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

Sulfamoylated Estradiol Analogs Targeting the Actin and Microtubule Cytoskeletons Demonstrate Anti-Cancer Properties In Vitro and In Ovo

The microtubule-disrupting agent 2-methoxyestradiol (2-ME) displays anti-tumor and anti-angiogenic properties, but its clinical development is halted due to poor pharmacokinetics. We therefore designed two 2-ME analogs in silico-an ESE-15-one and an ESE-16 one-with improved pharmacological properties. We investigated the effects of these compounds on the cytoskeleton in vitro, and their anti-angiogenic and anti-metastatic properties in ovo. Time-lapse fluorescent microscopy revealed that sub-lethal doses of the compounds disrupted microtubule dynamics. Phalloidin fluorescent staining of treated cervical (HeLa), metastatic breast (MDA-MB-231) cancer, and human umbilical vein endothelial cells (HUVECs) displayed thickened, stabilized actin stress fibers after 2 h, which rearranged into a peripheral radial pattern by 24 h. Cofilin phosphorylation and phosphorylated ezrin/radixin/moesin complexes appeared to regulate this actin response. These signaling pathways overlap with anti-angiogenic, extra-cellular communication and adhesion pathways. Sub-lethal concentrations of the compounds retarded both cellular migration and invasion. Anti-angiogenic and extra-cellular matrix signaling was evident with TIMP2 and P-VEGF receptor-2 upregulation. ESE-15-one and ESE-16 exhibited anti-tumor and anti-metastatic properties in vivo, using the chick chorioallantoic membrane assay. In conclusion, the sulfamoylated 2-ME analogs displayed promising anti-tumor, anti-metastatic, and anti-angiogenic properties. Future studies will assess the compounds for myeloproliferative effects, as seen in clinical applications of other drugs in this class.

Randomized phase II study of preoperative afatinib in untreated head and neck cancers: predictive and pharmacodynamic biomarkers of activity

There is no strong and reliable predictive biomarker in head and neck squamous cell carcinoma (HNSCC) for EGFR inhibitors. We aimed to identify predictive and pharmacodynamic biomarkers of efficacy of afatinib, a pan-HER tyrosine kinase inhibitor, in a window-of-opportunity trial (NCT01415674). Multi-omics analyses were carried out on pre-treatment biopsy and surgical specimen for biological assessment of afatinib activity. Sixty-one treatment-naïve and operable HNSCC patients were randomised to afatinib 40 mg/day for 21-28 days versus no treatment. Afatinib produced a high rate of metabolic response. Responders had a higher expression of pERK1/2 (P = 0.02) and lower expressions of pHER4 (P = 0.03) and pRB1 (P = 0.002) in pre-treatment biopsy compared to non-responders. At the cellular level, responders displayed an enrichment of tumor-infiltrating B cells under afatinib (P = 0.02). At the molecular level, NF-kappa B signaling was over-represented among upregulated genes in non-responders (P < 0.001; FDR = 0.01). Although exploratory, phosphoproteomics-based biomarkers deserve further investigations as predictors of afatinib efficacy.

A Longitudinal Study of Individual Radiation Responses in Pediatric Patients Treated with Proton and Photon Radiotherapy, and Interventional Cardiology: Rationale and Research Protocol of the HARMONIC Project

The Health Effects of Cardiac Fluoroscopy and Modern Radiotherapy (photon and proton) in Pediatrics (HARMONIC) is a five-year project funded by the European Commission that aimed to improve the understanding of the long-term ionizing radiation (IR) risks for pediatric patients. In this paper, we provide a detailed overview of the rationale, design, and methods for the biological aspect of the project with objectives to provide a mechanistic understanding of the molecular pathways involved in the IR response and to identify potential predictive biomarkers of individual response involved in long-term health risks. Biological samples will be collected at three time points: before the first exposure, at the end of the exposure, and one year after the exposure. The average whole-body dose, the dose to the target organ, and the dose to some important out-of-field organs will be estimated. State-of-the-art analytical methods will be used to assess the levels of a set of known biomarkers and also explore high-resolution approaches of proteomics and miRNA transcriptomes to provide an integrated assessment. By using bioinformatics and systems biology, biological pathways and novel pathways involved in the response to IR exposure will be deciphered.

Relationships between the abundance of 29 proteins and several meat or carcass quality traits in two bovine muscles revealed by a combination of univariate and multivariate analyses

We aimed to evaluate the relationships between meat or carcass properties and the abundance of 29 proteins quantified in two muscles, Longissimus thoracis and Rectus abdominis, of Rouge des Prés cows. The relative abundance of the proteins was evaluated using a high throughput immunological method: the Reverse Phase Protein array. A combination of univariate and multivariate analyses has shown that small HSPs (CRYAB, HSPB6), fast glycolytic metabolic and structural proteins (MYH1, ENO3, ENO1, TPI1) when assayed both in RA and LT, were related to meat tenderness, marbling, ultimate pH, as well as carcass fat-to-lean ratio or conformation score. In addition to some small HSP, ALDH1A1 and TRIM72 contributed to the molecular signature of muscular and carcass adiposity. MYH1 and HSPA1A were among the top proteins related to carcass traits. We thus shortened the list to 10 putative biomarkers to be considered in future tools to manage both meat and carcass properties. SIGNIFICANCE: In three aspects this manuscript is notable. First, this is the first proteomics study that aims to evaluate putative biomarkers of both meat and carcass qualities that are of economic importance for the beef industry. Second, the relationship between the abundance of proteins and the carcass or meat traits were evaluated by a combination of univariate and multivariate analyses on 48 cows that are representative of the biological variability of the traits. Third, we provide a short list of ten proteins to be tested in a larger population to feed the pipeline of biomarker discovery.

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.

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.

