Reverse-phase protein lysate microarrays for cell signaling analysis

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

Reversed-phase allergen microarrays on optical discs for multiplexed diagnostics of food allergies

A high percentage of the population suffers from multiple food allergies justifying  the importance of reliable diagnostic methods. Single-analyte solutions based on the determination of specific immunoglobulins E (sIgE) are safe and fast but are generally time-consuming and expensive. Thus sustainable microanalytical methods that provide multianalyte profiling information are highly demanded. This work presents the in vitro biosensing of specific IgE levels based on a reversed-phase allergen array. The approach consists of optical biosensing supported by direct multiplex immunoassays and on-disc technology. It identifies 12 sIgE associated with food allergies in a single analysis with a low serum sample volume (25 µL). After processing captured images, specific signals for each target biomarker correlate to their concentration. The assay analytically performs well with 0.3 IU/mL and 0.41 IU/mL as the detection and quantification limits in serum, respectively. This novel method achieves excellent clinical specificity (100%) and high sensitivity (91.1%), considering the diagnosis obtained by clinical history and ImmunoCAP analysis. The results demonstrate that microanalytical systems based on allergen arrays can potentially diagnose multiple food allergies and are easily implemented in primary care laboratory settings.

A reverse phase protein array based phospho-antibody characterization approach and its applicability for clinical derived tissue specimens

Systematic quantification of phosphoprotein within cell signaling networks in solid tissues remains challenging and precise quantification in large scale samples has great potential for biomarker identification and validation. We developed a reverse phase protein array (RPPA) based phosphor-antibody characterization approach by taking advantage of the lysis buffer compatible with alkaline phosphatase (AP) treatment that differs from the conventional RPPA antibody validation procedure and applied it onto fresh frozen (FF) and formalin-fixed and paraffin-embedded tissue (FFPE) to test its applicability. By screening 106 phospho-antibodies using RPPA, we demonstrated that AP treatment could serve as an independent factor to be adopted for rapid phospho-antibody selection. We also showed desirable reproducibility and specificity in clincical specimens indicating its potential for tissue-based phospho-protein profiling. Of further clinical significance, using the same approach, based on melanoma and lung cancer FFPE samples, we showed great interexperimental reproducibility and significant correlation with pathological markers in both tissues generating meaningful data that match clinical features. Our findings set a benchmark of an efficient workflow for phospho-antibody characterization that is compatible with high-plex clinical proteomics in precison oncology.

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

SUMMARY

Reverse-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 IMPLEMENTATION

The 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 INFORMATION

Supplementary data are available at Bioinformatics online.

BlotIt—Optimal alignment of Western blot and qPCR experiments

Biological systems are frequently analyzed by means of mechanistic mathematical models. In order to infer model parameters and provide a useful model that can be employed for systems understanding and hypothesis testing, the model is often calibrated on quantitative, time-resolved data. To do so, it is typically important to compare experimental measurements over broad time ranges and various experimental conditions, e.g. perturbations of the biological system. However, most of the established experimental techniques such as Western blot, or quantitative real-time polymerase chain reaction only provide measurements on a relative scale, since different sample volumes, experimental adjustments or varying development times of a gel lead to systematic shifts in the data. In turn, the number of measurements corresponding to the same scale enabling comparability is limited. Here, we present a new flexible method to align measurement data that obeys different scaling factors and compare it to existing normalization approaches. We propose an alignment model to estimate these scaling factors and provide the possibility to adapt this model depending on the measurement technique of interest. In addition, an error model can be specified to adequately weight the different data points and obtain scaling-model based confidence intervals of the finally scaled data points. Our approach is applicable to all sorts of relative measurements and does not need a particular experimental condition that has been measured over all available scales. An implementation of the method is provided with the R package blotIt including refined ways of visualization.

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.

Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays

PURPOSE

Predicting cancer dependencies from molecular data can help stratify patients and identify novel therapeutic targets. Recently available data on large-scale cancer cell line dependency allow a systematic assessment of the predictive power of diverse molecular features; however, the protein expression data have not been rigorously evaluated. By using the protein expression data generated by reverse-phase protein arrays, we aimed to assess their predictive power in identifying cancer dependencies and to develop a related analytic tool for community use.

MATERIALS AND METHODS

By using a machine learning schema, we conducted an analysis of feature importance based on cancer dependency and multiomic data from the DepMap and Cancer Cell Line Encyclopedia projects. We assessed the consistency of cancer dependency data between CRISPR/Cas9 and short hairpin RNA–mediated perturbation platforms. For a fair comparison, we focused on a set of genes with robust dependency data and four available expression-related features (copy number alteration, DNA methylation, messenger RNA expression, and protein expression) and performed the same-gene predictions of the cancer dependency using different molecular features.

