The Experts below are selected from a list of 78060 Experts worldwide ranked by ideXlab platform
Gordon B Mills - One of the best experts on this subject based on the ideXlab platform.
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using reverse phase Protein Array rppa to identify and target adaptive resistance
Advances in Experimental Medicine and Biology, 2019Co-Authors: Marilyne Labrie, Yong Fang, Nicholas D Kendsersky, Han Liang, Shannon N Westin, Zahi Mitri, Gordon B MillsAbstract:Tumor cells and the tumor ecosystem rapidly evolve in response to therapy. This tumor evolution results in the rapid emergence of drug resistance that limits the magnitude and duration of response to therapy including chemotherapy, targeted therapy, and immunotherapy. Thus, there is an urgent need to understand and interdict tumor evolution to improve patient benefit to therapy. Reverse phase Protein Array (RPPA) provides a powerful tool to evaluate and develop approaches to target the processes underlying one form of tumor evolution: adaptive evolution. Tumor cells and the tumor microenvironment rapidly evolve through rewiring of Protein networks to bypass the effects of therapy. In this review, we present the concepts underlying adaptive resistance and use of RPPA in understanding resistance mechanisms and identification of effective drug combinations. We further demonstrate that this novel information is resulting in biomarker-driven trials aimed at targeting adaptive resistance and improving patient outcomes.
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preclinical evaluation and reverse phase Protein Array based profiling of pi3k and mek inhibitors in endometrial carcinoma in vitro
BMC Cancer, 2018Co-Authors: Ozlem Aslan, Gordon B Mills, Mattia Cremona, Clare Morgan, Lydia W T Cheung, Bryan T HennessyAbstract:The phosphoinositide-3-kinase (PI3K) pathway is the most commonly activated pathway in cancers due to mutations at multiple nodes and loss of PTEN. Furthermore, in endometrial cancer (EC), PI3K and RAS/RAF/MEK/MAPK (RAS/MAPK herein) pathway mutations frequently co-exist. We examined the role of PI3K and RAS/MAPK pathway mutations in determining responsiveness to therapies targeted to these pathways in vitro in EC. 13 EC cell lines were profiled for their PI3K pathway and KRAS mutational and PTEN Protein status and treated with one MEK- and two PI3K- targeted inhibitors alone and in combination. Expression and phosphorylation of 66 Proteins were evaluated by Reverse-Phase-Protein-Array (RPPA) in 6 EC cell lines to identify signalling changes in these pathways in response to therapy. PTEN Protein loss and the absence of any tested pathway mutations are dominant negative predictors of sensitivity to MEK inhibition. KRAS-mutated cells were most sensitive to MEK inhibition, but significantly more resistant to PI3K inhibition than KRAS-wild-type cell lines. Combinations of PI3K and MEK inhibitors showed synergy or additivity in all but two cell lines tested. Treatment of KRAS-mutated cells with PI3K inhibitors and treatment of PTEN-low cells with a MEK inhibitor were most likely to induce activation of MEK/MAPK and AKT, respectively, likely indicative of feedback-loop regulation. MEK inhibition may be a promising treatment modality, not just for ECs with mutated KRAS, but also for those with retained PTEN. Up-regulation of MEK/MAPK signalling by PI3K inhibition, and up-regulation of AKT activation by MEK inhibition may serve as potential biomarkers of likely responsiveness to each inhibitor.
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spatial normalization of reverse phase Protein Array data
PLOS ONE, 2014Co-Authors: Poorvi Kaushik, Gordon B Mills, Wenbin Liu, Evan J Molinelli, Martin L Miller, Weiqing Wang, Anil Korkut, Chris SanderAbstract: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.
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A Comprehensive Comparison of Normalization Methods for Loading Control and Variance Stabilization of Reverse-Phase Protein Array Data
Cancer informatics, 2014Co-Authors: Wenbin Liu, Gordon B Mills, Rehan AkbaniAbstract:Loading control (LC) and variance stabilization of reverse-phase Protein Array (RPPA) data have been challenging mainly due to the small number of Proteins in an experiment and the lack of reliable inherent control markers. In this study, we compare eight different normalization methods for LC and variance stabilization. The invariant marker set concept was first applied to the normalization of high-throughput gene expression data. A set of “invariant” markers are selected to create a virtual reference sample. Then all the samples are normalized to the virtual reference. We propose a variant of this method in the context of RPPA data normalization and compare it with seven other normalization methods previously reported in the literature. The invariant marker set method performs well with respect to LC, variance stabilization and association with the immunohistochemistry/florescence in situ hybridization data for three key markers in breast tumor samples, while the other methods have inferior performance. The proposed method is a promising approach for improving the quality of RPPA data.