PARP inhibitors and radiation potentiate liver cell death in vitro. Do hepatocellular carcinomas have an achilles' heel?

BACKGROUND

A promising avenue for cancer treatment is exacerbating the deregulation of the DNA repair machinery that would normally protect the genome. To address the applicability of poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) combined with radiotherapy for the treatment of hepatocellular carcinoma (HCC) two approaches were used: firstly, the in vitro sensitivity to the PARPi Veliparib and Talazoparib +/- radiation exposure was determined in liver cell lines and the impact of the HBV X protein (HBx) that deregulates cellular DNA damage repair via SMC5/6 degradation was investigated. Secondly, PARP expression profiles and DNA damage levels using the surrogate marker gammaH2AX were assessed in a panel of control liver vs HCC tissues.

METHODS

Cell cytotoxicity was measured by clonogenic survival or relative cell growth and the DNA damage response using immunological-based techniques in Hep3B, PLC/PRF/5, HepG2- and HepaRG-derived models. Transcriptome changes due to HBx expression vs SMC6 loss were assessed by RNA sequencing in HepaRG-derived models. PARP and PARG transcripts (qPCR) and PARP1, H2AX and gammaH2AX protein levels (RPPA) were compared in control liver vs HBV-, HCV-, alcohol- and non-alcoholic steatohepatitis-associated HCC (tumor/peritumor) tissues.

RESULTS

PARPi cytotoxicity was significantly enhanced when combined with X-rays (2Gy) with Talazoparib having a greater impact than Veliparib in most in vitro models. HBx expression significantly lowered survival, probably driven by SMC5/6 loss based on the transcriptome analysis and higher DNA damage levels. PARP1 and PARP2 transcript levels were significantly higher in tumor than peritumor and control tissues. The HBV/HCV/alcohol-associated tumor tissues studied had reduced H2AX but higher gammaH2AX protein levels compared to peritumor and control tissues providing evidence of increased DNA damage during liver disease progression.

CONCLUSIONS

These proof-of-concept experiments support PARPi alone or combined with radiotherapy for HCC treatment, particularly for HBV-associated tumors, that warrant further investigation.

Spatial Bias in Antibody Microarrays May Be an Underappreciated Source of Variability

Antibody microarrays enable multiplexed protein detection with minimal reagent consumption, but they continue to be plagued by lack of reproducibility. Chemically functionalized glass slides are used as substrates, yet antibody binding spatial inhomogeneity across the slide has not been analyzed in antibody microarrays. Here, we characterize spatial bias across five commercial slides patterned with nine overlapping dense arrays (by combining three buffers and three different antibodies), and we measure signal variation for both antibody immobilization and the assay signal, generating 270 heatmaps. Spatial bias varied across models, and the coefficient of variation ranged from 4.6 to 50%, which was unexpectedly large. Next, we evaluated three layouts of spot replicates-local, random, and structured random-for their capacity to predict assay variation. Local replicates are widely used but systematically underestimate the whole-slide variation by up to seven times; structured random replicates gave the most accurate estimation. Our results highlight the risk and consequences of using local replicates: the underappreciation of spatial bias as a source of variability, poor assay reproducibility, and possible overconfidence in assay results. We recommend the detailed characterization of spatial bias for antibody microarrays and the description and use of distributed positive replicates for research and clinical applications.

Characterization of Signalling Pathways That Link Apoptosis and Autophagy to Cell Death Induced by Estrone Analogues Which Reversibly Depolymerize Microtubules

The search for novel anti-cancer compounds which can circumvent chemotherapeutic drug resistance and limit systemic toxicity remains a priority. 2-Ethyl-3-O-sulphamoyl-estra-1,3,5(10)15-tetraene-3-ol-17one (ESE-15-one) and 2-ethyl-3-O-sulphamoyl-estra-1,3,5(10)16-tetraene (ESE-16) are sulphamoylated 2-methoxyestradiol (2-ME) analogues designed by our research team. Although their cytotoxicity has been demonstrated in vitro, the temporal and mechanistic responses of the initiated intracellular events are yet to be determined. In order to do so, assays investigating the compounds' effects on microtubules, cell cycle progression, signalling cascades, autophagy and apoptosis were conducted using HeLa cervical- and MDA-MB-231 metastatic breast cancer cells. Both compounds reversibly disrupted microtubule dynamics as an early event by binding to the microtubule colchicine site, which blocked progression through the cell cycle at the G₁/S- and G₂/M transitions. This was supported by increased pRB and p27^Kip1 phosphorylation. Induction of apoptosis with time-dependent signalling involving the p-JNK, Erk1/2 and Akt/mTOR pathways and loss of mitochondrial membrane potential was demonstrated. Inhibition of autophagy attenuated the apoptotic response. In conclusion, the 2-ME analogues induced a time-dependent cross-talk between cell cycle checkpoints, apoptotic signalling and autophagic processes, with an increased reactive oxygen species formation and perturbated microtubule functioning appearing to connect the processes. Subtle differences in the responses were observed between the two compounds and the different cell lines.

