The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Emanuel F Petricoin - One of the best experts on this subject based on the ideXlab platform.
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a pilot study exploring the molecular architecture of the tumor microenvironment in human prostate cancer using laser capture microdissection and reverse phase Protein Microarray
Molecular Oncology, 2016Co-Authors: Elisa Pin, Lance A Liotta, Elisa Baldelli, Alex K Hodge, Steven P Stratton, Claudio Belluco, Ray B Nagle, Jianghong Deng, Ting Dong, Emanuel F PetricoinAbstract:The cross-talk between tumor epithelium and surrounding stromal/immune microenvironment is essential to sustain tumor growth and progression and provides new opportunities for the development of targeted treatments focused on disrupting the tumor ecology. Identification of novel approaches to study these interactions is of primary importance. Using laser capture microdissection (LCM) coupled with reverse phase Protein Microarray (RPPA) based Protein signaling activation mapping we explored the molecular interconnection between tumor epithelium and surrounding stromal microenvironment in 18 prostate cancer (PCa) specimens. Four specimen-matched cellular compartments (normal-appearing epithelium and its adjacent stroma, and malignant epithelium and its adjacent stroma) were isolated for each case. The signaling network analysis of the four compartments unraveled a number of molecular mechanisms underlying the communication between tumor cells and stroma in the context of the tumor microenvironment. In particular, differential expression of inflammatory mediators like IL-8 and IL-10 by the stroma cells appeared to modulate specific cross-talks between the tumor cells and surrounding microenvironment.
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abstract b1 14 a machine learning approach applied to reverse phase Protein Microarray data for pathways activation mapping of kras wild type and mutated adenocarcinomas of the lung
Cancer Research, 2015Co-Authors: Fortunato Bianconi, Emanuel F Petricoin, Elisa Baldelli, Eric B Haura, Lucio Crino, Federico Patiti, Paolo Valigi, Vienna Ludovini, Mariaelena PierobonAbstract:Background: KRAS proto-oncogene is one of the most commonly mutated genes in Non-Small Cell Lung Cancer (NSCLC) with greater frequency in the adenocarcinoma (AD) histotype. Patients with mutant KRAS often do not benefit from standard therapy and effective targeted therapy are still not available for these patients. Little is known about which pathways are activated in KRAS mutant ADs. Understanding which interactions drive KRAS mutant lesions can lead to the identification of novel targets for treating more effectively patients harboring a KRAS mutation. The aim of this study was to apply machine learning techniques to Reverse Phase Protein Microarray (RPPA) data to select Proteins whose activity can better describe the KRAS status (wild type or mutated). Materials and methods: A total of 58 samples (24 KRAS wild type and 34 KRAS mutated) were collected from surgically treated AD patients of the lung at the H. Lee Moffitt Cancer Center & Research Institute (Tampa, FL) and at the S. Maria della Misericordia Hospital (Perugia, Italy). Tumor cells were isolated with laser capture microdissection and RPPA was performed to quantitatively measure the expression/activation levels of 155 Proteins. Recursive Feature Elimination with Support Vector Machine (RFE-SVM) was used to rank Proteins according to the absolute value of their weight in the hyperplane defined by the SVM to separate the 2 groups (KRAS wild type or mutated). LSimpute algorithm was used to impute missing data due to depletion of biological sample. Stability and robustness of the results were achieved using the RFE-SVM algorithm within an ensemble feature selection framework. Results: The LSimpute algorithm was applied to impute missing data in 11 patients that presented a number of missing Proteins between 1% and 48% in the single record and of 5% of the overall dataset. The tested algorithm accuracy was 0.90. The RFE-SVM algorithm was then applied to the entire dataset (58 samples). The analysis of the RPPA data revealed that the activation of many signaling Proteins involved in the ERK pathway is also discriminative relatively to KRAS WT/MUT. Among the Proteins with higher rank were found p70S6K, ERK1/2T202/Y204, EGFR, PP2A and Akt S473. Stability and robustness of the output of the algorithm was confirmed in the completed dataset RFE-SVM algorithm. Conclusion: The proposed methodology is the first example of computational approach based on machine learning algorithms applied to the analysis of proteomic data in cancer translational research. The output of the procedure is a ranking of Proteins that could play potential key roles in the signal pathways of patients harboring KRAS mutations when compared to KRAS wild type patients. Results obtained from this study could make important contributions to the identification of Proteins that can be targeted to develop more effective treatments for AD patients with KRAS mutations. Furthermore this methodology can overcome the issue of missing values in RPPA datasets generating a stable and robust complete output. Citation Format: Fortunato Bianconi, Elisa Baldelli, Federico Patiti, Paolo Valigi, Eric B. Haura, Lucio Crino, Vienna Ludovini, Emanuel Petricoin, Mariaelena Pierobon. A machine learning approach applied to Reverse Phase Protein Microarray data for pathways activation mapping of KRAS wild type and mutated adenocarcinomas of the lung. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-14.
