Reverse-phase protein arrays (RPPAs) have become an important tool for the sensitive and high-throughput detection of proteins from minute amounts of lysates from cell lines and cryopreserved tissue. The current standard method for tissue preservation in almost all hospitals worldwide is formalin fixation and paraffin embedding, and it would be highly desirable if RPPA could also be applied to formalin-fixed and paraffin embedded (FFPE) tissue. We investigated whether the analysis of FFPE tissue lysates with RPPA would result in biologically meaningful data in two independent studies. In the first study on breast cancer samples, we assessed whether a human epidermal growth factor receptor (HER) 2 score based on immunohistochemistry (IHC) could be reproduced with RPPA. The results showed very good concordance between the IHC and RPPA classifications of HER2 expression. In the second study, we profiled FFPE tumor specimens from patients with adenocarcinoma and squamous cell carcinoma in order to find new markers for differentiating these two subtypes of non-small cell lung cancer. p21-activated kinase 2 could be identified as a new differentiation marker for squamous cell carcinoma. Overall, the results demonstrate the technical feasibility and the merits of RPPA for protein expression profiling in FFPE tissue lysates.
Motivation: Reverse phase protein arrays (RPPA) measure the relative expression levels of a protein in many samples simultaneously. A set of identically spotted arrays can be used to measure the levels of more than one protein. Protein expression within each sample on an array is estimated by borrowing strength across all the samples, but using only within array information. When comparing across slides, it is essential to account for sample loading, the total amount of protein printed per sample. Currently, total protein is estimated using either a housekeeping protein or the sample median across all slides. When the variability in sample loading is large, these methods are suboptimal because they do not account for the fact that the protein expression for each slide is estimated separately.
Results: We propose a new normalization method for RPPA data, called variable slope (VS) normalization, that takes into account that quantification of RPPA slides is performed separately. This method is better able to remove loading bias and recover true correlation structures between proteins.
Availability: Code to implement the method in the statistical package R and anonymized data are available at http://bioinformatics.mdanderson.org/supplements.html.
Supplementary data are available at Bioinformatics online.
The current study analyzed reverse phase protein arrays (RPPA) as a means to experimentally validate biomarkers in blood samples. One µl samples of sera (n=71), and plasma (n=78) were serially diluted and printed on nitrocellulose-coated slides. CA19-9 levels from RPPA results were compared with identical patient samples as measured by ELISA. There was a strong correlation between RPPA and ELISA (r=0.87) as determined by scatter plots. Sample reproducibility of CA19-9 levels was excellent (interslide correlation r=0.88; intraslide correlation r=0.83). The ability of RPPA to accurately distinguish CA19-9 levels between cancer and non-cancer samples were determined using receiver operating characteristic curves and compared with ELISA. The AUC for RPPA and ELISA was comparable (0.87 and 0.86, respectively). When the mean CA19-9 levels of normal samples was used as a cutoff for RPPA and compared with the standard clinical ELISA cutoff, comparable specificities (71% for both) were observed. Notably, RPPA samples normalized to albumin showed increased sensitivity compared to ELISA (90% vs 75%). As RPPA is a high throughput method that shows results comparable to that of ELISA, we propose that RPPA is a viable technique for rapid experimental screening and validation of candidate biomarkers in blood samples.
biomarker; CA19-9; pancreatic cancer; reverse phase protein array
Reverse phase protein array (RPPA) is a powerful dot-blot technology that allows studying protein expression levels as well as post-translational modifications in a large number of samples simultaneously. Yet, correct interpretation of RPPA data has remained a major challenge for its broad-scale application and its translation into clinical research. Satisfying quantification tools are available to assess a relative protein expression level from a serial dilution curve. However, appropriate tools allowing the normalization of the data for external sources of variation are currently missing.