Proliferation Genes Repressed by TGF-β Are Downstream of Slug/Snail2 in Normal Bronchial Epithelial Progenitors and Are Deregulated in COPD

Slug/Snail2 belongs to the Epithelial-Mesenchymal Transition (EMT)-inducing transcription factors involved in development and diseases. Slug is expressed in adult stem/progenitor cells of several epithelia, making it unique among these transcription factors. To investigate Slug role in human bronchial epithelium progenitors, we studied primary bronchial basal/progenitor cells in an air-liquid interface culture system that allows regenerating a bronchial epithelium. To identify Slug downstream genes we knocked down Slug in basal/progenitor cells from normal subjects and subjects with COPD, a respiratory disease presenting anomalies in the bronchial epithelium and high levels of TGF-β in the lungs. We show that normal and COPD bronchial basal/progenitors, even when treated with TGF-β, express both epithelial and mesenchymal markers, and that the epithelial marker E-cadherin is not a target of Slug and, moreover, positively correlates with Slug. We reveal that Slug downstream genes responding to both differentiation and TGF-β are different in normal and COPD progenitors, with in particular a set of proliferation-related genes that are among the genes repressed downstream of Slug in normal but not COPD. In COPD progenitors at the onset of differentiation in presence of TGF-β,we show that there is positive correlations between the effect of differentiation and TGF-β on proliferation-related genes and on Slug protein, and that their expression levels are higher than in normal cells. As well, the expression of Smad3 and β-Catenin, two molecules from TGF-βsignaling pathways, are higher in COPD progenitors, and our results indicate that proliferation-related genes and Slug protein are increased by different TGF-β-induced mechanisms.

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.

Genetic markers and phosphoprotein forms of beta-catenin pβ-Cat552 and pβ-Cat675 are prognostic biomarkers of cervical cancer

BACKGROUND

Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality world wide and constitutes the third most common malignancy in women. The RAIDs consortium (http://www.raids-fp7.eu/) conducted a prospective European study [BioRAIDs (NCT02428842)] with the objective to stratify CC patients for innovative treatments. A "metagene" of genomic markers in the PI3K pathway and epigenetic regulators had been previously associated with poor outcome [2].

METHODS

To detect new, more specific, targets for treatment of patients who resist standard chemo-radiation, a high-dimensional Cox model was applied to define dominant molecular variants, copy number variations, and reverse phase protein arrays (RPPA).

FINDINGS

Survival analysis on 89 patients with all omics data available, suggested loss-of-function (LOF) or activating molecular alterations in nine genes to be candidate biomarkers for worse prognosis in patients treated by chemo-radiation while LOF of ATRX, MED13 as well as CASP8 were associated with better prognosis. When protein expression data by RPPA were factored in, the supposedly low molecular weight and nuclear form, of beta-catenin, phosphorylated in Ser552 (pβ-Cat552), ranked highest for good prognosis, while pβ-Cat675 was associated with worse prognosis.

INTERPRETATION

These findings call for molecularly targeted treatments involving p53, Wnt pathway, PI3K pathway, and epigenetic regulator genes. Pβ-Cat552 and pβ-Cat675 may be useful biomarkers to predict outcome to chemo-radiation, which targets the DNA repair axis.

FUNDING

European Union's Seventh Program for research, technological development and demonstration (agreement N°304,810), the Fondation ARC pour la recherche contre le cancer.

Protein Array-Based Approach to Evaluate Biomarkers of Beef Tenderness and Marbling in Cows: Understanding of the Underlying Mechanisms and Prediction

This study evaluated the potential of a panel of 20 protein biomarkers, quantified by Reverse Phase Protein Array (RPPA), to explain and predict two important meat quality traits, these being beef tenderness assessed by Warner-Bratzler shear force (WBSF) and the intramuscular fat (IMF) content (also termed marbling), in a large database of 188 Protected Designation of Origin (PDO) Maine-Anjou cows. Thus, the main objective was to move forward in the progression of biomarker-discovery for beef qualities by evaluating, at the same time for the two quality traits, a list of candidate proteins so far identified by proteomics and belonging to five interconnected biological pathways: (i) energy metabolic enzymes, (ii) heat shock proteins (HSPs), (iii) oxidative stress, (iv) structural proteins and (v) cell death and protein binding. Therefore, three statistical approaches were applied, these being Pearson correlations, unsupervised learning for the clustering of WBSF and IMF into quality classes, and Partial Least Squares regressions (PLS-R) to relate the phenotypes with the 20 biomarkers. Irrespective of the statistical method and quality trait, seven biomarkers were related with both WBSF and IMF, including three small HSPs (CRYAB, HSP20 and HSP27), two metabolic enzymes from the oxidative pathway (MDH1: Malate dehydrogenase and ALDH1A1: Retinal dehydrogenase 1), the structural protein MYH1 (Myosin heavy chain-IIx) and the multifunctional protein FHL1 (four and a half LIM domains 1). Further, three more proteins were retained for tenderness whatever the statistical method, among which two were structural proteins (MYL1: Myosin light chain 1/3 and TNNT1: Troponin T, slow skeletal muscle) and one was glycolytic enzyme (ENO3: β-enolase 3). For IMF, two proteins were, in this trial, specific for marbling whatever the statistical method: TRIM72 (Tripartite motif protein 72, negative) and PRDX6 (Peroxiredoxin 6, positive). From the 20 proteins, this trial allowed us to qualify 10 and 9 proteins respectively as strongly related with beef tenderness and marbling in PDO Maine-Anjou cows.

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.

Induction of Acquired Resistance towards EGFR Inhibitor Gefitinib in a Patient-Derived Xenograft Model of Non-Small Cell Lung Cancer and Subsequent Molecular Characterization

In up to 30% of non-small cell lung cancer (NSCLC) patients, the oncogenic driver of tumor growth is a constitutively activated epidermal growth factor receptor (EGFR). Although these patients gain great benefit from treatment with EGFR tyrosine kinase inhibitors, the development of resistance is inevitable. To model the emergence of drug resistance, an EGFR-driven, patient-derived xenograft (PDX) NSCLC model was treated continuously with Gefitinib in vivo. Over a period of more than three months, three separate clones developed and were subsequently analyzed: Whole exome sequencing and reverse phase protein arrays (RPPAs) were performed to identify the mechanism of resistance. In total, 13 genes were identified, which were mutated in all three resistant lines. Amongst them the mutations in NOMO2, ARHGEF5 and SMTNL2 were predicted as deleterious. The 53 mutated genes specific for at least two of the resistant lines were mainly involved in cell cycle activities or the Fanconi anemia pathway. On a protein level, total EGFR, total Axl, phospho-NFκB, and phospho-Stat1 were upregulated. Stat1, Stat3, MEK1/2, and NFκB displayed enhanced activation in the resistant clones determined by the phosphorylated vs. total protein ratio. In summary, we developed an NSCLC PDX line modelling possible escape mechanism under EGFR treatment. We identified three genes that have not been described before to be involved in an acquired EGFR resistance. Further functional studies are needed to decipher the underlying pathway regulation.