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realizing the promise of reverse phase Protein Arrays for clinical translational and basic research a workshop report the rppa reverse phase Protein Array society
Molecular & Cellular Proteomics, 2014Co-Authors: Rehan Akbani, Ulrike Korf, Gordon B Mills, Leanne De Koning, Neil O Carragher, Karlfriedrich Becker, Theodore C Goldstein, Lance A Liotta, Satoshi Nishizuka, Michael PawlakAbstract:Reverse phase Protein Array (RPPA) technology introduced a miniaturized “antigen-down” or “dot-blot” immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a well-accepted reference standard exists) abundance of total Protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories. Advances in RPPA technology now offer scientists the opportunity to quantify Protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: preservation and optimization of pre-analytical sample quality, application of validated high-affinity and specific antibody (or other Protein affinity) detection reagents, dedicated informatics solutions to ensure accurate and robust quantification of Protein analytes, and quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments. In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.
Ulrike Korf - One of the best experts on this subject based on the ideXlab platform.
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integrative analysis of multi platform reverse phase Protein Array data for the pharmacodynamic assessment of response to targeted therapies
Scientific Reports, 2020Co-Authors: Adam Byron, Stephan Bernhardt, Berengere Ouine, Aurelie Cartier, Kenneth G Macleod, Neil O Carragher, Vonick Sibut, Ulrike KorfAbstract:Reverse-phase Protein Array (RPPA) technology uses panels of high-specificity antibodies to measure Proteins and Protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their Protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify Protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify Protein markers of disease or response to therapy.
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realizing the promise of reverse phase Protein Arrays for clinical translational and basic research a workshop report the rppa reverse phase Protein Array society
Molecular & Cellular Proteomics, 2014Co-Authors: Rehan Akbani, Ulrike Korf, Gordon B Mills, Leanne De Koning, Neil O Carragher, Karlfriedrich Becker, Theodore C Goldstein, Lance A Liotta, Satoshi Nishizuka, Michael PawlakAbstract:Reverse phase Protein Array (RPPA) technology introduced a miniaturized “antigen-down” or “dot-blot” immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a well-accepted reference standard exists) abundance of total Protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories. Advances in RPPA technology now offer scientists the opportunity to quantify Protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: preservation and optimization of pre-analytical sample quality, application of validated high-affinity and specific antibody (or other Protein affinity) detection reagents, dedicated informatics solutions to ensure accurate and robust quantification of Protein analytes, and quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments. In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.
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evaluation of reverse phase Protein Array rppa based pathway activation profiling in 84 non small cell lung cancer nsclc cell lines as platform for cancer proteomics and biomarker discovery
Biochimica et Biophysica Acta, 2014Co-Authors: Ramesh Ummanni, Heiko Mannsperger, Johanna Sonntag, Marcus Oswald, Ashwini Kumar Sharma, Rainer Konig, Ulrike KorfAbstract:The reverse phase Protein Array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant Proteins and phosphoProteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that Proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.
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rppanalyzer analysis of reverse phase Protein Array data
Bioinformatics, 2010Co-Authors: Heiko Mannsperger, Frauke Henjes, Stephan Gade, Tim Beissbarth, Ulrike KorfAbstract: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 e xible 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/. Contact: h.mannsperger@dkfz.de Supplementary information: Supplementary data are available at Bioinformatics online.
Stephen M Hewitt - One of the best experts on this subject based on the ideXlab platform.
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assessment of vascular endothelial growth factor in formalin fixed paraffin embedded colon cancer specimens by means of a well based reverse phase Protein Array
Proteome Science, 2014Co-Authors: Joonyong Chung, Till Braunschweig, Seungmo Hong, David S Kwon, Hyungjun Cho, Stephen M HewittAbstract:Background: Vascular endothelial growth factor (VEGF) is a critical pro-angiogenic factor, found in a number of cancers, and a target of therapy. It is typically assessed by immunohistochemistry (IHC) in clinical research. However, IHC is not a quantitative assay and is rarely reproducible. We compared VEGF levels in colon cancer by IHC and a quantitative immunoassay on Proteins isolated from formalin fixed, paraffin embedded tissues. Results: VEGF expression was studied by means of a well-based reverse phase Protein Array (RPPA) and immunohistochemistry in 69 colon cancer cases, and compared with various clinicopathologic factors. Protein lysates derived from formalin fixed, paraffin embedded tissue contained measurable immunoreactive VEGF molecules. The VEGF expression level of well differentiated colon cancer was significantly higher than those with moderately and poorly differentiated carcinomas by immunohistochemistry (P = 0.04) and well-based RPPA (P = 0.04). VEGF quantification by well-based RPPA also demonstrated an association with nodal metastasis status (P= 0.05). In addition, the normalized VEGF value by well-based RPPA correlated (r=0.283,P= 0.018). Furthermore, subgroup analysis by histologic type revealed that adenocarcinoma cases showed significant correlation (r= 0.315, P= 0.031) between well-based RPPA and IHC. Conclusions: The well-based RPPA method is a high throughput and sensitive approach, is an excellent tool for quantification of marker Proteins. Notably, this method may be helpful for more objective evaluation of Protein expression in cancer patients.
