RNAi-based validation of antibodies for reverse phase protein arrays

Background  

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.     

A highly sensitive near‐infrared fluorescent detection method to analyze signalling pathways by reverse‐phase protein array

The comprehensive and quantitative analysis of the protein phosphorylation patterns in different cellular context is of considerable and general interest. The ability to quantify phosphorylation of discrete signalling proteins in large collections of biological samples would greatly favour the development of systems biology in the field of cell signalling. Reverse‐phase protein array (RPPA) potentially represents a very attractive approach to map signal transduction networks with high throughput. In the present report, we describe an improved detection method for RPPA combining near‐infrared with one or two rounds of tyramide‐based signal amplification. The LOQ was lowered from 6.84 attomoles with a direct detection protocol to 0.21 attomole with two amplification steps. We validated this method in the context of intracellular signal transduction triggered by follicle‐stimulating hormone in HEK293 cells. We consistently detected phosphorylated proteins in the sub‐attomole range from less than 1 ng of total cell extracts. Importantly, the method correlated with Western blot analysis of the same samples while displaying excellent intra‐ and inter‐slide reproducibility. We conclude that RPPA combined with amplified near‐infrared detection can be used to capture the subtle regulations intrinsic to signalling network dynamics at an unprecedented throughput, from minute amounts of biological samples.     

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.     

HER-2 assessment in formalin-fixed paraffin-embedded breast cancer tissue by well-based reverse phase protein array

Background

The human epidermal growth factor receptor-2 (HER-2) expression level is a critical element for determining the prognosis and management of breast cancer. HER-2 targeted therapy in breast cancer depends on the reliable assessment of HER-2 expression status but current standard methods are lacking a rigorous quantitative assay. To address this challenge, we developed an assessment of HER-2 expression method by well-based reverse phase protein array (RPPA).

Results

Well-based RPPA is based on a robust protein isolation methodology paired with a novel electrochemiluminescence detection system. HER-2 value of well-based RPPA significantly correlated with dot blotting results (R² = 0.939). By well-based RPPA, we successfully detected HER-2 expression in 76 human breast formalin-fixed paraffin-embedded tissue samples. We observed 93.4% (71/76) concordance between well-based RPPA and current HER-2 immunohistochemical assessment guideline. When the cutoff level of HER-2 value was set to 0.689 (HER-2/GAPDH) on the basis of receiver-operating characteristic curve, the area under the curve was 0.975 (95% CI, 0.941-1.000). Sensitivity and specificity of well-based RPPA was 92.1% and 94.7%, respectively.

Conclusions

HER-2 value by well-based RPPA was correlated with the current HER-2 status guideline, suggesting that this normalized HER-2 assessment may offer advantages over unnormalized current immunohistochemical assessment methods.     

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.     

Validation of reverse phase protein array for practical screening of potential biomarkers in serum and plasma: accurate detection of CA19-9 levels in pancreatic cancer

The current study analyzed reverse phase protein arrays (RPPA) as a means to experimentally validate biomarkers in blood samples. One microliter samples of sera (n = 71), and plasma (n = 78) were serially diluted and printed on NC-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 noncancer 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.     

Robust estimation of protein expression ratios with lysate microarray technology

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm     

Investigating protein unfolding kinetics by pulse proteolysis

Investigation of protein unfolding kinetics of proteins in crude samples may provide many exciting opportunities to study protein energetics under unconventional conditions. As an effort to develop a method with this capability, we employed “pulse proteolysis” to investigate protein unfolding kinetics. Pulse proteolysis has been shown to be an effective and facile method to determine global stability of proteins by exploiting the difference in proteolytic susceptibilities between folded and unfolded proteins. Electrophoretic separation after proteolysis allows monitoring protein unfolding without protein purification. We employed pulse proteolysis to determine unfolding kinetics of E. coli maltose binding protein (MBP) and E. coli ribonuclease H (RNase H). The unfolding kinetic constants determined by pulse proteolysis are in good agreement with those determined by circular dichroism. We then determined an unfolding kinetic constant of overexpressed MBP in a cell lysate. An accurate unfolding kinetic constant was successfully determined with the unpurified MBP. Also, we investigated the effect of ligand binding on unfolding kinetics of MBP using pulse proteolysis. On the basis of a kinetic model for unfolding of MBP•maltose complex, we have determined the dissociation equilibrium constant (K_d) of the complex from unfolding kinetic constants, which is also in good agreement with known K_d values of the complex. These results clearly demonstrate the feasibility and the accuracy of pulse proteolysis as a quantitative probe to investigate protein unfolding kinetics.     