Combining the DNA Repair Inhibitor Dbait With Radiotherapy for the Treatment of High Grade Glioma: Efficacy and Protein Biomarkers of Resistance in Preclinical Models

High grade glioma relapses occur often within the irradiated volume mostly due to a high resistance to radiation therapy (RT). Dbait (which stands for DNA strand break bait) molecules mimic DSBs and trap DNA repair proteins, thereby inhibiting repair of DNA damage induced by RT. Here we evaluate the potential of Dbait to sensitize high grade glioma to RT. First, we demonstrated the radiosensitizer properties of Dbait in 6/9 tested cell lines. Then, we performed animal studies using six cell derived xenograft and five patient derived xenograft models, to show the clinical potential and applicability of combined Dbait+RT treatment for human high grade glioma. Using a RPPA approach, we showed that Phospho-H2AX/H2AX and Phospho-NBS1/NBS1 were predictive of Dbait efficacy in xenograft models. Our results provide the preclinical proof of concept that combining RT with Dbait inhibition of DNA repair could be of benefit to patients with high grade glioma.

Clinical and genetic landscape of treatment naive cervical cancer: Alterations in PIK3CA and in epigenetic modulators associated with sub-optimal outcome

BACKGROUND

There is a lack of information as to which molecular processes, present at diagnosis, favor tumour escape from standard-of-care treatments in cervical cancer (CC). RAIDs consortium (www.raids-fp7.eu), conducted a prospectively monitored trial, [BioRAIDs (NCT02428842)] with the objectives to generate high quality samples and molecular assessments to stratify patient populations and to identify molecular patterns associated with poor outcome.

METHODS

Between 2013 and 2017, RAIDs collected a prospective CC sample and clinical dataset involving 419 participant patients from 18 centers in seven EU countries. Next Generation Sequencing has so far been carried out on a total of 182 samples from 377 evaluable (48%) patients, allowing to define dominant genetic alterations. Reverse phase protein expression arrays (RPPA) was applied to group patients into clusters. Activation of key genetic pathways and protein expression signatures were tested for associations with outcome.

FINDINGS

At a median follow up (FU) of 22 months, progression-free survival rates of this FIGO stage IB1-IV population, treated predominantly (87%) by chemoradiation, were65•4% [CI95%: 60•2-71.1]. Dominant oncogenic alterations were seen in PIK3CA (40%), while dominant suppressor gene alterations were seen in KMT2D (15%) and KMT2C (16%). Cumulative frequency of loss-of-function (LOF) mutations in any epigenetic modulator gene alteration was 47% and it was associated with PIK3CA gene alterations in 32%. Patients with tumours harboring alterations in both pathways had a significantly poorer PFS. A new finding was the detection of a high frequency of gains of TLR4 gene amplifications (10%), as well as amplifications, mutations, and non-frame-shift deletions of Androgen receptor (AR) gene in 7% of patients. Finally, RPPA protein expression analysis defined three expression clusters.

INTERPRETATION

Our data suggests that patient population may be stratified into four different treatment strategies based on molecular markers at the outset. FUND: European Union's Seventh Program grant agreement No 304810.

Beef tenderness and intramuscular fat proteomic biomarkers: Effect of gender and rearing practices

This study analyzed the effect of gender on the abundances of 20 protein biomarkers of tenderness and/or intramuscular fat content in five muscles: Longissimus thoracis, previously identified as biomarkers of tenderness and/or intramuscular Semimembranosus, Rectus abdominis, Triceps brachii and Semitendinosus, from cows and steers of the Protected Designation Origin Maine Anjou. The protein abundances were quantified using Reverse Phase Protein Array with specific validated antibodies. Among the 20 studied proteins, the abundance of 8 biomarkers involved in energetic metabolism, contraction and cellular stress, was different according to gender. The gender effect was different depending on the muscle type with greater abundances in Semitendinosus, Rectus abdominis and Longissimus thoracis muscles. On the basis of animal characteristics and rearing factors, three rearing practices classes were identified for cows. Among the factors, fattening duration modified the abundance of 12 proteins mainly in Triceps brachii muscle. A positive correlation between the abundance of the small HSP20 and slaughter age was observed in the 5 muscles. Two proteins, Four and a half LIM domains 1 (FHL1) and Glycogen phosphorylase (PYGB) appeared to be muscle, gender and rearing practices independent. These results constitute valuable data to understand how to manage beef quality by controlling these different factors. SIGNIFICANCE: This study is the first to compare the relative abundance of 20 proteins previously identified as biomarkers of tenderness and/or intramuscular fat (IMF) content of beef meat between cows and steers among 5 different muscles. Its originality is in the use of Reverse Phase Protein Array for fast quantification of the proteins and the integration of data from rearing factors, carcass characteristics and biomarkers of meat qualities. The findings provide evidence for modulating biomarker levels by controlling the choice of animal type and rearing factors according to the type of muscle that would produce animals with the desired meat qualities.