RESULTS

For the genes surveyed, we observed that the protein expression data contained substantial predictive power for cancer dependencies, and they were the best predictive feature for the CRISPR/Cas9-based dependency data. We also developed a user-friendly protein-dependency analytic module and integrated it with The Cancer Proteome Atlas; this module allows researchers to explore and analyze our results intuitively.

CONCLUSION

This study provides a systematic assessment for predicting cancer dependencies of cell lines from different expression-related features of a gene. Our results suggest that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Myopathy associated LDB3 mutation causes Z-disc disassembly and protein aggregation through PKCα and TSC2-mTOR downregulation

Mechanical stress induced by contractions constantly threatens the integrity of muscle Z-disc, a crucial force-bearing structure in striated muscle. The PDZ-LIM proteins have been proposed to function as adaptors in transducing mechanical signals to preserve the Z-disc structure, however the underlying mechanisms remain poorly understood. Here, we show that LDB3, a well-characterized striated muscle PDZ-LIM protein, modulates mechanical stress signaling through interactions with the mechanosensing domain in filamin C, its chaperone HSPA8, and PKCα in the Z-disc of skeletal muscle. Studies of Ldb3Ala165Val/+ mice indicate that the myopathy-associated LDB3 p.Ala165Val mutation triggers early aggregation of filamin C and its chaperones at muscle Z-disc before aggregation of the mutant protein. The mutation causes protein aggregation and eventually Z-disc myofibrillar disruption by impairing PKCα and TSC2-mTOR, two important signaling pathways regulating protein stability and disposal of damaged cytoskeletal components at a major mechanosensor hub in the Z-disc of skeletal muscle.

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution

The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and conserved across mammalian species including human is unknown. Moreover, whether the stability-dependent correlation pattern is altered in the tumor has not been explored. To establish the quantitative relationship between stability and correlation between mRNA and protein levels, we performed a multi-omics data integration study across mammalian systems including diverse types of human tissues and cell lines in a genome-wide manner. The current study illuminated an important aspect of the mammalian proteotranscriptomic landscape by providing evidence that stability-constrained mRNA-to-protein correlation follows a hierarchical pattern that remains conserved across different tissues and mammalian species. By analyzing the tumor and non-tumor tissues, we further illustrated that mRNA-to-protein correlations deviate in tumor tissues. By gene-centric analysis, we harnessed the hierarchical correlation patterns to identify altered mRNA-to-protein correlation in tumors and characterized the tumor correlation-enhancing and -repressing genes. We elucidated the transcriptional regulatory circuits controlling the correlation-enhancing and -repressing genes that are associated with metabolic reprogramming and cancer-associated pathways in tumor tissue. By tightly controlling the mRNA-to-protein correlation of specific genes, the transcriptional regulatory circuits may enable the tumor cells to evolve in varying tumor microenvironment. The mRNA-to-protein correlation analysis thus can serve as a unique approach to identify the pathways prioritized by the tumor cells at different clinical stages. The component of transcriptional regulatory circuits identified by the current study can serve as potential candidates for stage-dependent anticancer therapy.

Organotypic Models in Drug Development "Tumor Models and Cancer Systems Biology for the Investigation of Anticancer Drugs and Resistance Development"

The landscape of cancer treatment has improved over the past decades, aiming to reduce systemic toxicity and enhance compatibility with the quality of life of the patient. However, at the therapeutic level, metastatic cancer remains hugely challenging, based on the almost inevitable emergence of therapy resistance. A small subpopulation of cells able to survive drug treatment termed the minimal residual disease may either harbor resistance-associated mutations or be phenotypically resistant, allowing them to regrow and become the dominant population in the therapy-resistant tumor. Characterization of the profile of minimal residual disease represents the key to the identification of resistance drivers that underpin cancer evolution. Therapeutic regimens must, therefore, be dynamic and tailored to take into account the emergence of resistance as tumors evolve within a complex microenvironment in vivo. This requires the adoption of new technologies based on the culture of cancer cells in ways that more accurately reflect the intratumor microenvironment, and their analysis using omics and system-based technologies to enable a new era in the diagnostics, classification, and treatment of many cancer types by applying the concept "from the cell plate to the patient." In this chapter, we will present and discuss 3D model building and use, and provide comprehensive information on new genomic techniques that are increasing our understanding of drug action and the emergence of resistance.

Effects of Different Exercise Training Protocols on Gene Expression of Rac1 and PAK1 in Healthy Rat Fast- and Slow-Type Muscles

Purpose

Rac1 and its downstream target PAK1 are novel regulators of insulin and exercise-induced glucose uptake in skeletal muscle. However, it is not yet understood how different training intensities affect the expression of these proteins. Therefore, we studied the effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on Rac1 and PAK1 expression in fast-type (gastrocnemius, GC) and slow-type (soleus, SOL) muscles in rats after HIIT and MICT swimming exercises.