RPPAML/RIMS: A metadata format and an information management system for reverse phase protein arrays

Background  

Reverse Phase Protein Arrays (RPPA) are convenient assay platforms to investigate the presence of biomarkers in tissue lysates. As with other high-throughput technologies, substantial amounts of analytical data are generated. Over 1000 samples may be printed on a single nitrocellulose slide. Up to 100 different proteins may be assessed using immunoperoxidase or immunoflorescence techniques in order to determine relative amounts of protein expression in the samples of interest.     

Evaluation of Protein Profiles From Treated Xenograft Tumor Models Identifies an Antibody Panel for Formalin-fixed and Paraffin-embedded (FFPE) Tissue Analysis by Reverse Phase Protein Arrays (RPPA)

Reverse phase protein arrays (RPPA) are an established tool for measuring the expression and activation status of multiple proteins in parallel using only very small amounts of tissue. Several studies have demonstrated the value of this technique for signaling pathway analysis using proteins extracted from fresh frozen (FF) tissue in line with validated antibodies for this tissue type; however, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation in the clinical setting. Hence, we performed RPPA to measure profiles for a set of 300 protein markers using matched FF and FFPE tissue specimens to identify which markers performed similarly using the RPPA technique in fixed and unfixed tissues. Protein lysates were prepared from matched FF and FFPE tissue specimens of individual tumors taken from three different xenograft models of human cancer. Materials from both untreated mice and mice treated with either anti-HER3 or bispecific anti-IGF-1R/EGFR monoclonal antibodies were analyzed. Correlations between signals from FF and FFPE tissue samples were investigated. Overall, 60 markers were identified that produced comparable profiles between FF and FFPE tissues, demonstrating significant correlation between the two sample types. The top 25 markers also showed significance after correction for multiple testing. The panel of markers covered several clinically relevant tumor signaling pathways and both phosphorylated and nonphosphorylated proteins were represented. Biologically relevant changes in marker expression were noted when RPPA profiles from treated and untreated xenografts were compared. These data demonstrate that, using appropriately selected antibodies, RPPA analysis from FFPE tissue is well feasible and generates biologically meaningful information. The identified panel of markers that generate similar profiles in matched fixed and unfixed tissue samples may be clinically useful for pharmacodynamic studies of drug effect using FFPE tissues.Many human diseases are characterized by abnormalities in complex signaling pathways (1). The expression and activation status of proteins from these deregulated pathways has traditionally been analyzed using single marker techniques such as immunohistochemistry and Western blotting. Although these techniques have provided valuable information on the molecular abnormalities underlying human disease, they are labor intensive, have a low throughput, and often require high sample volume. Furthermore, techniques such as Western blotting are not applicable in the routine clinical setting. Miniaturized parallel immunoassay techniques have been developed in recent years and have played a pivotal role in biomarker discovery (2). Antibody arrays enable multiple potential disease markers to be investigated in a single sample in parallel (3). Beyond this, Reverse Phase Protein Arrays (RPPA)¹ are sensitive high throughput tools that can quantify protein expression levels and activation status (posttranslational modifications such as phosphorylation) in multiple experimental samples simultaneously. The technique requires only minute amounts of samples, printed as lysate arrays onto slides, and hundreds of markers of interest can be investigated, array by array, in a miniaturized dot blot manner. Numerous reports have demonstrated that RPPA can be applied to various sources of cells and tissues to analyze protein profiles, signaling pathway networks, and for the identification of biomarkers (4–13). A recently published workshop report reviews the full potential and advances of RPPA for use in clinical, translational, and basic research (11).In oncology, the parallel profiling of multiple protein markers is particularly desirable to study tumor initiation and progression, to classify tumor disease states on the molecular level, and to discover and monitor biomarkers that can predict therapeutic response or tumor recurrence (14–16). The study of signaling response and analysis of pharmacodynamic (PD) markers upon treatment using in vitro and in vivo test systems (e.g. cell line or patient derived xenograft tumor models) is an established component of preclinical and early clinical drug development. These techniques can provide evidence of target pathway modulation