High Precision RPPA: Concept, Features, and Application Performance of the Integrated Zeptosens Platform

An integrated reverse phase protein array (RPPA) platform shall allow the precise monitoring of expression level and changes of proteins and their functional states in a highly parallel manner even when samples exhibit a complex matrix like in tumor tissues and are available only in very limited amounts. Ideally the full workflow from sample preparation to data visualization shall be covered.This book chapter describes the key elements of the integrated Zeptosens RPPA platform. It addresses critical platform and process design requirements, considerations, and elements as well as critical process steps and quality aspects. Sophisticated instrumentation, high sensitivity readout, and dedicated chip and assay handling equipment act in concert with streamlined protocols, optimal reagents, and dedicated lab equipment in the hands of trained users to achieve an outstanding overall performance of the realized system. Based on results from comprehensive signaling protein and pathway profiling studies targeted for preclinical drug efficacy testing and development, it gives an overview of application performance by means of coefficients of variation (CVs) that can be achieved for assay signals from technical and biological sample replicates with this state-of-the-art integrated RPPA platform and process.The Zeptosens RPPA platform has proven to provide valuable biological information with a high level of confidence and has shown its validity in generating sound mechanistic as well as prognostic and predictive information when analyzing cell and tissue materials on the functional protein level.

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.

Inhibition of mTOR downregulates expression of DNA repair proteins and is highly efficient against BRCA2-mutated breast cancer in combination to PARP inhibition

Breast cancer is a complex disease in which each patient could present several genetic alterations that are therapeutically relevant in cancers. Here we explored the therapeutic benefit of combining PARP and mTOR inhibitors in a context of DNA repair deficiency and PI3K pathway activation.

The combination of everolimus and olaparib was tested in BRCA2-mutated patient-derived xenografts (PDX) carrying alterations in the PI3K/AKT/mTOR pathway. An RPPA analysis of different signalling pathways was performed in untreated and treated xenografts.

Everolimus and olaparib showed marked anti-tumor activities in the monotherapy setting and high efficacy when given in combination with 100% of mice showing tumor regressions. The fraction of P-H2AX positive cells was increased in both monotherapy arms and strongly increased in the combination setting. Everolimus given as monotherapy resulted in downregulation of different proteins involved in DNA damage repair, including FANCD2, RAD50 and SUV39H1. In the combination setting, expression of these proteins was almost completely abolished, suggesting convergence of PARP and mTOR in downregulation of DNA damage repair components.

In conclusion, our results suggest that combining mTOR and DNA repair inhibition could be a successful strategy to treat a subset of breast cancer with BRCA2 mutation and alterations in the PI3K/AKT/mTOR pathway.

Beef tenderness and intramuscular fat proteomic biomarkers: muscle type effect

Tenderness and intramuscular fat content are key attributes for beef sensory qualities. Recently some proteomic analysis revealed several proteins which are considered as good biomarkers of these quality traits. This study focuses on the analysis of 20 of these proteins representative of several biological functions: muscle structure and ultrastructure, muscle energetic metabolism, cellular stress and apoptosis. The relative abundance of the proteins was measured by Reverse Phase Protein Array (RPPA) in five muscles known to have different tenderness and intramuscular lipid contents: Longissimus thoracis (LT), Semimembranosus (SM), Rectus abdominis (RA), Triceps brachii (TB) and Semitendinosus (ST). The main results showed a muscle type effect on 16 among the 20 analyzed proteins. They revealed differences in protein abundance depending on the contractile and metabolic properties of the muscles. The RA muscle was the most different by 11 proteins differentially abundant comparatively to the four other muscles. Among these 11 proteins, six were less abundant namely enolase 3 (ENO3), phosphoglucomutase 1 (PGK1), aldolase (ALDOA), myosin heavy chain IIX (MyHC-IIX), fast myosin light chain 1 (MLC1F), triosephosphate isomerase 1 (TPI1) and five more abundant: Heat shock protein (HSP27, HSP70-1A1, αB-crystallin (CRYAB), troponin T slow (TNNT1), and aldolase dehydrogenase 1 (ALDH1A1). Four proteins: HSP40, four and a half LIM domains protein 1 (FHL1), glycogen phosphorylase B (PYGB) and malate dehydrogenase (MDH1) showed the same abundance whatever the muscle. The correlations observed between the 20 proteins in all the five muscles were used to construct a correlation network. The proteins the most connected with the others were in the following order MyHC-IIX, CRYAB, TPI1, PGK1, ALDH1A1, HSP27 and TNNT1. This knowledge is important for understanding the biological functions related to beef tenderness and intramuscular fat content.

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.

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

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

Reverse phase protein arrays for the identification/validation of biomarkers of beef texture and their use for early classification of carcasses

The validation of biomarkers and tools for the prediction of beef texture remains a challenging task. In this study, reverse phase protein arrays (RPPA) quantified 29 protein biomarkers in the m. Longissimus thoracis of Charolais cattle sampled early post-mortem. Myosin heavy chain 1 (MHC1, slow-oxidative fibers) and Retinal dehydrogenase 1 (ALDH1A1, oxidative enzyme) discriminated between tender and juicy vs. tough meat with residues classes and are validated as prime biomarkers of beef texture. Several proteins belonging to energy metabolism, heat shock and oxidative stress, cytoskeletal, cell signaling and apoptosis were related with tenderness. Among the unusual proteins, Four and a half LIM domains 1 (FHL1) and Tripartite motif protein 72 (TRIM72) correlated respectively negatively and positively with beef tenderness. Principal component regression was used for the first time to explain beef texture traits using biomarkers. The results are very promising as they revealed sophisticated mechanisms behind the tenderizing process.

Protein biomarkers predictive for response to anti-EGFR treatment in RAS wild-type metastatic colorectal carcinoma

BACKGROUND

Metastatic colorectal cancer (mCRC) patients with mutant KRAS or NRAS are ineligible for anti-epidermal growth factor receptor (anti-EGFR) therapy, as RAS mutations activate downstream pathways independently of EGFR and induce primary resistance. However, even among RAS wild-type (WT) patients, only a fraction responds to anti-EGFR therapy, suggesting that other mechanisms of resistance exist. We hypothesise that different (epi)genetic alterations can lead to primary anti-EGFR resistance and that the crucial end point is the activation of protein signalling pathways.