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the needle in the haystack application of breast fine needle aspirate samples to quantitative Protein Microarray technology
Cancer, 2007Co-Authors: Amy V Rapkiewicz, Virginia Espina, Julia Wulfkuhle, Emanuel F Petricoin, Lance A Liotta, Jo Anne Zujewski, Peter F Lebowitz, Armando C Filie, Kevin Camphausen, Andrea AbatiAbstract:BACKGROUND. There is an unmet clinical need for economic, minimally invasive procedures that use a limited number of cells for the molecular profiling of tumors in individual patients. Reverse-phase Protein Microarray (RPPM) technology has been applied successfully to the quantitative analysis of breast, ovarian, prostate, and colorectal cancers using frozen surgical specimens. METHODS. For this report, the authors investigated the novel use of RPPM technology for the analysis of both archival cytology aspirate smears and frozen fine-needle aspiration (FNA) samples. RPPMs were printed with 63 breast FNA samples that were obtained before, during, and after treatment from 21 patients who were enrolled in a Phase II trial of neoadjuvant capecitabine and docetaxel therapy for breast cancer. RESULTS. Based on an MCF7 cell line model of breast adenocarcinoma, the sensitivity of the RPPM detection method was in the femtomolar range with a coefficient of variance <13.5% for the most dilute sample. Assay linearity was noted from 1.0 μg/μL to 7.8 ng/μL total Protein/array spot (R2 = 0.9887) for a membrane receptor Protein (epidermal growth factor receptor; R2 = 0.9935). CONCLUSIONS. The results from this study indicated that low-abundance analytes and phosphorylated and nonphosphorylated Proteins in specimens that consist of a few thousand cells obtained through FNA can be quantified with RPPM technology. The ability to monitor the in vivo state of cell-signaling Proteins before and after treatment potentially will augment the ability to design individualized therapy regimens through the mapping of aberrant cell-signaling phenotypes. The mapping of these Protein pathways will further the development of rational drug targets. Cancer (Cancer Cytopathol) 2007. Published 2007 by the American Cancer Society.
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Protein Microarray detection strategies focus on direct detection technologies
Journal of Immunological Methods, 2004Co-Authors: Virginia Espina, Elisa C Woodhouse, Julia Wulfkuhle, Heather D Asmussen, Emanuel F Petricoin, Lance A LiottaAbstract:Protein Microarrays are being utilized for functional proteomic analysis, providing information not obtainable by gene arrays. Microarray technology is applicable for studying Protein-Protein, Protein-ligand, kinase activity and posttranslational modifications of Proteins. A precise and sensitive Protein Microarray, the direct detection or reverse-phase Microarray, has been applied to ongoing clinical trials at the National Cancer Institute for studying phosphorylation events in EGF-receptor-mediated cell signaling pathways. The variety of Microarray applications allows for multiple, creative Microarray designs and detection strategies. Herein, we discuss detection strategies and challenges for Protein Microarray technology, focusing on direct detection of Protein Microarrays.
