Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition.

Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a multi-omics integration framework, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover consequences of Poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer cells on cell cycle and DNA damage response as well as interferon and hippo signaling. We found that TPP offers a complementary perspective to other omics data modalities, and that its integration allowed us to obtain a more complete molecular overview of PARP inhibition. We anticipate that this strategy can be used to integrate functional proteomics with other omics to study molecular processes.

Multiplexed Proteome Dynamics Profiling Reveals Mechanisms Controlling Protein Homeostasis.

Protein degradation plays important roles in biological processes and is tightly regulated. Further, targeted proteolysis is an emerging research tool and therapeutic strategy. However, proteome-wide technologies to investigate the causes and consequences of protein degradation in biological systems are lacking. We developed "multiplexed proteome dynamics profiling" (mPDP), a mass-spectrometry-based approach combining dynamic-SILAC labeling with isobaric mass tagging for multiplexed analysis of protein degradation and synthesis. In three proof-of-concept studies, we uncover different responses induced by the bromodomain inhibitor JQ1 versus a JQ1 proteolysis targeting chimera; we elucidate distinct modes of action of estrogen receptor modulators; and we comprehensively classify HSP90 clients based on their requirement for HSP90 constitutively or during synthesis, demonstrating that constitutive HSP90 clients have lower thermal stability than non-clients, have higher affinity for the chaperone, vary between cell types, and change upon external stimuli. These findings highlight the potential of mPDP to identify dynamically controlled degradation mechanisms in cellular systems.

Monitoring Cell-surface N-Glycoproteome Dynamics by Quantitative Proteomics Reveals Mechanistic Insights into Macrophage Differentiation.

The plasma membrane proteome plays a crucial role in inter- and intracellular signaling, cell survival, and cell identity. As such, it is a prominent target for pharmacological intervention. The relatively low abundance of this subproteome in conjunction with challenging extractability and solubility still hampers its comprehensive analysis. Here, we combined a chemical glycoprotein-tagging strategy with mass spectrometry to enable comprehensive analysis of the cell-surface glycoproteome. To benchmark this workflow and to provide guidance for cell line selection for functional experiments, we generated an inventory of the N-linked cell-surface glycoproteomes of 15 standard laboratory human cell lines and three primary lymphocytic cell types. On average, about 900 plasma membrane and secreted proteins were identified per experiment, including more than 300 transporters and ion channels. Primary cells displayed distinct expression of surface markers and transporters underpinning the importance of carefully validating model cell lines selected for the study of cell surface-mediated processes. To monitor dynamic changes of the cell-surface proteome in a highly multiplexed experiment, we employed an isobaric mass tag-based chemical labeling strategy. This enabled the time-resolved analysis of plasma membrane protein presentation during differentiation of the monocytic suspension cell line THP-1 into macrophage-like adherent cells. Time-dependent changes observed in membrane protein presentation reflect functional remodeling during the phenotypic transition in three distinct phases: rapid surface presentation and secretion of proteins from intracellular pools concurrent with rapid internalization of no longer needed proteins and finally delayed presentation of newly synthesized macrophage markers. Perturbation of this process using marketed receptor tyrosine kinase inhibitors revealed dasatinib to severely compromise macrophage differentiation due to an off-target activity. This finding suggests that dynamic processes can be highly vulnerable to drug treatment and should be monitored more rigorously to identify adverse drug effects.

Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry.

The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.

Impact of body mass index on neoadjuvant treatment outcome: a pooled analysis of eight prospective neoadjuvant breast cancer trials.