Methods

The mRNA expression was determined using qPCR and protein expression levels with reverse-phase protein microarray (RPPA).

Results

HIIT significantly decreased Rac1 mRNA expression in GC compared to MICT (p = 0.003) and to the control group (CON) (p = 0.001). At the protein level Rac1 was increased in GC in both training groups, but only the difference between HIIT and CON was significant (p = 0.02). HIIT caused significant decrease of PAK1 mRNA expression in GC compared to MICT (p = 0.007) and to CON (p = 0.001). At the protein level, HIIT increased PAK1 expression in GC compared to MICT and CON (by ∼17%), but the difference was not statistically significant (p = 0.3, p = 0.2, respectively). There were no significant differences in the Rac1 or PAK1 expression in SOL between the groups.

Conclusion

Our results indicate that HIIT, but not MICT, decreases Rac1 and PAK1 mRNA expression and increases the protein expression of especially Rac1 but only in fast-type muscle. These exercise training findings may reveal new therapeutic targets to treat patients with metabolic diseases.

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.

Short-term CDK4/6 Inhibition Radiosensitizes Estrogen Receptor-Positive Breast Cancers

PURPOSE

Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have improved progression-free survival for metastatic, estrogen receptor-positive (ER⁺) breast cancers, but their role in the nonmetastatic setting remains unclear. We sought to understand the effects of CDK4/6 inhibition (CDK4/6i) and radiotherapy in multiple preclinical breast cancer models.

EXPERIMENTAL DESIGN

Transcriptomic and proteomic analyses were used to identify significantly altered pathways after CDK4/6i. Clonogenic assays were used to quantify the radiotherapy enhancement ratio (rER). DNA damage was quantified using γH2AX staining and the neutral comet assay. DNA repair was assessed using RAD51 foci formation and nonhomologous end joining (NHEJ) reporter assays. Orthotopic xenografts were used to assess the efficacy of combination therapy.

RESULTS

Palbociclib significantly radiosensitized multiple ER⁺ cell lines at low nanomolar, sub IC₅₀ concentrations (rER: 1.21-1.52) and led to a decrease in the surviving fraction of cells at 2 Gy (P < 0.001). Similar results were observed in ribociclib-treated (rER: 1.08-1.68) and abemaciclib-treated (rER: 1.19-2.05) cells. Combination treatment decreased RAD51 foci formation (P < 0.001), leading to a suppression of homologous recombination activity, but did not affect NHEJ efficiency (P > 0.05). Immortalized breast epithelial cells and cells with acquired resistance to CDK4/6i did not demonstrate radiosensitization (rER: 0.94-1.11) or changes in RAD51 foci. In xenograft models, concurrent palbociclib and radiotherapy led to a significant decrease in tumor growth.

CONCLUSIONS

These studies provide preclinical rationale to test CDK4/6i and radiotherapy in women with locally advanced ER⁺ breast cancer at high risk for locoregional recurrence.

Online informatics resources to facilitate cancer target and chemical probe discovery

The advances in cancer genomics, chemical biology, high-throughput screening technologies, and synthetic medicinal chemistry have tremendously expanded the biological space of cancer targets and chemical space of bioactive small molecules to interrogate oncogenic signaling. To explore and leverage these exponentially growing cancer-associated data, a great number of computational tools, databases, and algorithms have been developed. This review summarizes recent cancer-related web resources that allow researchers working at the interface of chemical, biological, and cancer genomics fields to integrate clinical and genomics data for specific actionable targets and selective chemical compounds to facilitate cancer therapeutic discovery.

Technological Advances in Preclinical Drug Evaluation: The Role of -Omics Methods

Preclinical drug development is an essential step in the drug development process where the evaluation of new chemical entities occurs. In particular, preclinical drug development phases include deep analysis of drug candidates' interactions with biomolecules/targets, their safety, toxicity, pharmacokinetics, metabolism by use of assays in vitro and in vivo animal assays. Legal aspects of the required procedures are well-established. Herein, we present a comprehensive summary of current state-of-the art approaches and techniques used in preclinical studies. In particular, we will review the potential of new, -omics methods and platforms for mechanistic evaluation of drug candidates and speed-up of the preclinical evaluation steps.