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similarities of prosurvival signals in bcl 2 positive and bcl 2 negative follicular lymphomas identified by reverse phase Protein Microarray
Laboratory Investigation, 2004Co-Authors: Mark Raffeld, Emanuel F Petricoin, Lance A Liotta, Lu Charboneau, Stefania Pittaluga, Larry W Kwak, Elaine S JaffeAbstract:Similarities of prosurvival signals in Bcl-2-positive and Bcl-2-negative follicular lymphomas identified by reverse phase Protein Microarray
Ruifu Yang - One of the best experts on this subject based on the ideXlab platform.
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antibody profiling in plague patients by Protein Microarray
Microbes and Infection, 2008Co-Authors: Bei Li, Dongsheng Zhou, Zuyun Wang, Zhizhong Song, Hu Wang, Min Li, Xingqi Dong, Mingshou Wu, Ruifu YangAbstract:A Protein Microarray containing 144 known or putative virulence-related Proteins of Yersinia pestis was used to evaluate the antibody responses of plague patients. Forty-two Proteins were found to be expressed in vivo and antibodies against 14 of them were detected in all patients analyzed, providing potential candidates for novel protective antigens and novel serodiagnostic markers in Y. pestis. Moreover, the lack of antibody to LcrV in the five patients in Focus F might be a challenge to our understanding of the pathogenesis of Y. pestis.
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quorum sensing affects virulence associated Proteins f1 lcrv katy and ph6 etc of yersinia pestis as revealed by Protein Microarray based antibody profiling
Microbes and Infection, 2006Co-Authors: Zeliang Chen, Dongsheng Zhou, Zhaobiao Guo, Yajun Song, Jianshan Zhang, Long Qin, Yanping Han, Ruifu YangAbstract:Protein Microarray that consists of virulence-associated Proteins of Yersinia pestis is used to compare antibody profiles elicited by the wild-type and quorum sensing (QS) mutant strain of this bacterium to define the immunogens that are impacted by QS. The results will lead the way for future functional proteomics studies. The antibody profile that was induced by the QS mutant differed from that of the parent strain. Detailed comparison of the antibody profiles, according to the Proteins' functional annotations, showed that QS affects the expression of many virulence-associated Proteins of Y. pestis. The antibodies to many virulence-associated Proteins were not detected or lower titers of antibodies to many Proteins were detected in the sera of rabbits immunized with the QS mutant, relative to those of the wild type, which indicated that these Proteins were not expressed or expressed at relatively lower levels in the QS mutant. The results demonstrated that antibody profiling by Protein Microarrays is a promising high-throughput method for revealing the interactions between pathogens and the host immune system.
Philip L Felgner - One of the best experts on this subject based on the ideXlab platform.
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antibody profiling in naive and semi immune individuals experimentally challenged with plasmodium vivax sporozoites
PLOS Neglected Tropical Diseases, 2016Co-Authors: Myriam Arevaloherrera, Philip L Felgner, Aarti Jain, Mary Lopezperez, Emmanuel Y Dotsey, Kelly Rubiano, Huw D Davies, Socrates HerreraAbstract:Background Acquisition of malaria immunity in low transmission areas usually occurs after relatively few exposures to the parasite. A recent Plasmodium vivax experimental challenge trial in malaria naive and semi-immune volunteers from Colombia showed that all naive individuals developed malaria symptoms, whereas semi-immune subjects were asymptomatic or displayed attenuated symptoms. Sera from these individuals were analyzed by Protein Microarray to identify antibodies associated with clinical protection. Methodology/Principal Findings Serum samples from naive (n = 7) and semi-immune (n = 9) volunteers exposed to P. vivax sporozoite-infected mosquito bites were probed against a custom Protein Microarray displaying 515 P. vivax antigens. The array revealed higher serological responses in semi-immune individuals before the challenge, although malaria naive individuals also had pre-existing antibodies, which were higher in Colombians than US adults (control group). In both experimental groups the response to the P. vivax challenge peaked at day 45 and returned to near baseline at day 145. Additional analysis indicated that semi-immune volunteers without fever displayed a lower response to the challenge, but recognized new antigens afterwards. Conclusion Clinical protection against experimental challenge in volunteers with previous P. vivax exposure was associated with elevated pre-existing antibodies, an attenuated serological response to the challenge and reactivity to new antigens.