Obesity is associated with an increased risk of breast cancer (BC) and poorer outcome. We assessed the impact of body mass index (BMI) on pathological complete response (pCR), disease-free (DFS), and overall survival (OS), according to BC subtypes in patients with primary BC treated with neoadjuvant chemotherapy. 8,872 patients with primary BC from eight neoadjuvant trials were categorized according to BMI: underweight (<18.5 kg/m(2)), normal weight (18.5 to <25 kg/m(2)), overweight (25 to <30 kg/m(2)), obese (30 to <40 kg/m(2)), and very obese (≥40 kg/m(2)). BC subtypes were defined as luminal-like (ER/PgR-positive and HER2-negative), HER2/luminal (ER/PgR-positive and HER2-positive), HER2-like (ER/PgR-negative and HER2-positive), and triple-negative (TNBC; ER/PgR- and HER2-negative). pCR rate was higher in normal weight patients compared with all other BMI groups (P = 0.003). Mean DFS and OS were shorter in obese (87.3 months, P = 0.014 and 94.9 months, P = 0.001, respectively) and very obese (66.6 months, P < 0.001 and 75.3 months, P < 0.001, respectively) compared with normal weight patients (91.5 and 98.8 months, respectively) which was confirmed by subpopulation treatment effect pattern plot analyses and was consistent in luminal-like and TNBC. No interaction was observed between BMI and pCR. Normal weight patients experienced less non-hematological adverse events (P = 0.002) and were more likely to receive full taxane doses (P < 0.001) compared with all other BMI groups. In multivariable analysis, the dose of taxanes was predictive for pCR (P < 0.001). Higher BMI was associated with lower pCR and a detrimental impact on survival. Normal weight patients had the best compliance to chemotherapy and received the highest taxane doses, which seems to be related with treatment outcomes.

Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers.

PURPOSE: Modulation of immunologic interactions in cancer tissue is a promising therapeutic strategy. To investigate the immunogenicity of human epidermal growth factor receptor 2 (HER2) -positive and triple-negative (TN) breast cancers (BCs), we evaluated tumor-infiltrating lymphocytes (TILs) and immunologically relevant genes in the neoadjuvant GeparSixto trial. PATIENTS AND METHODS: GeparSixto investigated the effect of adding carboplatin (Cb) to an anthracycline-plus-taxane combination (PM) on pathologic complete response (pCR). A total of 580 tumors were evaluated before random assignment for stromal TILs and lymphocyte-predominant BC (LPBC). mRNA expression of immune-activating (CXCL9, CCL5, CD8A, CD80, CXCL13, IGKC, CD21) as well as immunosuppressive factors (IDO1, PD-1, PD-L1, CTLA4, FOXP3) was measured in 481 tumors. RESULTS: Increased levels of stromal TILs predicted pCR in univariable (P < .001) and multivariable analyses (P < .001). pCR rate was 59.9% in LPBC and 33.8% for non-LPBC (P < .001). pCR rates ≥ 75% were observed in patients with LPBC tumors treated with PMCb, with a significant test for interaction with therapy in the complete (P = .002) and HER2-positive (P = .006), but not the TNBC, cohorts. Hierarchic clustering of mRNA markers revealed three immune subtypes with different pCR rates (P < .001). All 12 immune mRNA markers were predictive for increased pCR. The highest odds ratios (ORs) were observed for PD-L1 (OR, 1.57; 95% CI, 1.34 to 1.86; P < .001) and CCL5 (OR, 1.41; 95% CI, 1.23 to 1.62; P < .001). CONCLUSION: Immunologic factors were highly significant predictors of therapy response in the GeparSixto trial, particularly in patients treated with Cb. After further standardization, they could be included in histopathologic assessment of BC.

Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data.

Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.

PIK3CA mutations are associated with lower rates of pathologic complete response to anti-human epidermal growth factor receptor 2 (her2) therapy in primary HER2-overexpressing breast cancer.