METHODS

We analysed the expression and activation of proteins involved in cell signalling, using reverse phase protein arrays, on a multicentre French cohort of RAS WT mCRC treated with anti-EGFR treatment.

RESULTS

We identify activated EGFR and HER3 as protein biomarkers predictive for better overall survival. Active EGFR signalling and downstream PI3K, but not MAPK, pathway activation are associated with response to anti-EGFR treatment. Left-sided mCRC displays active ErbB2/3 and Wnt pathways and a better response to anti-EGFR therapy compared to right-sided mCRC.

CONCLUSIONS

We identify active EGFR and PI3K signalling as a key factor for response to anti-EGFR treatment in mCRC and highlight the importance of developing these biomarkers in clinical practice for the selection of RAS WT mCRC patients that would benefit from anti-EGFR treatment.

Predictive biomarkers of resistance to hypofractionated radiotherapy in high grade glioma

Background

Radiotherapy plays a major role in the management of high grade glioma. However, the radioresistance of glioma cells limits its efficiency and drives recurrence inside the irradiated tumor volume leading to poor outcome for patients. Stereotactic hypofractionated radiotherapy is one option for recurrent high grade gliomas. Optimization of hypofractionated radiotherapy with new radiosensitizing agents requires the identification of robust druggable targets involved in radioresistance.

Methods

We generated 11 xenografted glioma models: 6 were derived from cell lines (1 WHO grade III and 5 grade IV) and 5 were patient derived xenografts (2 WHO grade III and 3 grade IV). Xenografts were treated by hypofractionated radiotherapy (6x5Gy). We searched for 89 biomarkers of radioresistance (39 total proteins, 26 phosphoproteins and 24 ratios of phosphoproteins on total proteins) using Reverse Phase Protein Array.

Results

Both type of xenografted models showed equivalent spectrum of sensitivity and profile of response to hypofractionated radiotherapy. We report that Phospho-EGFR/EGFR, Phospho-Chk1/Chk1 and VCP were associated to resistance to hypofractionated radiotherapy.

Conclusions

Several compounds targeting EGFR or CHK1 are already in clinical use and combining them with stereotactic hypofractionated radiotherapy for recurrent high grade gliomas might be of particular interest.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-017-0858-0) contains supplementary material, which is available to authorized users.

Clustering and Network Analysis of Reverse Phase Protein Array Data

Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

Reverse Phase Protein Arrays and Drug Discovery

Reverse Phase Protein Arrays (RPPA) represent a sensitive antibody-based proteomic approach, which enables simultaneous quantification of the abundance of multiple proteins and posttranslational modifications across multiple samples. Here, we provide protocols for RPPA performed on two distinct protein-binding substrates associated with two most commonly used RPPA platform technologies. We compare and contrast the respective advantages and limitations of each platform within the context of drug discovery applications.

Co-occurring Mutations of Tumor Suppressor Genes, LATS2 and NF2, in Malignant Pleural Mesothelioma

Purpose: To better define malignant pleural mesothelioma (MPM) heterogeneity and identify molecular subtypes of MPM, we focus on the tumor suppressor gene LATS2, a member of the Hippo signaling pathway, which plays a key role in mesothelial carcinogenesis.Experimental Design: Sixty-one MPM primary cultures established in our laboratory were screened for mutations in LATS2 Gene inactivation was modeled using siRNAs. Gene and protein expressions were analyzed by quantitative RT-PCR, Western blot analysis, and reverse phase protein array. Cell proliferation, viability, apoptosis, mobility, and invasion were determined after siRNA knockdown or YAP (verteporfin), mTOR (rapamycin), and mTOR/PI3K/AKT (PF-04691502) inhibitor treatment.Results: The LATS2 gene was altered in 11% of MPM by point mutations and large exon deletions. Genetic data coupled with transcriptomic data allowed the identification of a new MPM molecular subgroup, C2^LN, characterized by a co-occurring mutation in the LATS2 and NF2 genes in the same MPM. MPM patients of this subgroup presented a poor prognosis. Coinactivation of LATS2 and NF2 leads to loss of cell contact inhibition between MPM cells. Hippo signaling pathway activity, mTOR expression, and phosphorylation were altered in the C2^LN MPM subgroup. MPMs of this new subgroup show higher sensitivity to PF-04691502 inhibitor. The MOK gene was identified as a potential biomarker of the C2^LN MPM subgroup and PF-04691502 sensitivity.Conclusions: We identified a new MPM molecular subgroup that shares common genetic and transcriptomic characteristics. Our results made it possible to highlight a greater sensitivity to an anticancer compound for this MPM subgroup and to identify a specific potential biomarker. Clin Cancer Res; 23(12); 3191-202. ©2016 AACR.

Characterization of protein expression levels with label-free detected reverse phase protein arrays

In reverse-phase protein arrays (RPPA), one immobilizes complex samples (e.g., cellular lysate, tissue lysate or serum etc.) on solid supports and performs parallel reactions of antibodies with immobilized protein targets from the complex samples. In this work, we describe a label-free detection of RPPA that enables quantification of RPPA data and thus facilitates comparison of studies performed on different samples and on different solid supports. We applied this detection platform to characterization of phosphoserine aminotransferase (PSAT) expression levels in Acanthamoeba lysates treated with artemether and the results were confirmed by Western blot studies.