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a well based reverse phase Protein Array applicable to extracts from formalin fixed paraffin embedded tissue
Proteomics Clinical Applications, 2008Co-Authors: Joonyong Chung, Seojin Lee, Ylaya Kris, Till Braunschweig, June L Traicoff, Stephen M HewittAbstract:Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue offers significant diagnostic utility but is complicated due to the high level of covalently crosslinked Proteins arising from formalin fixation. To address these challenges, we developed a reliable Protein extraction method for FFPE tissue, based on heat-induced antigen retrieval within a pressure cooker. The Protein extraction yield from archival FFPE tissue section is approximately 90% of that recovered from frozen tissue. This method demonstrates preservation of immunoreactivity and recovery of full-length Proteins by Western blotting. Additionally, we developed a well-based RP Protein Array platform utilizing an electrochemiluminescence detection system. Protein samples derived from FFPE tissue by means of laser capture dissection, with as few as 500 shots demonstrate measurable signal differences for different Proteins. The lysates coated to the Array plate, remain stable over 1 month at room temperature. Theses data suggest that this new Protein-profiling platform coupled with the Protein extraction method can be used for molecular profiling analysis in FFPE tissue, and contribute to the validation and development of biomarkers in clinical studies.
Heng Zhu - One of the best experts on this subject based on the ideXlab platform.
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identification of serological biomarkers for early diagnosis of lung cancer using a Protein Array based approach
Molecular & Cellular Proteomics, 2017Co-Authors: Jianbo Pan, Heng Zhu, Guang Song, Dunyan Chen, Shuang Liu, Christian Rosa, Daniel Eichinger, Ignacio Pino, Jiang Qian, Yi HuangAbstract:Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a Protein Array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt Arrays, and identified 170 candidate Proteins significantly associated with LC. In Phase II, we constructed a LC focused Array with the 170 Proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.
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Protein Array based approaches for biomarker discovery in cancer
Genomics Proteomics & Bioinformatics, 2017Co-Authors: Yi Huang, Heng ZhuAbstract:Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, Protein Array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of Protein Arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.
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Protein Array identification of substrates of the epstein barr virus Protein kinase bglf4
Journal of Virology, 2009Co-Authors: Jian Zhu, Gangling Liao, Liang Shan, Jun Zhang, Meiru Chen, Gary S Hayward, Diane S Hayward, Prashant Desai, Heng ZhuAbstract:A conserved family of herpesvirus Protein kinases plays a crucial role in herpesvirus DNA replication and virion production. However, despite the fact that these kinases are potential therapeutic targets, no systematic studies have been performed to identify their substrates. We generated an Epstein-Barr virus (EBV) Protein Array to evaluate the targets of the EBV Protein kinase BGLF4. Multiple Proteins involved in EBV lytic DNA replication and virion assembly were identified as previously unrecognized substrates for BGLF4, illustrating the broad role played by this Protein kinase. Approximately half of the BGLF4 targets were also in vitro substrates for the cellular kinase CDK1/cyclin B. Unexpectedly, EBNA1 was identified as a substrate and binding partner of BGLF4. EBNA1 is essential for replication and maintenance of the episomal EBV genome during latency. BGLF4 did not prevent EBNA1 binding to sites in the EBV latency origin of replication, oriP. Rather, we found that BGLF4 was recruited by EBNA1 to oriP in cells transfected with an oriP vector and BGLF4 and in lytically induced EBV-positive Akata cells. In cells transfected with an oriP vector, the presence of BGLF4 led to more rapid loss of the episomal DNA, and this was dependent on BGLF4 kinase activity. Similarly, expression of doxycycline-inducible BGLF4 in Akata cells led to a reduction in episomal EBV genomes. We propose that BGLF4 contributes to effective EBV lytic cycle progression, not only through phosphorylation of EBV lytic DNA replication and virion Proteins, but also by interfering with the EBNA1 replication function.
Yi Huang - One of the best experts on this subject based on the ideXlab platform.
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identification of serological biomarkers for early diagnosis of lung cancer using a Protein Array based approach
Molecular & Cellular Proteomics, 2017Co-Authors: Jianbo Pan, Heng Zhu, Guang Song, Dunyan Chen, Shuang Liu, Christian Rosa, Daniel Eichinger, Ignacio Pino, Jiang Qian, Yi HuangAbstract:Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a Protein Array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt Arrays, and identified 170 candidate Proteins significantly associated with LC. In Phase II, we constructed a LC focused Array with the 170 Proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.
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Protein Array based approaches for biomarker discovery in cancer
Genomics Proteomics & Bioinformatics, 2017Co-Authors: Yi Huang, Heng ZhuAbstract:Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, Protein Array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of Protein Arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.