PURPOSE: Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in breast cancer, with mutations in PIK3CA being the most common. This study investigated the association between PIK3CA genotype and pathologic complete response (pCR) rates in human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with either dual or single anti-HER2 treatment in addition to neoadjuvant chemotherapy. PATIENTS AND METHODS: PIK3CA mutations in 504 tumor samples from participants in the neoadjuvant GeparQuattro, GeparQuinto, and GeparSixto studies were evaluated. All HER2-positive patients received either trastuzumab or lapatinib or the combination plus anthracycline-taxane chemotherapy. PIK3CA mutations were evaluated in formalin-fixed, paraffin-embedded tissues from core biopsies with a tumor cell content of ≥ 20% by using classical Sanger sequencing of exon 9 and exon 20. RESULTS: Overall, 21.4% of the patients harbored a PIK3CA mutation. Detection of a PIK3CA mutation was significantly associated with a lower pCR rate (19.4% with PIK3CA mutation v 32.8% with PIK3CA wild-type; odds ratio [OR], 0.49; 95% CI, 0.29 to 0.83; P = .008). In the 291 hormone receptor (HR) -positive tumors, pCR rate was 11.3% with a PIK3CA mutation compared with 27.5% with PIK3CA wild-type (OR, 0.34; 95% CI, 0.15 to 0.78; P = .011). In 213 patients with HR-negative tumors, pCR rate was 30.4% with PIK3CA mutation and 40.1% without (OR, 0.65; 95% CI, 0.32 to 1.32; P = .233; interaction test P = .292). In multivariable analysis, HR status and PIK3CA status provided independent predictive information. In patients with PIK3CA mutation, the pCR rates were 16%, 24.3%, and 17.4% with lapatinib, trastuzumab, and the combination, respectively (P = .654) and in the wild-type group, they were 18.2%, 33.%, and 37.1%, respectively (P = .017). Disease-free survival and overall survival were not statistically significantly different between patients with mutant and wild-type PIK3CA. CONCLUSION: HER2-positive breast carcinomas with a PIK3CA mutation are less likely to achieve a pCR after neoadjuvant anthracycline-taxane-based chemotherapy plus anti-HER2 treatment, even if a dual anti-HER2 treatment is given.

Changes in serum levels of miR-21, miR-210, and miR-373 in HER2-positive breast cancer patients undergoing neoadjuvant therapy: a translational research project within the Geparquinto trial.

Trastuzumab and lapatinib are established treatments for patients with HER2 (human epidermal growth factor receptor 2)-positive breast cancer with different mechanisms of action. The focus of this study is to investigate, whether altered expression levels of potentially relevant microRNAs (miRs) in serum are associated with response to trastuzumab or lapatinib. Circulating miR-21, miR-210, and miR-373 were quantified with TaqMan MicroRNA assays in serum of 127 HER2-postive breast cancer patients before and after neoadjuvant therapy and in 19 healthy controls. Patients received chemotherapy combined with either trastuzumab or lapatinib within the prospectively randomized Geparquinto trial. The association between miR levels and pathological response (pCR) to therapy and type of therapy was examined. Serum levels of miR-21 (p = 5.04e-08, p = 1.43e-10), miR-210 (p = 0.00151, p = 1.6e-05), and miR-373 (p = 7.87e-06, p = 1.75e-07) were significantly higher in patients before and after chemotherapy than in healthy women. Concentrations of miR-21 (p = 5.73e-08), miR-210 (p = 0.000724), and miR-373 (p = 0.00209) increased further after chemotherapy. A significant association of higher serum levels of miR-373 with advanced clinical tumor stage could be detected (p < 0.002). An association of miR-21 levels before (p = 0.0091) and after (p = 0.037) chemotherapy with overall survival of the patients could be detected, independent of type of anti-HER2 therapy. No association of circulating miRs with pCR was found. Our findings demonstrate a specific influence of neoadjuvant therapy on the serum levels of miR-21, miR-210, and miR-373 in breast cancer patients together with a prognostic value of miR-21.

Monounsaturated fatty acids in serum triacylglycerols are associated with response to neoadjuvant chemotherapy in breast cancer patients.

Changes in cellular lipid metabolism are a common feature in most solid tumors, which occur already in early stages of the tumor progression. However, it remains unclear if the tumor-specific lipid changes can be detected at the level of systemic lipid metabolism. The objective of this study was to perform comprehensive analysis of lipids in breast cancer patient serum samples. Lipidomic profiling using an established analytical platform was performed in two cohorts of breast cancer patients receiving neoadjuvant chemotherapy. The analyses were performed for 142 patients before and after neoadjuvant chemotherapy, and the results before chemotherapy were validated in an independent cohort of 194 patients. The analyses revealed that in general the tumor characteristics are not reflected in the serum samples. However, there was an association of specific triacylglycerols (TGs) in patients' response to chemotherapy. These TGs containing mainly oleic acid (C18:1) were found in lower levels in those patients showing pathologic complete response before receiving chemotherapy. Some of these TGs were also associated with estrogen receptor status and overall or disease-free survival of the patients. The results suggest that the altered serum levels of oleic acid in breast cancer patients are associated with their response to chemotherapy.