Global Conservation of Protein Status between Cell Lines and Xenografts

Common preclinical models for testing anticancer treatment include cultured human tumor cell lines in monolayer, and xenografts derived from these cell lines in immunodeficient mice. Our goal was to determine how similar the xenografts are compared with their original cell line and to determine whether it is possible to predict the stability of a xenograft model beforehand. We studied a selection of 89 protein markers of interest in 14 human cell cultures and respective subcutaneous xenografts using the reverse-phase protein array technology. We specifically focused on proteins and posttranslational modifications involved in DNA repair, PI3K pathway, apoptosis, tyrosine kinase signaling, stress, cell cycle, MAPK/ERK signaling, SAPK/JNK signaling, NFκB signaling, and adhesion/cytoskeleton. Using hierarchical clustering, most cell culture-xenograft pairs cluster together, suggesting a global conservation of protein signature. Particularly, Akt, NFkB, EGFR, and Vimentin showed very stable protein expression and phosphorylation levels highlighting that 4 of 10 pathways were highly correlated whatever the model. Other proteins were heterogeneously conserved depending on the cell line. Finally, cell line models with low Akt pathway activation and low levels of Vimentin gave rise to more reliable xenograft models. These results may be useful for the extrapolation of cell culture experiments to in vivo models in novel targeted drug discovery.

Prognostic value of a newly identified MALAT1 alternatively spliced transcript in breast cancer

Background:

Epigenetic deregulation is considered as a new hallmark of cancer. The long non-coding RNA MALAT1 has been implicated in several cancers; however, its role in breast cancer is still little known.

Methods:

We used RT–PCR, in situ hybridisation, and RPPA methods to quantify (i) the full-length (FL) and an alternatively spliced variant (Δsv) of MALAT1, and (ii) a panel of transcripts and proteins involved in MALAT1 pathways, in a large series of breast tumours from patients with known clinical/pathological status and long-term outcome.

Results:

MALAT1 was overexpressed in 14% (63/446) of the breast tumours. MALAT1-overexpressed tumour epithelial cells showed marked diffuse nuclear signals and numerous huge nuclear speckles. Screening of the dbEST database led to the identification of Δsv-MALAT1, a major alternatively spliced MALAT1 transcript, with a very different expression pattern compared with FL-MALAT1. This alternative Δsv-MALAT1 transcript was mainly underexpressed (18.8%) in our breast tumour series. Multivariate analysis showed that alternative Δsv-MALAT1 transcript is an independent prognostic factor. Δsv-MALAT1 expression was associated with alterations of the pre-mRNAs alternative splicing machinery, and of the Drosha-DGCR8 complex required for non-coding RNA biogenesis. Alternative Δsv-MALAT1 transcript expression was associated to YAP protein status and with an activation of the PI3K-AKT pathway.

Conclusions:

Our results reveal a complex expression pattern of various MALAT1 transcript variants in breast tumours, and suggest that this pattern of expressions should be taken into account to evaluate MALAT1 as predictive biomarker and therapeutic target.

Chronic oxidative stress promotes H2AX protein degradation and enhances chemosensitivity in breast cancer patients

Anti‐cancer drugs often increase reactive oxygen species (ROS) and cause DNA damage. Here, we highlight a new cross talk between chronic oxidative stress and the histone variant H2AX, a key player in DNA repair. We observe that persistent accumulation of ROS, due to a deficient JunD‐/Nrf2‐antioxidant response, reduces H2AX protein levels. This effect is mediated by an enhanced interaction of H2AX with the E3 ubiquitin ligase RNF168, which is associated with H2AX poly‐ubiquitination and promotes its degradation by the proteasome. ROS‐mediated H2AX decrease plays a crucial role in chemosensitivity. Indeed, cycles of chemotherapy that sustainably increase ROS reduce H2AX protein levels in Triple‐Negative breast cancer (TNBC) patients. H2AX decrease by such treatment is associated with an impaired NRF2‐antioxidant response and is indicative of the therapeutic efficiency and survival of TNBC patients. Thus, our data describe a novel ROS‐mediated regulation of H2AX turnover, which provides new insights into genetic instability and treatment efficacy in TNBC patients.

Essential role for cyclic-AMP responsive element binding protein 1 (CREB) in the survival of acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) relapse remains a leading cause of cancer related death in children, therefore, new therapeutic options are needed. Recently, we showed that a peptide derived from Cyclic-AMP Responsive Element Binding Protein (CREB) was highly phosphorylated in pediatric leukemias.

In this study, we determined CREB phosphorylation and mRNA levels showing that CREB expression was significantly higher in ALL compared to normal bone marrow (phosphorylation: P < 0.0001, mRNA: P = 0.004). High CREB and phospho-CREB expression was correlated with a lower median overall survival in a cohort of 140 adult ALL patients. ShRNA mediated knockdown of CREB in ALL cell lines blocked leukemic cell growth by inducing cell cycle arrest and apoptosis. Gene expression array analysis showed downregulation of CREB target genes regulating cell proliferation and glucose metabolism and upregulation of apoptosis inducing genes. Similar to CREB knockdown, the CREB inhibitor KG-501 decreased leukemic cell viability and induced apoptosis in ALL cell lines, as well as primary T-ALL samples, with cases showing high phospho-CREB levels being more sensitive than those with lower phospho-CREB levels.

Together, these in vitro findings support an important role for CREB in the survival of ALL cells and identify this transcription factor as a potential target for treatment.

Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer

Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the most frequent molecular alterations in ILC. We identified two main subtypes of ILCs: (i) an immune related subtype with mRNA up-regulation of PD-L1, PD-1 and CTLA-4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone related subtype, associated with Epithelial to Mesenchymal Transition (EMT), and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using the somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization may help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies.

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.