Here we propose a new method, called NormaCurve, that allows simultaneous quantification and normalization of RPPA data. For this, we modified the quantification method SuperCurve in order to include normalization for (i) background fluorescence, (ii) variation in the total amount of spotted protein and (iii) spatial bias on the arrays. Using a spike-in design with a purified protein, we test the capacity of different models to properly estimate normalized relative expression levels. The best performing model, NormaCurve, takes into account a negative control array without primary antibody, an array stained with a total protein stain and spatial covariates. We show that this normalization is reproducible and we discuss the number of serial dilutions and the number of replicates that are required to obtain robust data. We thus provide a ready-to-use method for reliable and reproducible normalization of RPPA data, which should facilitate the interpretation and the development of this promising technology.
The raw data, the scripts and the NormaCurve package are available at the following web site: http://microarrays.curie.fr.
Reverse phase protein arrays (RPPAs) are a powerful high-throughput tool for measuring protein concentrations in a large number of samples. In RPPA technology, the original samples are often diluted successively multiple times, forming dilution series to extend the dynamic range of the measurements and to increase confidence in quantitation. An RPPA experiment is equivalent to running multiple ELISA assays concurrently except that there is usually no known protein concentration from which one can construct a standard response curve. Here, we describe a new method called ‘serial dilution curve for RPPA data analysis’. Compared with the existing methods, the new method has the advantage of using fewer parameters and offering a simple way of visualizing the raw data. We showed how the method can be used to examine data quality and to obtain robust quantification of protein concentrations.
Availability: A computer program in R for using serial dilution curve for RPPA data analysis is freely available at http://odin.mdacc.tmc.edu/~zhangli/RPPA.
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.
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.
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.
Reverse phase protein arrays (RPPA) have been demonstrated to be a useful experimental platform for quantitative protein profiling in a high-throughput format. Target protein detection relies on the readout obtained from a single detection antibody. For this reason, antibody specificity is a key factor for RPPA. RNAi allows the specific knockdown of a target protein in complex samples and was therefore examined for its utility to assess antibody performance for RPPA applications.
To proof the feasibility of our strategy, two different anti-EGFR antibodies were compared by RPPA. Both detected the knockdown of EGFR but at a different rate. Western blot data were used to identify the most reliable antibody. The RNAi approach was also used to characterize commercial anti-STAT3 antibodies. Out of ten tested anti-STAT3 antibodies, four antibodies detected the STAT3-knockdown at 80-85%, and the most sensitive anti-STAT3 antibody was identified by comparing detection limits. Thus, the use of RNAi for RPPA antibody validation was demonstrated to be a stringent approach to identify highly specific and highly sensitive antibodies. Furthermore, the RNAi/RPPA strategy is also useful for the validation of isoform-specific antibodies as shown for the identification of AKT1/AKT2 and CCND1/CCND3-specific antibodies.
RNAi is a valuable tool for the identification of very specific and highly sensitive antibodies, and is therefore especially useful for the validation of RPPA-suitable detection antibodies. On the other hand, when a set of well-characterized RPPA-antibodies is available, large-scale RNAi experiments analyzed by RPPA might deliver useful information for network reconstruction.
Measuring the states of cell signaling pathways in tumor samples promises to advance understanding of oncogenesis and identify response biomarkers. Here, we describe the use of Reverse Phase Protein Arrays (RPPAs or RPLAs) to profile signaling proteins in 56 breast cancers and matched normal tissue. In RPPAs, hundreds to thousands of lysates are arrayed in dense regular grids and each grid is probed with a different antibody (100 in the current work, of which 71 yielded strong signals with breast tissue). Although RPPA technology is quite widely used, measuring changes in phosphorylation reflective of protein activation remains challenging. Using repeat deposition and well-validated antibodies we show that diverse patterns of phosphorylation can be monitored in tumor samples and changes mapped onto signaling networks in a coherent fashion. The patterns are consistent with biomarker-based classification of breast cancers and known mechanisms of oncogenesis. We explore in detail one tumor-associated pattern that involves changes in the abundance of the Axl receptor tyrosine kinase (RTK) and phosphorylation of the cMet RTK. Both cMet and Axl have been implicated in breast cancer, or in resistance to anti-cancer drugs, but the two RTKs are not known to be linked functionally. Protein depletion and over-expression studies in a “triple-negative” breast cells line reveal crosstalk between Axl and cMet involving Axl-mediated modification of cMet, a requirement for cMet in efficient and timely signal transduction by the Axl ligand Gas6 and the potential for the two receptors to interact physically. These findings have potential therapeutic implications since they imply that bi-specific receptor inhibitors (e.g. ATP-competitive small kinase inhibitors such as GSK1363089, BMS-777607 or MP470) may be more efficacious than the monospecific therapeutic antibodies currently in development (e.g. MetMAb).