for new therapeutic lead candidate compounds and provide valuable information on the drug mode of action (17), especially in the translational phase. Multiplex analyses of PD biomarkers by RPPA have been performed in vitro using cancer cell lines (18, 19) as well as in patient-derived tumor tissue and blood samples (20, 21) to assess response to treatment and target inhibition. A combination of RPPA signaling pathway mapping and functional PET imaging has recently been successfully evaluated in xenograft models as an early response PD marker for anti-cancer drug efficacy (13).Translating miniaturized multiple protein analysis platforms-such as RPPA - from preclinical to clinical applicability is highly desirable; however, issues such as the limited amount of available clinical samples and tumor heterogeneity must first be addressed. Furthermore, most studies of RPPA in tumor tissue to date have been conducted using proteins extracted from fresh-frozen (FF) tissue specimens; whereas, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation used in clinical pathology laboratories. FFPE yields excellent tissue architecture for histological assessment and enables analysis of individual proteins in situ by techniques such as immunohistochemistry. However, formalin fixation leads to extensive protein–protein and protein–nucleic acid cross-linking (22), which can hamper protein extraction and reduce both the overall yield of extracted protein and the profile of proteins detectable by proteomic techniques (23, 24). Furthermore, formalin-induced cross-linking induces conformational changes in protein structure that can alter the immunoreactivity of some proteins in situ by hiding or altering peptide epitopes (25, 26). Such artifacts are absent from snap-frozen tissue; therefore, protein profiles obtained from FF tissue are likely to reflect the in vivo biology of the tumor more closely. However, FF tumor tissue is not widely available because it is costly to collect and maintain in the clinical setting. FFPE tissue samples are routinely archived by nearly every hospital and offer a unique opportunity to study thousands of samples retrospectively with extensive clinical records and follow-up information.Several groups have now established protocols for retrieving cross-linked proteins from fixed tissues (27–33). These methods are mainly based on the use of concentrated ionic detergents and high temperature protocols closely related to the antigen retrieval methods developed for immunohistochemistry. These studies show that obtaining nondegraded, full-length proteins from FFPE tissues for multiplex analyses is feasible (27–33). More recently, protein extraction techniques optimized for fixed samples have been used to successfully conduct RPPA using FFPE tissue biopsies from different cancer types (34–40). Guo et al. systematically investigated several protein extraction methods and demonstrated that RPPA of FFPE materials is feasible, reproducible and can generate biologically relevant protein profiles (41). Other studies have confirmed the validity of this approach and shown that data generated from RPPA analyses of FFPE tissue demonstrate good concordance with traditional immunohistochemistry markers such as HER2 protein in breast cancer (34, 40). However, to date, analyses have been performed only for a limited set of protein markers.To evaluate whether analysis of a broader panel of protein markers is feasible and generates meaningful data from FFPE tumor tissue sections, we conducted RPPA on matched samples of FF and FFPE tissues using a set of 300 markers, the largest panel reported to date. Our aim was to identify markers that performed similarly when comparing the protein profiles measured in protein extracts from matched FF and FFPE tissue, using RPPA assays established for use in frozen tissues. Correlating selected markers and assays in such a way should qualify RPPA for further use with FFPE tissues of clinical relevance, e.g. in PD marker studies. In this paper, we have specifically focused on the technical issues relevant for using the RPPA platform in a clinical setting, and did not address the biology of the test systems used in detail. However, the models used have been pre-characterized to identify key signaling parameters in context of targeted drug treatment (42). We conducted a systematic comparison of RPPA protein profiles in matched FF and FFPE tumor tissues resected from three different xenograft models of human cancer, each treated with targeted therapeutic antibodies that have previously been shown to achieve tumor growth inhibition. Furthermore, we investigated the effect of targeted drug treatment on protein expression and activation status, and the concordance of matched FF and FFPE tissue RPPA profiles. Finally, with one of the applied tumor models, we compared a set of protein profiles measured with two different multiple assay platforms - the RPPA and the Luminex Bio-Plex system, and discuss their relevance with respect to the analysis of FFPE tissue.     

