score as a robust metric to credential single samples for integrated proteogenomic studies. Samples with high PMC scores have significantly higher protein-mRNA correlation, total protein content and tumor purity. Our results highlight the importance of credentialing individual samples prior to proteogenomic analysis.

Prediction of dyslipidemia using gene mutations, family history of diseases and anthropometric indicators in children and adolescents: The CASPIAN-III study

Dyslipidemia, the disorder of lipoprotein metabolism resulting in high lipid profile, is an important modifiable risk factor for coronary heart diseases. It is associated with more than four million worldwide deaths per year. Half of the children with dyslipidemia have hyperlipidemia during adulthood, and its prediction and screening are thus critical. We designed a new dyslipidemia diagnosis system. The sample size of 725 subjects (age 14.66 ± 2.61 years; 48% male; dyslipidemia prevalence of 42%) was selected by multistage random cluster sampling in Iran. Single nucleotide polymorphisms (rs1801177, rs708272, rs320, rs328, rs2066718, rs2230808, rs5880, rs5128, rs2893157, rs662799, and Apolipoprotein-E2/E3/E4), and anthropometric, life-style attributes, and family history of diseases were analyzed. A framework for classifying mixed-type data in imbalanced datasets was proposed. It included internal feature mapping and selection, re-sampling, optimized group method of data handling using convex and stochastic optimizations, a new cost function for imbalanced data and an internal validation. Its performance was assessed using hold-out and 4-foldcross-validation. Four other classifiers namely as supported vector machines, decision tree, and multilayer perceptron neural network and multiple logistic regression were also used. The average sensitivity, specificity, precision and accuracy of the proposed system were 93%, 94%, 94% and 92%, respectively in cross validation. It significantly outperformed the other classifiers and also showed excellent agreement and high correlation with the gold standard. A non-invasive economical version of the algorithm was also implemented suitable for low- and middle-income countries. It is thus a promising new tool for the prediction of dyslipidemia.

An Optimized Framework for Cancer Prediction Using Immunosignature

Background:

Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma.

Methods:

In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers.

Results:

The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm.

Conclusion:

The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature.

Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.

Time-course assays of signaling proteins in cancer cell lines under kinase inhibition

Causal conceptual framework for network analysis

Data shed light on causal protein networks that are specific to biological context

Resource for signaling biology and for benchmarking computational methods

IL2 Inducible T-cell Kinase, a Novel Therapeutic Target in Melanoma

PURPOSE

IL2 inducible T-cell kinase (ITK) promoter CpG sites are hypomethylated in melanomas compared with nevi. The expression of ITK in melanomas, however, has not been established and requires elucidation.

EXPERIMENTAL DESIGN

An ITK-specific monoclonal antibody was used to probe sections from deidentified, formalin-fixed paraffin-embedded tumor blocks or cell line arrays and ITK was visualized by IHC. Levels of ITK protein differed among melanoma cell lines and representative lines were transduced with four different lentiviral constructs that each contained an shRNA designed to knockdown ITK mRNA levels. The effects of the selective ITK inhibitor BI 10N on cell lines and mouse models were also determined.

RESULTS

ITK protein expression increased with nevus to metastatic melanoma progression. In melanoma cell lines, genetic or pharmacologic inhibition of ITK decreased proliferation and migration and increased the percentage of cells in the G0-G1 phase. Treatment of melanoma-bearing mice with BI 10N reduced growth of ITK-expressing xenografts or established autochthonous (Tyr-Cre/Pten(null)/Braf(V600E)) melanomas.

CONCLUSIONS

We conclude that ITK, formerly considered an immune cell-specific protein, is aberrantly expressed in melanoma and promotes tumor development and progression. Our finding that ITK is aberrantly expressed in most metastatic melanomas suggests that inhibitors of ITK may be efficacious for melanoma treatment. The efficacy of a small-molecule ITK inhibitor in the Tyr-Cre/Pten(null)/Braf(V600E) mouse melanoma model supports this possibility.

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery.

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century’s work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery.

Challenges and Strategies for Proteome Analysis of the Interaction of Human Pathogenic Fungi with Host Immune Cells

Opportunistic human pathogenic fungi including the saprotrophic mold Aspergillus fumigatus and the human commensal Candida albicans can cause severe fungal infections in immunocompromised or critically ill patients. The first line of defense against opportunistic fungal pathogens is the innate immune system. Phagocytes such as macrophages, neutrophils and dendritic cells are an important pillar of the innate immune response and have evolved versatile defense strategies against microbial pathogens. On the other hand, human-pathogenic fungi have sophisticated virulence strategies to counteract the innate immune defense. In this context, proteomic approaches can provide deeper insights into the molecular mechanisms of the interaction of host immune cells with fungal pathogens. This is crucial for the identification of both diagnostic biomarkers for fungal infections and therapeutic targets. Studying host-fungal interactions at the protein level is a challenging endeavor, yet there are few studies that have been undertaken. This review draws attention to proteomic techniques and their application to fungal pathogens and to challenges, difficulties, and limitations that may arise in the course of simultaneous dual proteome analysis of host immune cells interacting with diverse morphotypes of fungal pathogens. On this basis, we discuss strategies to overcome these multifaceted experimental and analytical challenges including the viability of immune cells during co-cultivation, the increased and heterogeneous protein complexity of the host proteome dynamically interacting with the fungal proteome, and the demands on normalization strategies in terms of relative quantitative proteome analysis.

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery

Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

Studying Cellular Signal Transduction with OMIC Technologies

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.