reverse phase protein arrays; breast cancer; tumor lysate; cell signaling; MET/AXL
The goal of personalized medicine is to provide patients optimal drug screening and treatment based on individual genomic or proteomic profiles. Reverse-Phase Protein Array (RPPA) technology offers proteomic information of cancer patients which may be directly related to drug sensitivity. For cancer patients with different drug sensitivity, the proteomic profiling reveals important pathophysiologic information which can be used to predict chemotherapy responses.
The goal of this paper is to present a framework for personalized medicine using both RPPA and drug sensitivity (drug resistance or intolerance). In the proposed personalized medicine system, the prediction of drug sensitivity is obtained by a proposed augmented naive Bayesian classifier (ANBC) whose edges between attributes are augmented in the network structure of naive Bayesian classifier. For discriminative structure learning of ANBC, local classification rate (LCR) is used to score augmented edges, and greedy search algorithm is used to find the discriminative structure that maximizes classification rate (CR). Once a classifier is trained by RPPA and drug sensitivity using cancer patient samples, the classifier is able to predict the drug sensitivity given RPPA information from a patient.
In this paper we proposed a framework for personalized medicine where a patient is profiled by RPPA and drug sensitivity is predicted by ANBC and LCR. Experimental results with lung cancer data demonstrate that RPPA can be used to profile patients for drug sensitivity prediction by Bayesian network classifier, and the proposed ANBC for personalized cancer medicine achieves better prediction accuracy than naive Bayes classifier in small sample size data on average and outperforms other the state-of-the-art classifier methods in terms of classification accuracy.
Reverse phase protein arrays (RPPA) measure the relative expression levels of a protein in many samples simultaneously. Observed signal from these arrays is a combination of true signal, additive background, and multiplicative spatial effects. Background subtraction alone is not sufficient to remove all nonbiological trends from the data. We developed a surface adjustment that uses information from positive control spots to correct for spatial trends on the array beyond additive background. This method uses a generalized additive model to estimate a smoothed surface from positive controls. When positive controls are printed in a dilution series, a nested surface adjustment performs an intensity-based correction. When applicable, surface adjustment is able to remove spatial trends and increase within slide replicate agreement better than background subtraction alone as demonstrated on two sets of arrays. This work demonstrates the importance of including positive control spots on the array.
protein array; normalization; control spots; generalized additive models
Using Reverse Phase Protein Array (RPPA) we measured protein expression associated with response to primary chemotherapy in patients with advanced-stage high-grade serous ovarian cancer.
Tumor samples were obtained from forty-five patients with advanced high-grade serous cancers from the Gynecology Tumor Bank at the British Columbia Cancer Agency. Treatment consisted of platinum-based chemotherapy following debulking surgery. Protein lysates were prepared from fresh frozen tumor samples and 80 validated proteins from signaling pathways implicated in ovarian carcinogenesis were measured by RPPA. Normalization of Ca-125 by the 3rd cycle of chemotherapy was chosen as the primary outcome measure of chemotherapy response. Logistic regression was used for multivariate analysis to identify protein predictors of Ca-125 normalization, and Cox regression to test for the association between protein expression and PFS. A significance level of p ≤ 0.05 was used.