Protein-protein interaction studies on protein arrays: Effect of detection strategies on signal-to-background ratios

Protein arrays hold great promise for proteome-scale analysis of protein-protein interaction networks, but the technical challenges have hindered their adoption by proteomics researchers. The crucial issue of design and fabrication of protein arrays have been addressed in several studies, but the detection strategies used for identifying protein-protein interactions have received little attention. In this study, we evaluated six different detection strategies to identify four different protein-protein interaction pairs. We discuss each detection approach in terms of signal-to-background (S/B) ratio, ease of use, and adaptability to high-throughput format. Protein arrays for this study were made by expressing both the bait proteins (proteins captured at the surface) and prey proteins (probes) in cell-free rabbit reticulocyte lysate (RRL) systems. Bait proteins were expressed as HaloTag fusions that allow covalent capture on a HaloTag ligand-coated glass without any prior protein purification step. Prey proteins were expressed and modified with either tags (protein or peptides) or labels (fluorescent or radiometric) for detection. This simple method for creating protein arrays in combination with our analyses of several detection strategies should increase the usefulness of protein array technologies.     

Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT)

In the past decade, shotgun proteomic analysis has been utilized extensively to answer complex biological questions. New challenges arise in large scale proteomic profiling when dealing with complex biological mixtures such as the mammalian cell lysate. In this study, we explored the approach of protein separation prior to the shotgun multidimensional protein identification technology (MudPIT) analysis. We fractionated the mammalian cancer cell lysate using the PF 2D ProteomeLab system and analyzed the distribution of molecular weight, isoelectric point, and cellular localization of the eluted proteins. As a result, we were able to reduce sample complexity by protein fractionation and increase the possibility of detecting proteins with lower abundance in the complex protein mixture.     

Proteomics as a Quality Control Tool of Pharmaceutical Probiotic Bacterial Lysate Products

Probiotic bacteria have a wide range of applications in veterinary and human therapeutics. Inactivated probiotics are complex samples and quality control (QC) should measure as many molecular features as possible. Capillary electrophoresis coupled to mass spectrometry (CE/MS) has been used as a multidimensional and high throughput method for the identification and validation of biomarkers of disease in complex biological samples such as biofluids. In this study we evaluate the suitability of CE/MS to measure the consistency of different lots of the probiotic formulation Pro-Symbioflor which is a bacterial lysate of heat-inactivated Escherichia coli and Enterococcus faecalis. Over 5000 peptides were detected by CE/MS in 5 different lots of the bacterial lysate and in a sample of culture medium. 71 to 75% of the total peptide content was identical in all lots. This percentage increased to 87–89% when allowing the absence of a peptide in one of the 5 samples. These results, based on over 2000 peptides, suggest high similarity of the 5 different lots. Sequence analysis identified peptides of both E. coli and E. faecalis and peptides originating from the culture medium, thus confirming the presence of the strains in the formulation. Ontology analysis suggested that the majority of the peptides identified for E. coli originated from the cell membrane or the fimbrium, while peptides identified for E. faecalis were enriched for peptides originating from the cytoplasm. The bacterial lysate peptides as a whole are recognised as highly conserved molecular patterns by the innate immune system as microbe associated molecular pattern (MAMP). Sequence analysis also identified the presence of soybean, yeast and casein protein fragments that are part of the formulation of the culture medium. In conclusion CE/MS seems an appropriate QC tool to analyze complex biological products such as inactivated probiotic formulations and allows determining the similarity between lots.     

Sensitive single-molecule protein quantification and protein complex detection in a microarray format

Single-molecule protein analysis provides sensitive protein quantitation with a digital read-out and is promising for studying biological systems and detecting biomarkers clinically. However, current single-molecule platforms rely on the quantification of one protein at a time. Conventional antibody microarrays are scalable to detect many proteins simultaneously, but they rely on less sensitive and less quantitative quantification by the ensemble averaging of fluorescent molecules. Here, we demonstrate a single-molecule protein assay in a microarray format enabled by an ultra-low background surface and single-molecule imaging. The digital read-out provides a highly sensitive, low femtomolar limit of detection and four orders of magnitude of dynamic range through the use of hybrid digital-analog quantification. From crude cell lysate, we measured levels of p53 and MDM2 in parallel, proving the concept of a digital antibody microarray for use in proteomic profiling. We also applied the single-molecule microarray to detect the p53-MDM2 protein complex in cell lysate. Our study is promising for development and application of single-molecule protein methods because it represents a technological bridge between single-plex and highly multiplex studies.     