Serum miR-142-3p is associated with early relapse in operable lung adenocarcinoma patients.

The outcome of resectable non-small cell lung cancer (NSCLC) is critically determined by metastatic spread: About 30-50% of early-stage NSCLC patients encounter tumour recurrence within 5 years after surgery. A biomarker-driven stratification of early-stage lung cancer with a high risk of recurrence may improve therapy management and patient care. The aim of this study was to identify microRNAs (miRNAs) in serum of patients associated with early relapse in pulmonary adenocarcinoma. Serum samples were collected from 204 patients before surgery. miRNA screening was done using qRT-PCR based low-density arrays (664 miRNAs) comparing adenocarcinoma patients (n=40) with and without recurrence 24 months after surgery. Selected miRNAs associated with disease recurrence were validated in an independent patient cohort (n=114). miRNAs were also measured in advanced adenocarcinoma patients (n=29), and individuals with benign pulmonary diagnosis (n=21). Circulating miR-142-3p (p=0.005) and miR-29b (p=0.01) were identified in the screening and confirmed in the validation cohort to be increased in sera of early-stage adenocarcinoma patients suffering from recurrence within 24 months. Elevated miRNA levels were exclusively observed in the group of high-risk patient diagnosed for operable adenocarcinoma compared with benign diagnosis or advanced tumour disease. The differentiation between pulmonary adenocarcinoma patients with low and high risk for recurrence was improved by accounting for both miR-142-3p levels and tumour stage (p=0.007; AUC=0.78). In conclusion, circulating miR-142-3p was found to be associated with a high risk of recurrence in early-stage lung adenocarcinoma patients, and a putative serum marker for risk assessment.

Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer.

Early-onset prostate cancer (EO-PCA) represents the earliest clinical manifestation of prostate cancer. To compare the genomic alteration landscapes of EO-PCA with "classical" (elderly-onset) PCA, we performed deep sequencing-based genomics analyses in 11 tumors diagnosed at young age, and pursued comparative assessments with seven elderly-onset PCA genomes. Remarkable age-related differences in structural rearrangement (SR) formation became evident, suggesting distinct disease pathomechanisms. Whereas EO-PCAs harbored a prevalence of balanced SRs, with a specific abundance of androgen-regulated ETS gene fusions including TMPRSS2:ERG, elderly-onset PCAs displayed primarily non-androgen-associated SRs. Data from a validation cohort of > 10,000 patients showed age-dependent androgen receptor levels and a prevalence of SRs affecting androgen-regulated genes, further substantiating the activity of a characteristic "androgen-type" pathomechanism in EO-PCA.

The maternal embryonic leucine zipper kinase (MELK) is upregulated in high-grade prostate cancer.

Loss of cell cycle control is a prerequisite for cancer onset and progression. In prostate cancer, increased activity of cell cycle genes has been associated with prognostic parameters such as biochemical relapse and survival. The identification of novel oncogenic and druggable targets in patient subgroups with poor prognosis may help to develop targeted therapy approaches. We analyzed prostate cancer and corresponding benign tissues (n = 98) using microarrays. The comparison of high- and low-grade tumors (Gleason score ≥ 4 + 3 vs. ≤ 3 + 4) revealed 144 differentially expressed genes (p < 0.05). Out of these, 15 genes were involved in the cell cycle process. The gene maternal embryonic leucine zipper kinase (MELK) was identified to be highly correlated with cell cycle genes like UBE2C, TOP2A, CCNB2, and AURKB. Increased MELK gene expression in high-risk prostate cancer was validated by qPCR in an independent patient cohort (p < 0.005, n = 79). Immunohistochemistry analysis using a tissue microarray (n = 94) revealed increased MELK protein expression in prostate cancer tissues of high Gleason scores. RNAi-based inhibition of MELK in PC3 and LNCaP cells suggested putative function in chromatin modification, embryonic development and cell migration. The concerted inhibition of MELK and other cell cycle targets by the antibiotic siomycin A strongly impaired cell viability of prostate cancer cells, and may point to a novel therapy approach for a subset of high-risk prostate cancer patients.