The mean age at diagnosis was 56.8 years. EGFR, YKL-40 and several TGFβ pathway proteins (c-Jun N-terminal kinase JNK, JNK phosphorylated at residues 183 and 185, PAI-1, Smad3, TAZ) showed significant associations with Ca-125 normalization on univariate testing. On multivariate analysis, EGFR (p < 0.02), JNK (p < 0.01), and Smad3 (p < 0.04) were significantly associated with normalization of Ca-125. Contingency table analysis of pathway-classified proteins revealed that the selection of TGFβ pathway proteins was unlikely due to false discovery (p < 0.007, Bonferroni-adjusted).
TGFβ pathway signaling likely plays an important role as a marker or mediator of chemoresistance in advanced serous ovarian cancer. On this basis, future studies to develop and validate a useful predictor of treatment failure are warranted.
ovarian cancer; functional proteomics; chemotherapy response; reverse phase protein array; Ca-125
The identification of key pathways dysregulated in non-small cell lung cancer (NSCLC) is an important step toward understanding lung pathogenesis and developing new therapeutic approaches.
Toward this goal, reverse-phase protein lysate arrays (RPPA) were used to compare signaling pathways between NSCLC tumors and paired normal lung tissue from 46 patients and assess their association with clinical outcome.
After RPPA quantification of 63 proteins and phosphoproteins, tissue pairs were randomized to a training set (n = 25 pairs) and test set (n = 21 pairs). In the training set, 15 protein markers were differentially expressed between tumors and normal lung (p ≤ 0.01), including markers in the PI3K/AKT and p38 MAPK signaling pathways (e.g., p70S6K, S6, p38, and phospho-p38), as well as caveolin-1 and β-catenin. A four-protein signature (p70S6K, cyclin B1, pSrc(Y527), and caveolin-1) independent of histology classified specimens as tumor versus normal with a predicted accuracy of 83%, sensitivity of 67%, and specificity of 100%. The signature was validated in the test set, correctly classifying all normal tissues and 14 of 21 tumor tissues. RPPA results were confirmed by immunohistochemistry for caveolin-1 and p70S6K. In tumors from patients with resected NSCLC, expression of proteins in the energy-sensing AMPK pathway (pLKB1, AMPK, p-Acetyl-CoA, pTSC2), adhesion, EGFR, and Rb signaling pathways was inversely associated with NSCLC recurrence.
These data provide evidence for dysregulation of several pathways including those involving energy sensing and adhesion that are potentially associated with NSCLC pathogenesis and disease recurrence.
NSCLC; Proteomics; Recurrence; AMPK; Adhesion
Ovarian cancer is now recognized as a number of distinct diseases primarily defined by histological subtype. Both clear cell ovarian carcinomas (CCC) and ovarian endometrioid carcinomas (EC) may arise from endometriosis and frequently harbor mutations in the ARID1A tumor suppressor gene. We studied the influence of histological subtype on protein expression with reverse phase protein array (RPPA) and assessed proteomic changes associated with ARID1A mutation/BAF250a expression in EC and CCC.
Immunohistochemistry (IHC) for BAF250a expression was performed on 127 chemotherapy-naive ovarian carcinomas (33 CCC, 29 EC, and 65 high-grade serous ovarian carcinomas (HGSC)). Whole tumor lysates were prepared from frozen banked tumor samples and profiled by RPPA using 116 antibodies. ARID1A mutations were identified by exome sequencing, and PIK3CA mutations were characterized by MALDI-TOF mass spectrometry. SAM (Significance Analysis of Microarrays) was performed to determine differential protein expression by histological subtype and ARID1A mutation status. Multivariate logistic regression was used to assess the impact of ARID1A mutation status/BAF250a expression on AKT phosphorylation (pAKT). PIK3CA mutation type and PTEN expression were included in the model. BAF250a knockdown was performed in 3 clear cell lines using siRNA to ARID1A.