Subtyping of breast cancer using reverse phase protein arrays

Reverse phase protein arrays (RPPAs) present a robust and sensitive high capacity platform for targeted proteomics that relies on highly specific antibodies to obtain a quantitative readout regarding phosphorylation state and abundance of proteins of interest. This review summarizes the current state of RPPA-based proteomic profiling of breast cancer in the context of existing preanalytical strategies and sample preparation protocols. RPPA-based subtypes identified so far are compared to those obtained by other approaches such as immunohistochemistry, genomics and transcriptomics. Special attention is given to discussing the potential of RPPA for biomarker discovery and biomarker validation.

Reverse phase protein arrays: mapping the path towards personalized medicine

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

miR-137 Targets p160 Steroid Receptor Coactivators SRC1, SRC2, and SRC3 and Inhibits Cell Proliferation

The p160 family of steroid receptor coactivators (SRCs) are pleiotropic transcription factor coactivators and "master regulators" of gene expression that promote cancer cell proliferation, survival, metabolism, migration, invasion, and metastasis. Cancers with high p160 SRC expression exhibit poor clinical outcomes and resistance to therapy, highlighting the SRCs as critical oncogenic drivers and, thus, therapeutic targets. microRNAs are important epigenetic regulators of protein expression. To examine the regulation of p160 SRCs by microRNAs, we used and combined 4 prediction algorithms to identify microRNAs that could target SRC1, SRC2, and SRC3 expression. For validation of these predictions, we assessed p160 SRC protein expression and cell viability after transfection of corresponding microRNA mimetics in breast cancer, uveal melanoma, and prostate cancer (PC) cell lines. Transfection of selected microRNA mimetics into breast cancer, uveal melanoma, and PC cells depleted SRC protein expression levels and exerted potent antiproliferative activity in these cell types. In particular, microRNA-137 (miR-137) depleted expression of SRC1, SRC2, and very potently, SRC3. The latter effect can be attributed to the presence of 3 miR-137 recognition sequences within the SRC3 3'-untranslated region. Using reverse phase protein array analysis, we identified a network of proteins, in addition to SRC3, that were modulated by miR-137 in PC cells. We also found that miR-137 and its host gene are epigenetically silenced in human cancer specimens and cell lines. These results support the development and testing of microRNA-based therapies (in particular based on restoring miR-137 levels) for targeting the oncogenic family of p160 SRCs in cancer.

Modified SuperCurve Method for Analysis of Reverse-Phase Protein Array Data

Reverse-phase protein arrays (RPPAs) are widely used in biological and biomedical fields of study. One of the most popular analytic methods in RPPA data analysis is the SuperCurve method, which requires estimation of the background fluorescence level. This estimation is usually not accurate and has sample bias and spatial bias. Here, we propose a taking-the-difference method to overcome this problem. Briefly, for each two consecutive RPPA cycles, we subtract the later cycle from the earlier cycle, transforming the m-cycle data into m-1 cycle of data. This removes most of the background fluorescence noise. We then use the m-1 cycle of data to fit a new model accordingly derived from the SuperCurve model. To evaluate our proposed method, we compare the accuracy and precision between our proposed model and the original SuperCurve model by testing them on both real and simulated datasets. For both situations, our modified model shows improved results. The modified SuperCurve method is easy to perform and the taking-the-difference idea is recommended for application to all current methods of RPPA data analysis.

Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets

Genomic analyses promise to improve tumor characterization to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors identified mutational signatures associated with specific risk factors, mainly combined alcohol and tobacco consumption and exposure to aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrently altered pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (hepatitis B virus, HBV) and AXIN1. Analyses according to tumor stage progression identified TERT promoter mutation as an early event, whereas FGF3, FGF4, FGF19 or CCND1 amplification and TP53 and CDKN2A alterations appeared at more advanced stages in aggressive tumors. In 28% of the tumors, we identified genetic alterations potentially targetable by US Food and Drug Administration (FDA)-approved drugs. In conclusion, we identified risk factor-specific mutational signatures and defined the extensive landscape of altered genes and pathways in HCC, which will be useful to design clinical trials for targeted therapy.

ATM has a major role in the double-strand break repair pathway dysregulation in sporadic breast carcinomas and is an independent prognostic marker at both mRNA and protein levels

BACKGROUND

Ataxia telangiectasia mutated (ATM) is a kinase that has a central role in the maintenance of genomic integrity by activating cell cycle checkpoints and promoting repair of DNA double-strand breaks (DSB). In breast cancer, a low level of ATM was correlated with poor outcome; however, the molecular mechanism of this downregulation is still unclear.

METHODS

We used qRT-PCR assay to quantify mRNA levels of ATM gene in 454 breast tumours from patients with known clinical/pathological status and outcome; reverse phase protein arrays (RPPA) were used to assess the levels of ATM and 14 proteins in 233 breast tumours.

RESULTS

ATM mRNA was associated with poor metastasis-free survival (MFS) (P=0.00012) on univariate analysis. ATM mRNA and protein levels were positively correlated (P=0.00040). A low level of ATM protein was correlated with poorer MFS (P=0.000025). ATM expression at mRNA or protein levels are independent prognostic factors on multivariate analysis (P=0.00046 and P=0.00037, respectively). The ATM protein level was positively correlated with the levels of six proteins of the DSB repair pathway: H2AX (P<0.0000001), XRCC5 (P<0.0000001), NBN (P<0.0000001), Mre11 (P=0.0000029), Rad50 (P=0.0064), and TP53BP1 (P=0.026), but not with proteins involved in other pathways that are altered in cancer. Low expression of ATM protein was significantly associated with high miR-203 expression (P=0.011).

CONCLUSION

We confirmed that ATM expression is an independent prognostic marker at both RNA and protein levels. We showed that alteration of ATM is involved in dysregulation of the DSB repair pathway. Finally, miR-203 may be responsible for downregulation of ATM in breast cancers.

Spatial normalization of reverse phase protein array data

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

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

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