Engineering Orthogonal, Plasma Membrane-Specific SLIPT Systems for Multiplexed Chemical Control of Signaling Pathways in Living Single Cells

Most cell behaviors are the outcome of processing information from multiple signals generated upon cell stimulation. Thus, a systematic understanding of cellular systems requires methods that allow the activation of more than one specific signaling molecule or pathway within a cell. However, the construction of tools suitable for such multiplexed signal control remains challenging. In this work, we aimed to develop a platform for chemically manipulating multiple signaling molecules/pathways in living mammalian cells based on self-localizing ligand-induced protein translocation (SLIPT). SLIPT is an emerging chemogenetic tool that controls protein localization and cell signaling using synthetic self-localizing ligands (SLs). Focusing on the inner leaflet of the plasma membrane (PM), where there is a hub of intracellular signaling networks, here we present the design and engineering of two new PM-specific SLIPT systems based on an orthogonal eDHFR and SNAP-tag pair. These systems rapidly induce translocation of eDHFR- and SNAP-tag-fusion proteins from the cytoplasm to the PM specifically in a time scale of minutes upon addition of the corresponding SL. We then show that the combined use of the two systems enables chemically inducible, individual translocation of two distinct proteins in the same cell. Finally, by integrating the orthogonal SLIPT systems with fluorescent reporters, we demonstrate simultaneous multiplexed activation and fluorescence imaging of endogenous ERK and Akt activities in a single cell. Collectively, orthogonal PM-specific SLIPT systems provide a powerful new platform for multiplexed chemical signal control in living single cells, offering new opportunities for dissecting cell signaling networks and synthetic cell manipulation.

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive ‘atlas’ of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Network-based integration of multi-omics data for prioritizing cancer genes

Motivation

Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression.

Results

We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS’ competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types.

Availability and implementation

NetICS is available at https://github.com/cbg-ethz/netics.

Supplementary information

Supplementary data are available at Bioinformatics online.

Impact of Cold Ischemic Time and Freeze-Thaw Cycles on RNA, DNA and Protein Quality in Colorectal Cancer Tissues Biobanking

Tissue-derived RNA, DNA and protein samples become more and more crucial for molecular detection in clinical research, personalized and targeted cancer therapy. This study evaluated how to biobanking colorectal tissues through examining the influences of cold ischemic time and freeze-thaw cycles on RNA, DNA and protein integrity. Here, 144 pairs of tumor and normal colorectal tissues were used to investigate the impact of cold ischemic times (0-48h) on RNA, DNA and protein integrity at on ice or room temperature conditions. Additionally, 45 pairs of tissues experienced 0-9 freeze-thaw cycles, and then the RNA, DNA and protein quality were analyzed. On ice, RNA, DNA and protein from colorectal tumor and normal tissues were all stable up to 48h after surgery. At room temperature, RNA in colorectal tumor and normal tissues began to degrade at 8h and 24h, respectively. Meanwhile, the tumor tissues DNA degradation occurred at 24h after surgery at room temperature. Similarly, the protein expression level of tumor and normal tissues began to change at 24h after the surgery at room temperature. Interestingly, tissue RNA and DNA remained stable even after 9 freeze-thaw cycles, whereas the proteins levels were remarkably changed after 7 freeze-thaw cycles. This study provided a useful evidence on how to store human colorectal tissues for biobanking. Preserving the surgical colorectal tissue on ice was an effective way to prevent RNA, DNA and protein degradation. Importantly, more than 7 repeated freeze-thaw cycles were not recommended for colorectal tissues.

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data

Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.

Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer

Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on the lower dimensional projections of the graph is performed based on Laplacian embeddings using a penalized k-means approach, motivated by Dirichlet process mixture models in Bayesian nonparametrics. In contrast to standard algorithmic approaches for known graphs, the proposed method allows estimation and inference for both graph structure learning and clustering. More importantly, the arguments for Laplacian embeddings as suitable projections for graph clustering are formalized by providing theoretical support for the consistency of the eigenspace of the estimated graph Laplacians. Fast computational algorithms are proposed to scale the method to large number of nodes. Extensive simulations are presented to compare the clustering performance with standard methods. The methods are applied to a novel pan-cancer proteomic data set, and protein networks and clusters are evaluated across multiple different cancer types.

Detecting Secretory Proteins by Acoustic Droplet Ejection in Multiplexed High-Throughput Applications

Nearly one-third of the encoded proteome is comprised of secretory proteins that enable communication between cells and organ systems, playing a ubiquitous role in human health and disease. High-throughput detection of secreted proteins would enhance efforts to identify therapies for secretion-related diseases. Using the Z mutant of alpha-1 antitrypsin as a human secretory model, we have developed 1536-well high-throughput screening assays that utilize acoustic droplet ejection to transfer nanoliter volumes of sample for protein quantification. Among them, the acoustic reverse phase protein array (acoustic RPPA) is a multiplexable, low-cost immunodetection technology for native, endogenously secreted proteins from physiologically relevant model systems like stem cells that is compatible with plate-based instrumentation. Parallel assay profiling with the LOPAC¹²⁸⁰ chemical library validated performance and orthogonality between a secreted bioluminescent reporter and acoustic RPPA method by consistently identifying secretory modulators with comparable concentration response relationships. Here, we introduce a robust, multiplexed drug discovery platform coupling extracellular protein quantification by acoustic RPPA with intracellular and cytotoxicity analyses from single wells, demonstrating proof-of-principle applications for human induced pluripotent stem cell-derived hepatocytes.