Marked differences in protein expression were observed that are driven by histotype. Compared to HGSC, SAM identified over 50 proteins that are differentially expressed in CCC and EC. These included PI3K/AKT pathway proteins, those regulating the cell cycle, apoptosis, transcription, and other signaling pathways including steroid hormone signaling. Multivariate models showed that tumors with loss of BAF250a expression showed significantly higher levels of AKT-Thr308 and AKT-Ser473 phosphorylation (p < 0.05). In 31 CCC cases, pAKT was similarly significantly increased in tumors with BAF250a loss on IHC. Knockdown of BAF250a by siRNA in three CCC cell lines wild type for ARID1A showed no increase in either pAKT-Thr308 or pAKT-S473 suggesting that pAKT in tumor tissues is indirectly regulated by BAF250a expression.
Proteomic assessment of CCC and EC demonstrates remarkable differences in protein expression that are dependent on histotype, thereby further characterizing these cancers. AKT phosphorylation is associated with ARID1A/BAF250a deficient tumors, however in ovarian cancers the mechanism remains to be elucidated.
Ovarian cancer; Proteomics; ARID1A/BAF250a; PIK3CA mutation; AKT; Phosphorylation
Because cell signaling and cell metabolic pathways are executed through proteins, protein signatures in primary tumors are useful for identifying key nodes in signaling networks whose alteration is associated with malignancy and/or clinical outcomes. This study aimed to determine protein signatures in primary lung cancer tissues.
Methodology/ Principal Findings
We analyzed 126 proteins and/or protein phosphorylation sites in case-matched normal and tumor samples from 101 lung cancer patients with reverse-phase protein array (RPPA) assay. The results showed that 18 molecules were significantly different (p<0.05) by at least 30% between normal and tumor tissues. Most of those molecules play roles in cell proliferation, DNA repair, signal transduction and lipid metabolism, or function as cell surface/matrix proteins. We also validated RPPA results by Western blot and/or immunohistochemical analyses for some of those molecules. Statistical analyses showed that Ku80 levels were significantly higher in tumors of nonsmokers than in those of smokers. Cyclin B1 levels were significantly overexpressed in poorly differentiated tumors while Cox2 levels were significantly overexpressed in neuroendocrinal tumors. A high level of Stat5 is associated with favorable survival outcome for patients treated with surgery.
Our results revealed that some molecules involved in DNA damage/repair, signal transductions, lipid metabolism, and cell proliferation were drastically aberrant in lung cancer tissues, and Stat5 may serve a molecular marker for prognosis of lung cancers.
Protein extraction from formalin-fixed paraffin-embedded (FFPE) tissues is challenging due to extensive molecular crosslinking that occurs upon formalin fixation. Reverse-phase protein array (RPPA) is a high-throughput technology, which can detect changes in protein levels and protein functionality in numerous tissue and cell sources. It has been used to evaluate protein expression mainly in frozen preparations or FFPE-based studies of limited scope. Reproducibility and reliability of the technique in FFPE samples has not yet been demonstrated extensively. We developed and optimized an efficient and reproducible procedure for extraction of proteins from FFPE cells and xenografts, and then applied the method to FFPE patient tissues and evaluated its performance on RPPA.
Fresh frozen and FFPE preparations from cell lines, xenografts and breast cancer and renal tissues were included in the study. Serial FFPE cell or xenograft sections were deparaffinized and extracted by six different protein extraction protocols. The yield and level of protein degradation were evaluated by SDS-PAGE and Western Blots. The most efficient protocol was used to prepare protein lysates from breast cancer and renal tissues, which were subsequently subjected to RPPA. Reproducibility was evaluated and Spearman correlation was calculated between matching fresh frozen and FFPE samples.
The most effective approach from six protein extraction protocols tested enabled efficient extraction of immunoreactive protein from cell line, breast cancer and renal tissue sample sets. 85% of the total of 169 markers tested on RPPA demonstrated significant correlation between FFPE and frozen preparations (p < 0.05) in at least one cell or tissue type, with only 23 markers common in all three sample sets. In addition, FFPE preparations yielded biologically meaningful observations related to pathway signaling status in cell lines, and classification of renal tissues.