MYCN-mediated overexpression of mitotic spindle regulatory genes and loss of p53-p21 function jointly support the survival of tetraploid neuroblastoma cells.

High-risk neuroblastomas often harbor structural chromosomal alterations, including amplified MYCN, and usually have a near-di/tetraploid DNA index, but the mechanisms creating tetraploidy remain unclear. Gene-expression analyses revealed that certain MYCN/MYC and p53/pRB-E2F target genes, especially regulating mitotic processes, are strongly expressed in near-di/tetraploid neuroblastomas. Using a functional RNAi screening approach and live-cell imaging, we identified a group of genes, including MAD2L1, which after knockdown induced mitotic-linked cell death in MYCN-amplified and TP53-mutated neuroblastoma cells. We found that MYCN/MYC-mediated overactivation of the metaphase-anaphase checkpoint synergizes with loss of p53-p21 function to prevent arrest or apoptosis of tetraploid neuroblastoma cells.

Joint Bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data.

MOTIVATION: There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse. RESULTS: In this study, we propose Bayesian inference of regulation of transcriptional activity (BIRTA) as a novel approach to infer both, TF and miRNA activities, from combined miRNA and mRNA expression data in a condition specific way. That means our model explains mRNA and miRNA expression for a specific experimental condition by the activities of certain miRNAs and TFs, hence allowing for differentiating between switches from active to inactive (negative switch) and inactive to active (positive switch) forms. Extensive simulations of our model reveal its good prediction performance in comparison to other approaches. Furthermore, the utility of BIRTA is demonstrated at the example of Escherichia coli data comparing aerobic and anaerobic growth conditions, and by human expression data from pancreas and ovarian cancer. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package birta, which is freely available for Bio-conductor (>=2.10) on http://www.bioconductor.org/packages/release/bioc/html/birta.html.

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-ß signaling.

BACKGROUND: TMPRSS2-ERG gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although TMPRSS2-ERG fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear. METHODS: We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays. RESULTS: Comparison of gene expression levels among TMPRSS2-ERG fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like CRISP3 were found to be associated with the gene fusion status. WNT and TGF-ß/BMP signaling pathways were significantly associated with genes upregulated in TMPRSS2-ERG fusion-positive tumors. CONCLUSIONS: The TMPRSS2-ERG gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.

Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer.

BACKGROUND: One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. RESULTS: Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection. CONCLUSIONS: Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement.

Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients.

MOTIVATION: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures. RESULTS: We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes. AVAILABILITY: The R code of the proposed algorithm is given in Supplementary Material.

RPPanalyzer: Analysis of reverse-phase protein array data.

SUMMARY: RPPanalyzer is a statistical tool developed to read reverse-phase protein array data, to perform the basic data analysis and to visualize the resulting biological information. The R-package provides different functions to compare protein expression levels of different samples and to normalize the data. Implemented plotting functions permit a quality control by monitoring data distribution and signal validity. Finally, the data can be visualized in heatmaps, boxplots, time course plots and correlation plots. RPPanalyzer is a flexible tool and tolerates a huge variety of different experimental designs. AVAILABILITY: The RPPAanalyzer is open source and freely available as an R-Package on the CRAN platform http://cran.r-project.org/.

Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification.

BACKGROUND: Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level. RESULTS: A new antibody-mediated signal amplification (AMSA) strategy relying on sequential incubation steps with fluorescently-labeled secondary antibodies reactive against each other is introduced here. The signal quantification is performed in the near-infrared range. The RPPA-based analysis of 14 endogenous proteins in seven different cell lines demonstrated a strong correlation (r = 0.89) between AMSA and standard NIR detection. Probing serial dilutions of human cancer cell lines with different primary antibodies demonstrated that the new amplification approach improved the limit of detection especially for low abundant target proteins. CONCLUSIONS: Antibody-mediated signal amplification is a convenient and cost-effective approach for the robust and specific quantification of low abundant proteins on RPPAs. Contrasting other amplification approaches it allows target protein detection over a large linear range.