Mechanisms of skeletal muscle wasting in a mouse model for myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is a multi-systemic disease resulting in severe muscle weakening and wasting. DM1 is caused by expansion of CTG repeats in the 3' untranslated region of the dystrophia myotonica protein kinase (DMPK) gene. We have developed an inducible, skeletal muscle-specific mouse model of DM1 (CUG960) that expresses 960 CUG repeat-expressing animals (CUG960) in the context of human DMPK exons 11-15. CUG960 RNA-expressing mice induced at postnatal day 1, as well as adult-onset animals, show clear, measurable muscle wasting accompanied by severe histological defects including central myonuclei, reduced fiber cross-sectional area, increased percentage of oxidative myofibers, the presence of nuclear RNA foci that colocalize with Mbnl1 protein, and increased Celf1 protein in severely affected muscles. Importantly, muscle loss, histological abnormalities and RNA foci are reversible, demonstrating recovery upon removal of toxic RNA. RNA-seq and protein array analysis indicate that the balance between anabolic and catabolic pathways that normally regulate muscle mass may be disrupted by deregulation of platelet derived growth factor receptor β signaling and the PI3K/AKT pathways, along with prolonged activation of AMP-activated protein kinase α signaling. Similar changes were detected in DM1 skeletal muscle compared with unaffected controls. The mouse model presented in this paper shows progressive skeletal muscle wasting and has been used to identify potential molecular mechanisms underlying skeletal muscle loss. The reversibility of the phenotype establishes a baseline response for testing therapeutic approaches.

A review on machine learning principles for multi-view biological data integration

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

A Systematic Analysis Workflow for High-Density Customized Protein Microarrays in Biomarker Screening

High-density protein microarrays constitute a promising high-throughput platform for the characterization of protein expression patterns, biomarker discovery, and validation. Different types of protein microarrays have been described according to several features (such as content, format, and detection system) presenting advantages and disadvantages which are relevant for the specific application and purposes. Therefore, an experimental design is key for any screening based on protein microarrays assays; in fact, the data analysis strategy is directly related to the experimental design, type of protein microarray and consequently the final outcome, the data and results interpretation, is also directly linked. Here, it is proposed a systematic workflow for biomarker discovery based on tailor-made protein microarrays platforms which obtain comprehensively info for the functional protein characterization in high-throughput format.

RPPA: Origins, Transition to a Validated Clinical Research Tool, and Next Generations of the Technology

RPPA technology has graduated from a research tool to an essential component of clinical drug discovery research and personalized medicine. Next generations of RPPA technology will be a single clinical instrument that integrates all the steps of the workflow.

Contribution of the Alkylquinolone Quorum-Sensing System to the Interaction of Pseudomonas aeruginosa With Bronchial Epithelial Cells

Pseudomonas aeruginosa causes infections in patients with compromised epithelial barrier function. Multiple virulence factors produced by P. aeruginosa are controlled by quorum sensing (QS) via 2-alkyl-4(1H)-quinolone (AQ) signal molecules. Here, we investigated the impact of AQs on P. aeruginosa PAO1 infection of differentiated human bronchial epithelial cells (HBECs). The pqsA-E operon is responsible for the biosynthesis of AQs including the 2-alkyl-3-hydroxy-4-quinolones, 4-hydroxy-2-alkylquinolines, and 4-hydroxy-2-alkylquinoline N-oxides as exemplified by pseudomonas quinolone signal (PQS), 2-heptyl-4-hydroxyquinoline (HHQ), and 2-heptyl-4-hydroxyquinoline N-oxide (HQNO), respectively. PQS and HHQ both act as QS signal molecules while HQNO is a cytochrome inhibitor. PqsE contributes both to AQ biosynthesis and promotes virulence in a PQS-independent manner. Our results show that PQS, HHQ, and HQNO were produced during PAO1 infection of HBECs, but no differences in growth or cytotoxicity were apparent when PAO1 and an AQ-negative ΔpqsA mutant were compared. Both strains promoted synthesis of inflammatory cytokines TNF-α, interleukin (IL)-6, and IL-17C by HBECs, and the provision of exogenous PQS negatively impacted on this response without affecting bacterial growth. Expression of pqsE and the PQS-independent PqsE-regulated genes mexG and lecA was detected during HBEC infection. Levels were reduced in the ΔpqsA mutant, that is, in the absence of PQS, and increased by exogenous PQS. These results support an AQ-independent role for PqsE during initial infection of HBEC by P. aeruginosa and for PQS as an enhancer of PqsE and PqsE-controlled virulence determinants and as an immunomodulator.