With optimized protein extraction methods, FFPE tissues can be a valuable source in generating reproducible and biologically relevant proteomic profiles using RPPA, with specific marker performance varying according to tissue type.
Formalin-fixed; Paraffin-embedded tissue; Protein extraction; Reverse phase protein array; Breast cancer; Renal cancer
The lack of large panels of validated antibodies, tissue handling variability, and intratumoral heterogeneity potentially hamper comprehensive study of the functional proteome in non-microdissected solid tumors. The purpose of this study was to address these concerns and to demonstrate clinical utility for the functional analysis of proteins in non-microdissected breast tumors using reverse phase protein arrays (RPPA).
Herein, 82 antibodies that recognize kinase and steroid signaling proteins and effectors were validated for RPPA. Intraslide and interslide coefficients of variability were <15%. Multiple sites in non-microdissected breast tumors were analyzed using RPPA after intervals of up to 24 h on the benchtop at room temperature following surgical resection.
Twenty-one of 82 total and phosphoproteins demonstrated time-dependent instability at room temperature with most variability occurring at later time points between 6 and 24 h. However, the 82-protein functional proteomic “fingerprint” was robust in most tumors even when maintained at room temperature for 24 h before freezing. In repeat samples from each tumor, intratumoral protein levels were markedly less variable than intertumoral levels. Indeed, an independent analysis of prognostic biomarkers in tissue from multiple tumor sites accurately and reproducibly predicted patient outcomes. Significant correlations were observed between RPPA and immunohistochemistry. However, RPPA demonstrated a superior dynamic range. Classification of 128 breast cancers using RPPA identified six subgroups with markedly different patient outcomes that demonstrated a significant correlation with breast cancer subtypes identified by transcriptional profiling.
Thus, the robustness of RPPA and stability of the functional proteomic “fingerprint” facilitate the study of the functional proteome in non-microdissected breast tumors.
Functional proteome; RPPA; Breast cancer; Kinase signaling; Steroid signaling
Metadata providing information about the stimulus, data acquisition, and experimental conditions are indispensable for the analysis and management of experimental data within a lab. However, only rarely are metadata available in a structured, comprehensive, and machine-readable form. This poses a severe problem for finding and retrieving data, both in the laboratory and on the various emerging public data bases. Here, we propose a simple format, the “open metaData Markup Language” (odML), for collecting and exchanging metadata in an automated, computer-based fashion. In odML arbitrary metadata information is stored as extended key–value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability. We started to define such terminologies for neurophysiological data, but aim at a community driven extension and refinement of the proposed definitions. By customized terminologies that map to these standard terminologies, metadata can be named and organized as required or preferred without softening the standard. Together with the respective libraries provided for common programming languages, the odML format can be integrated into the laboratory workflow, facilitating automated collection of metadata information where it becomes available. The flexibility of odML also encourages a community driven collection and definition of terms used for annotating data in the neurosciences.
metadata; ontology; neuroscience; datamodel; datasharing
Linking experimental data to mathematical models in biology is impeded by the lack of suitable software to manage and transform data. Model calibration would be facilitated and models would increase in value were it possible to preserve links to training data along with a record of all normalization, scaling, and fusion routines used to assemble the training data from primary results.
We describe the implementation of DataRail, an open source MATLAB-based toolbox that stores experimental data in flexible multi-dimensional arrays, transforms arrays so as to maximize information content, and then constructs models using internal or external tools. Data integrity is maintained via a containment hierarchy for arrays, imposition of a metadata standard based on a newly proposed MIDAS format, assignment of semantically typed universal identifiers, and implementation of a procedure for storing the history of all transformations with the array. We illustrate the utility of DataRail by processing a newly collected set of ~22,000 measurements of protein activities obtained from cytokine-stimulated primary and transformed human liver cells.
In highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system.