Exploring the impact of nucleic acids on protein stability in bacterial cell lysate

- Addition of salt enhanced thermal stability of model substrate proteins by reducing electrostatic repulsion between protein molecules.- However, the opposite effect was observed with bacterial cell lysate, indicating that certain molecules within the lysate could enhance protein stability via electrostatic interactions.- Such molecules present in cell lysate were found to be nucleic acids known to have a potent anti-aggregation activity toward proteins involving electrostatic interactions.- Nucleic acids showed chaperone activity in physiological salt concentration within cells and in buffer or medium commonly used in experiments.- The chaperone activity of nucleic acids should be taken into account when performing various in vitro assays using cell lysate or samples containing nucleic acids.     

Multiple Protein Analysis of Formalin-fixed and Paraffin-embedded Tissue Samples with Reverse phase Protein Arrays

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.Many diseases are characterized by the expression of specific proteins and the activation status of distinct signaling pathways (1). Thus, protein expression profiling and activation patterns are instrumental for understanding disease, the development of effective treatments, and the identification of patients who will respond to particular therapies. Traditional ways of analyzing protein expression (e.g. Western blot) can be used for these purposes but often are labor intensive, have low throughput, and consume high sample volumes. Reverse-phase protein array (RPPA)¹ technology is a very promising method that circumvents these issues (2–4). For RPPA, minute amounts of whole protein lysates from a multitude of samples are spotted onto slides, and individual proteins are detected via protein-specific antibodies. This enables medium- to high-throughput analysis of precious low-volume sample material.Lysates for RPPA have so far been generated mainly from cell lines or fresh frozen tissue. However, because of the high amount of effort involved in the use of liquid nitrogen for sample preservation, in almost all hospitals worldwide formalin fixation and paraffin embedding is the preferred method for tissue preservation. Therefore, it would be highly desirable if protein-specific epitopes could be quantitatively extracted and analyzed from formalin-fixed and paraffin embedded (FFPE) tissue, as this would make the majority of clinical specimens accessible for mechanistic protein-based research.In recent years, several research groups have established protocols for protein extraction from FFPE tissue. Common to all of them is the use of high concentrations of ionic detergents, such as sodium dodecyl sulfate, and high temperature. It was shown that these methods even make it possible to extract full-length proteins from FFPE tissue (5–12). The coefficient of variation of the relative extraction efficiency based on Western blot and densitometric assessment of actin typically is below 20% (13). To assess whether the analysis of FFPE tissue lysates would result in biologically meaningful data, we analyzed FFPE breast cancer tissue samples by RPPA for the expression of human epidermal growth factor receptor 2 (HER2) and compared it to HER2 assessment by the gold standard used in clinical practice, which is based on immunohistochemistry (IHC). Successful recovery of HER2 from FFPE tissue should result in concordant HER2 classification between RPPA and IHC.In the second part of the study, FFPE samples of non-small cell lung cancer (NSCLC) were examined via RPPA. Samples from two subtypes of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC), were analyzed for more than 150 proteins, including two proteins that are known to be differentially expressed between the two subtypes. The objectives of this analysis were to further assess the validity of the approach by confirming the two positive controls and to identify new markers for the differentiation of the two subtypes of NSCLC.     

Highly specific label-free protein detection from lysed cells using internally referenced microcantilever sensors

We report the investigation of label-free protein detection directly from lysed cells using microcantilever sensors. The integration of an internally referenced microcantilever sensor combined with peptide aptamer technology enables scalable and label-free detection of proteins from a complex biological environment (e.g. cell lysate). The internally referenced microcantilever sensor was found to be effective in minimizing both the effects of thermal drift and non-specific binding interactions with the backside of the cantilever, thereby allowing protein detection in a complex biological background. Highly specific peptide aptamers are used to modify the cantilever surface to specifically detect less than 80nM CDK2 protein from yeast cell lysate. This binding of CDK2 on the microcantilever generates a tensile surface stress of average magnitude equal to 70+/-22mN/m. Similar experiments conducted with quartz crystal microbalance (QCM) technology are consistent with the response observed using microcantilever sensors.     

A framework for personalized medicine: prediction of drug sensitivity in cancer by proteomic profiling

Background

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.

Results

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.

Conclusion

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.