Comprehensive analysis of Reverse Phase Protein Array data reveals characteristic unique proteomic signatures for glioblastoma subtypes

The most common and lethal type of intracranial tumors include the astrocytomas. Grade IV astrocytoma or Glioblastoma (GBM) is highly aggressive and treatment-refractory with a median survival of only 14 to 16 months. Molecular profiling of GBMs reveals a high degree of intra- and inter-tumoral heterogeneity, and hence it is important to understand the important signalling axes that get deregulated in different GBM subtypes to provide effective tailor-made therapies. In this study, we have carried out extensive analysis of Reverse Phase Protein Array (RPPA) data from TCGA cohort to develop protein signatures that define glioma grades or subtypes. The protein signatures that distinguished Grade II or III from GBM had largely overlapped, and pathway analysis revealed the positive enrichment of extracellular matrix proteins (ECM), MYC pathway, uPAR pathway and G2/M checkpoint genes in GBM. We also identified protein signatures for GBMs with genetic alterations (IDH mutation, p53 mutation, EGFR amplification or mutation, CDKN2A/CDKN2B deletion, and PTEN mutation) that occur at high frequency. G-CIMP positive GBM-specific protein signature showed a large similarity with IDH1-mutant protein signature, thus signifying the importance of IDH1 mutation driving the G-CIMP. Gene expression subtype analysis revealed an association of specific proteins to classical (EGFR and phosphor variants), mesenchymal (SERPINE1, TAZ, and Myosin-IIa_pS1943), neural (TUBA1B), and proneural (GSK3_pS9) types. Univariate Cox regression analysis identified several proteins showing significant correlation with GBM survival. Multivariate analysis revealed that IGFBP2 and RICTOR_pT1135 are independent predictors of survival. Overall, our analyses reveal that specific proteins are regulated in different glioma subtypes underscoring the importance of diverse signalling axes playing important role in the pathogenesis of glioma tumors.

Quantitative screening of serum protein biomarkers by reverse phase protein arrays

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

RPPAware: A software suite to preprocess, analyze and visualize reverse phase protein array data

Reverse Phase Protein Arrays (RPPA) is a high-throughput technology used to profile levels of protein expression. Handling the large datasets generated by RPPA can be facilitated by appropriate software tools. Here, we describe RPPAware, a free and intuitive software suite that was developed specifically for analysis and visualization of RPPA data. RPPAware is a portable tool that requires no installation and was built using Java. Many modules of the tool invoke R to utilize the statistical features. To demonstrate the utility of RPPAware, data generated from screening brain regions of a mouse model of Down syndrome with 62 antibodies were used as a case study. The ease of use and efficiency of RPPAware can accelerate data analysis to facilitate biological discovery. RPPAware 1.0 is freely available under GNU General Public License from the project website at http://downsyndrome.ucdenver.edu/iddrc/rppaware/home.htm along with a full documentation of the tool.

Application of CRISPR-Cas9 Based Genome-Wide Screening Approaches to Study Cellular Signalling Mechanisms

The cellular signalling process is a highly complex mechanism, involving multiple players, which together orchestrate the cell's response to environmental changes and perturbations. Given the multitude of genes that participate in the process of cellular signalling, its study in a genome-wide manner has proven challenging. Recent advances in gene editing technologies, including clustered regularly-interspaced short palindromic repeats/Cas9 (CRISPR/Cas9) approaches, have opened new opportunities to investigate global regulatory signalling programs of cells in an unbiased manner. In this review, we focus on how the application of pooled genetic screening approaches using the CRISPR/Cas9 system has contributed to a systematic understanding of cellular signalling processes in normal and disease contexts.

QSurface: fast identification of surface expression markers in cancers

Background

Cell surface proteins have provided useful targets and biomarkers for advanced cancer therapies. The recent clinical success of antibody-drug conjugates (ADCs) highlights the importance of finding selective surface antigens for given cancer subtypes. We thus attempted to develop stand-alone software for the analysis of the cell surface transcriptome of patient cancer samples and to prioritize lineage- and/or mutation-specific over-expression markers in cancer cells.

Results

A total of 519 genes were selected as surface proteins, and their expression was profiled in 14 cancer subtypes using patient sample transcriptome data. Lineage/mutation-oriented analysis was used to identify subtype-specific surface markers with statistical confidence. Experimental validation confirmed the unique over-expression of predicted surface markers (MUC4, MSLN, and SLC7A11) in lung cancer cells at the protein level. The differential cell surface gene expression of cell lines may differ from that of tissue samples due to the absence of the tumor microenvironment.