Proteins are used as prognostic and predictive biomarkers in breast cancer. However, the variability of protein expression within the same tumor is not well studied. The aim of this study was to assess intratumoral heterogeneity in protein expression levels by reverse-phase-protein-arrays (RPPA) (i) within primary breast cancers and (ii) between axillary lymph node metastases from the same patient. Protein was extracted from 106 paraffin-embedded samples from 15 large (≥3 cm) primary invasive breast cancers, including different zones within the primary tumor (peripheral, intermediate, central) as well as 2–5 axillary lymph node metastases in 8 cases. Expression of 35 proteins including 15 phosphorylated proteins representing the HER2, EGFR, and uPA/PAI-1 signaling pathways was assessed using reverse-phase-protein-arrays. All 35 proteins showed considerable intratumoral heterogeneity within primary breast cancers with a mean coefficient of variation (CV) of 31% (range 22–43%). There were no significant differences between phosphorylated (CV 32%) and non-phosphorylated proteins (CV 31%) and in the extent of intratumoral heterogeneity within a defined tumor zone (CV 28%, range18–38%) or between different tumor zones (CV 24%, range 17–38%). Lymph node metastases from the same patient showed a similar heterogeneity in protein expression (CV 27%, range 18–34%). In comparison, the variation amongst different patients was higher in primary tumors (CV 51%, range 29–98%) and lymph node metastases (CV 65%, range 40–146%). Several proteins showed significant differential expression between different tumor stages, grades, histological subtypes and hormone receptor status. Commonly used protein biomarkers of breast cancer, including proteins from HER2, uPA/PAI-1 and EGFR signaling pathways showed higher than previously reported intratumoral heterogeneity of expression levels both within primary breast cancers and between lymph node metastases from the same patient. Assessment of proteins as diagnostic or prognostic markers may require tumor sampling in several distinct locations to avoid sampling bias.
Traditional Chinese medicine has gained popularity due to its ability to kill tumor cells. Recently, the apoptotic and anti-angiogenic effects of Trametes robiniophila murr (Huaier) have been investigated. The aim of this study was to investigate its effect on cell mobility and tumor growth in ovarian cancer. Cell viability and motility were measured using SRB, scratch and migration assays. Cell apoptosis was analysed by annexin V/PI staining. Using a reverse-phase protein array (RPPA) assay, we analyzed the levels of 153 proteins and/or phosphorylations in Huaier-treated and untreated cells. Huaier inhibited cell viability and induced both early and late apoptosis in SKOV3, SKOV3.ip1 and Hey cells in a time- and dose-dependent manner. Cell invasiveness and migration were also suppressed significantly. The RPPA results showed significant differences (of at least 30%; P <0.05) in the levels of 7 molecules in SKOV3 cells and 10 in SKOV3.ip1 cells between the untreated and treated cells. Most of the molecules identified play roles in cell proliferation, apoptosis or cell adhesion/invasion. Western blot analysis further validated that Huaier treatment resulted in decreased AKT phosphorylation, enhanced expression of total GSK3β, inhibition of the phosphorylation of GSK3β on S9, reduction of both cytoplasmic β-catenin expression and nuclear β-catenin translocation, and transcriptional repression of several Wnt/β-catenin target genes (DIXDC1, LRP6, WNT5A, and cyclin D1). After knocking down GSK3β, β-catenin expression could not be inhibited by Huaier. Finally, Huaier inhibited the growth of ovarian tumor xenografts in vivo. These studies indicate that Huaier inhibits tumor cell mobility in ovarian cancer via the AKT/GSK3β/β-catenin signaling pathway.