Conclusions

In the present study, advanced 3D models of lung cell lines successfully reproduced the predicted patterns, demonstrating the physiological relevance of cell line-based 3D models in validating surface markers from patient tumor data. Also QSurface software is freely available at http://compbio.sookmyung.ac.kr/~qsurface.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0541-6) contains supplementary material, which is available to authorized users.

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.

Electrophoretic cytometry of adherent cells

Cell-matrix and cell-cell interactions influence intracellular signalling and play an important role in physiologic and pathologic processes. Detachment of cells from the surrounding microenvironment alters intracellular signalling. Here, we demonstrate and characterise an integrated microfluidic device to culture single and clustered cells in tuneable microenvironments and then directly analyse the lysate of each cell in situ, thereby eliminating the need to detach cells prior to analysis. First, we utilise microcontact printing to pattern cells in confined geometries. We then utilise a microscale isoelectric focusing (IEF) module to separate, detect, and analyse lamin A/C from substrate-adhered cells seeded and cultured at varying (500, 2000, and 9000 cells per cm2) densities. We report separation performance (minimum resolvable pI difference of 0.11) that is on par with capillary IEF and independent of cell density. Moreover, we map lamin A/C and β-tubulin protein expression to morphometric information (cell area, circumference, eccentricity, form factor, and cell area factor) of single cells and observe poor correlation with each of these parameters. By eliminating the need for cell detachment from substrates, we enhance detection of cell receptor proteins (CD44 and β-integrin) and dynamic phosphorylation events (pMLCS19) that are rendered undetectable or disrupted by enzymatic treatments. Finally, we optimise protein solubilisation and separation performance by tuning lysis and electrofocusing (EF) durations. We observe enhanced separation performance (decreased peak width) with longer EF durations by 25.1% and improved protein solubilisation with longer lysis durations. Overall, the combination of morphometric analyses of substrate-adhered cells, with minimised handling, will yield important insights into our understanding of adhesion-mediated signalling processes.

Explore, Visualize, and Analyze Functional Cancer Proteomic Data Using the Cancer Proteome Atlas

Reverse-phase protein arrays (RPPA) represent a powerful functional proteomic approach to elucidate cancer-related molecular mechanisms and to develop novel cancer therapies. To facilitate community-based investigation of the large-scale protein expression data generated by this platform, we have developed a user-friendly, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA, http://tcpaportal.org), which contains two separate web applications. The first one focuses on RPPA data of patient tumors, which contains >8,000 samples of 32 cancer types from The Cancer Genome Atlas and other independent patient cohorts. The second application focuses on the RPPA data of cancer cell lines and contains >650 independent cell lines across 19 lineages. Many of these cell lines have publicly available, high-quality DNA, RNA, and drug screening data. TCPA provides various analytic and visualization modules to help cancer researchers explore these datasets and generate testable hypotheses in an effective and intuitive manner. Cancer Res; 77(21); e51-54. ©2017 AACR.

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening

Background

In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC’s prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA).

Results

With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data.

Conclusions

Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients’ survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4026-6) contains supplementary material, which is available to authorized users.

Topical Fibronectin Improves Wound Healing of Irradiated Skin

Wound healing is significantly delayed in irradiated skin. To better understand global changes in protein expression after radiation, we utilized a reverse phase protein array (RPPA) to identify significant changes in paired samples of normal and irradiated human skin. Of the 210 proteins studied, fibronectin was the most significantly and consistently downregulated in radiation-damaged skin. Using a murine model, we confirmed that radiation leads to decreased fibronectin expression in the skin as well as delayed wound healing. Topically applied fibronectin was found to significantly improve wound healing in irradiated skin and was associated with decreased inflammatory infiltrate and increased angiogenesis. Fibronectin treatment may be a useful adjunctive modality in the treatment of non-healing radiation wounds.

Inhibition of PI3K suppresses propagation of drug-tolerant cancer cell subpopulations enriched by 5-fluorouracil

Drug-tolerant cancer cell subpopulations are responsible for relapse after chemotherapy. By continuously exposing the gastric cancer cell line MKN45 to 5-FU for >100 passages, we established a 5-fluorouracil (5-FU)-tolerant line, MKN45/5FU. Orthotopic xenografts of MKN45/5FU cells in the stomach of nude mice revealed that these cells had a high potential to metastasize to sites such as the liver. Levels of phosphorylated phosphatidylinositide 3-kinase (PI3K) increased both in 5-FU-tolerant subpopulations according to the 5-FU dose, and in gastric submucosal orthotopic xenografts of MKN45/5FU cells. Sequential administration of 5-FU and a PI3K inhibitor, GDC-0941, targeted the downstream ribosomal S6 kinase phosphorylation to significantly suppress 5-FU-tolerant subpopulations and tumor propagation of orthotopic MKN45/5FU xenografts. These results suggest that administration of 5-FU followed by GDC-0941 may suppress disease relapse after 5-FU-based gastric cancer chemotherapy.

Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays

Cancer cell lines are major model systems for mechanistic investigation and drug development. However, protein expression data linked to high-quality DNA, RNA, and drug-screening data have not been available across a large number of cancer cell lines. Using reverse-phase protein arrays, we measured expression levels of ∼230 key cancer-related proteins in >650 independent cell lines, many of which have publically available genomic, transcriptomic, and drug-screening data. Our dataset recapitulates the effects of mutated pathways on protein expression observed in patient samples, and demonstrates that proteins and particularly phosphoproteins provide information for predicting drug sensitivity that is not available from the corresponding mRNAs. We also developed a user-friendly bioinformatic resource, MCLP, to help serve the biomedical research community.

Dishevelled segment polarity protein 3 (DVL3): a novel and easily applicable recurrence predictor in localised prostate adenocarcinoma

OBJECTIVE

To identify new biomarkers for biochemical recurrence (BCR) of prostate adenocarcinoma.

PATIENTS AND METHODS

Clinical information of 500 patients with prostate adenocarcinoma and their 152 RNA-sequencing and protein-array data from The Cancer Genome Atlas (TCGA) were separated into a discovery set and a validation set. Each dataset was analysed according to the Gleason grade groups reflecting BCR. The results obtained from the analysis using TCGA dataset were confirmed by immunohistochemistry analyses of a confirmation cohort composed of 395 patients with localised prostate adenocarcinoma.

RESULTS

TCGA discovery set was subgrouped into lower- and higher-risk groups for recurrence-free survival (RFS) (P < 0.001). Cyclin B1 (CCNB1), dishevelled segment polarity protein 3 (DVL3), paxillin (PXN), RAF1, transferrin, X-ray repair cross complementing 5 (XRCC5) and BIM had lower expression in the lower-risk group than that in the higher-risk group (all, P < 0.05). In TCGA validation set, CCNB1, DVL3, transferrin, XRCC5 and BIM were also differently expressed between the two groups. Immunohistochemically, DVL3 positivity was associated with high prostate-specific antigen (PSA) levels, resection margin involvement, and BCR (all, P < 0.05). A high Gleason score indicated a marginal relationship (P = 0.055). BIM positivity was related to high PSA levels, lymphovascular invasion, and BCR (all, P < 0.05). Both DVL3 positivity (P = 0.010) and BIM positivity (P = 0.024) were associated with shorter RFS, but statistical significance was lost when the multivariate Cox regression model included all patients. In the lower-risk group, the multivariate Cox model confirmed that DVL3 was an independent predictor for poor RFS (hazard ratio 1.80, P = 0.040), and the concordance index (C-index) was 0.805.

CONCLUSIONS

DVL3 and BIM were expressed in patients with a higher risk of BCR. DVL3 may be a novel and easily applicable recurrence predictor of localised prostate adenocarcinoma.

Exploring cancer genomic data from the cancer genome atlas project

The Cancer Genome Atlas (TCGA) has compiled genomic, epigenomic, and proteomic data from more than 10,000 samples derived from 33 types of cancer, aiming to improve our understanding of the molecular basis of cancer development. Availability of these genome-wide information provides an unprecedented opportunity for uncovering new key regulators of signaling pathways or new roles of pre-existing members in pathways. To take advantage of the advancement, it will be necessary to learn systematic approaches that can help to uncover novel genes reflecting genetic alterations, prognosis, or response to treatments. This minireview describes the updated status of TCGA project and explains how to use TCGA data.

Phosphosite-Specific Antibodies: A Brief Update on Generation and Applications

Phosphate addition is a posttranslational modification of proteins, and this modification can affect the activity and other properties of intracellular proteins. Different animal species can be used to generate phosphosite-specific antibodies as either polyclonals or monoclonals, and each approach offers its own benefits and disadvantages. The validation of phosphosite-specific antibodies requires multiple techniques and tactics to demonstrate their specificity. These antibodies can be used in arrays, flow cytometry, and imaging platforms. The specificity of phosphosite-specific antibodies is vital for their use in proteomics and profiling of disease.

Protein Arrays III: Reverse-Phase Protein Arrays

The reverse-phase protein array (RPPA) is to use highly specific antibodies to interrogate pan or posttranslationally modified protein targets, such as phosphorylated proteins, particularly the proteins involved in cell signaling pathways. In this protocol we will cover the preparation of cell (or tissue) lysates, sample printing, antibody validation, antibody interrogation, signal amplification steps, imaging and data analysis. In this protocol, colorimetric catalyzed signal amplification (CSA) chemistry, fluorescence and near-infrared (NIR) based detection methods will be described.