Acute myeloid leukemia (AML) is believed to arise from leukemic stem-like cells (LSC) making understanding the biological differences between LSC and normal stem cells (HSC) or common myeloid progenitors (CMP) crucial to understanding AML biology. To determine if protein expression patterns were different in LSC compared to other AML and CD34+ populations, we measured the expression of 121 proteins by Reverse Phase Protein Arrays (RPPA) in 5 purified fractions from AML marrow and blood samples: Bulk (CD3/CD19 depleted), CD34-, CD34+(CMP), CD34+CD38+ and CD34+CD38-(LSC). LSC protein expression differed markedly from Bulk (n=31 cases, 93/121 proteins) and CD34+ cells (n= 30 cases, 88/121 proteins) with 54 proteins being significantly different (31 higher, 23 lower) in LSC than in either Bulk or CD34+ cells. Sixty-seven proteins differed significantly between CD34+ and Bulk blasts (n=69 cases). Protein expression patterns in LSC and CD34+ differed markedly from normal CD34+ cells. LSC were distinct from CD34+ and Bulk cells by principal component and by protein signaling network analysis which confirmed individual protein analysis. Potential targetable submodules in LSC included the proteins PU.1(SP1), P27, Mcl1, HIF1α, cMET, P53, Yap, and phospho-Stats 1, 5 and 6. Protein expression and activation in LSC differs markedly from other blast populations suggesting that studies of AML biology should be performed in LSC.
A large number of patients suffering from oesophageal adenocarcinomas do not respond to conventional chemotherapy; therefore, it is necessary to identify new predictive biomarkers and patient signatures to improve patient outcomes and therapy selections. We analysed 87 formalin-fixed and paraffin-embedded (FFPE) oesophageal adenocarcinoma tissue samples with a reverse phase protein array (RPPA) to examine the expression of 17 cancer-related signalling molecules. Protein expression levels were analysed by unsupervised hierarchical clustering and correlated with clinicopathological parameters and overall patient survival. Proteomic analyses revealed a new, very promising molecular subtype of oesophageal adenocarcinoma patients characterised by low levels of the HSP27 family proteins and high expression of those of the HER family with positive lymph nodes, distant metastases and short overall survival. After confirmation in other independent studies, our results could be the foundation for the development of a Her2-targeted treatment option for this new patient subgroup of oesophageal adenocarcinoma.
To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline-taxane-based systemic therapy (NST).
Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR.
Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021).
We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.
Breast Cancer; Functional Proteomics; Prognosis; Prediction
The presence of the Philadelphia chromosome in patients with acute lymphoblastic leukemia (Ph+ALL) is a negative prognostic indicator. Tyrosine kinase inhibitors (TKI) that target BCR/ABL, such as imatinib, have improved treatment of Ph+ALL and are generally incorporated into induction regimens. This approach has improved clinical responses, but molecular remissions are seen in less than 50% of patients leaving few treatment options in the event of relapse. Thus, identification of additional targets for therapeutic intervention has potential to improve outcomes for Ph+ALL. The human epidermal growth factor receptor 2 (ErbB2) is expressed in ∼30% of B-ALLs, and numerous small molecule inhibitors are available to prevent its activation. We analyzed a cohort of 129 ALL patient samples using reverse phase protein array (RPPA) with ErbB2 and phospho-ErbB2 antibodies and found that activity of ErbB2 was elevated in 56% of Ph+ALL as compared to just 4.8% of Ph−ALL. In two human Ph+ALL cell lines, inhibition of ErbB kinase activity with canertinib resulted in a dose-dependent decrease in the phosphorylation of an ErbB kinase signaling target p70S6-kinase T389 (by 60% in Z119 and 39% in Z181 cells at 3 µM). Downstream, phosphorylation of S6-kinase was also diminished in both cell lines in a dose-dependent manner (by 91% in both cell lines at 3 µM). Canertinib treatment increased expression of the pro-apoptotic protein Bim by as much as 144% in Z119 cells and 49% in Z181 cells, and further produced caspase-3 activation and consequent apoptotic cell death. Both canertinib and the FDA-approved ErbB1/2-directed TKI lapatinib abrogated proliferation and increased sensitivity to BCR/ABL-directed TKIs at clinically relevant doses. Our results suggest that ErbB signaling is an additional molecular target in Ph+ALL and encourage the development of clinical strategies combining ErbB and BCR/ABL kinase inhibitors for this subset of ALL patients.