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profiling the humoral immune response of acute and chronic q fever by Protein Microarray
Molecular & Cellular Proteomics, 2011Co-Authors: Adam Vigil, Jozelyn Pablo, Rie Nakajimasasaki, Chen Chen, Aarti Jain, Algimantas Jasinskas, Laura R Hendrix, James E Samuel, Philip L FelgnerAbstract:Antigen profiling using comprehensive Protein Microarrays is a powerful tool for characterizing the humoral immune response to infectious pathogens. Coxiella burnetii is a CDC category B bioterrorist infectious agent with worldwide distribution. In order to assess the antibody repertoire of acute and chronic Q fever patients we have constructed a Protein Microarray containing 93% of the proteome of Coxiella burnetii, the causative agent of Q fever. Here we report the profile of the IgG and IgM seroreactivity in 25 acute Q fever patients in longitudinal samples. We found that both early and late time points of infection have a very consistent repertoire of IgM and IgG response, with a limited number of Proteins undergoing increasing or decreasing seroreactivity. We also probed a large collection of acute and chronic Q fever patient samples and identified serological markers that can differentiate between the two disease states. In this comparative analysis we confirmed the identity of numerous IgG biomarkers of acute infection, identified novel IgG biomarkers for acute and chronic infections, and profiled for the first time the IgM antibody repertoire for both acute and chronic Q fever. Using these results we were able to devise a test that can distinguish acute from chronic Q fever. These results also provide a unique perspective on isotype switch and demonstrate the utility of Protein Microarrays for simultaneously examining the dynamic humoral immune response against thousands of Proteins from a large number of patients. The results presented here identify novel seroreactive antigens for the development of recombinant Protein-based diagnostics and subunit vaccines, and provide insight into the development of the antibody response.
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high throughput prediction of Protein antigenicity using Protein Microarray data
Bioinformatics, 2010Co-Authors: Christophe N Magnan, Michael Zeller, Matthew A Kayala, Adam Vigil, Arlo Randall, Philip L Felgner, Pierre BaldiAbstract:Motivation: Discovery of novel protective antigens is fundamental to the development of vaccines for existing and emerging pathogens. Most computational methods for predicting Protein antigenicity rely directly on homology with previously characterized protective antigens; however, homology-based methods will fail to discover truly novel protective antigens. Thus, there is a significant need for homology-free methods capable of screening entire proteomes for the antigens most likely to generate a protective humoral immune response. Results: Here we begin by curating two types of positive data: (i) antigens that elicit a strong antibody response in protected individuals but not in unprotected individuals, using human immunoglobulin reactivity data obtained from Protein Microarray analyses; and (ii) known protective antigens from the literature. The resulting datasets are used to train a sequence-based prediction model, ANTIGENpro, to predict the likelihood that a Protein is a protective antigen. ANTIGENpro correctly classifies 82% of the known protective antigens when trained using only the Protein Microarray datasets. The accuracy on the combined dataset is estimated at 76% by cross-validation experiments. Finally, ANTIGENpro performs well when evaluated on an external pathogen proteome for which Protein Microarray data were obtained after the initial development of ANTIGENpro. Availability: ANTIGENpro is integrated in the SCRATCH suite of predictors available at http://scratch.proteomics.ics.uci.edu. Contact: pfbaldi@ics.uci.edu
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identification of the feline humoral immune response to bartonella henselae infection by Protein Microarray
PLOS ONE, 2010Co-Authors: Adam Vigil, Rie Nakajimasasaki, Aarti Jain, Rocio Ortega, Xiaolin Tan, Bruno B Chomel, Rickie W Kasten, Jane E Koehler, Philip L FelgnerAbstract:Background: Bartonella henselae is the zoonotic agent of cat scratch disease and causes potentially fatal infections in immunocompromised patients. Understanding the complex interactions between the host’s immune system and bacterial pathogens is central to the field of infectious diseases and to the development of effective diagnostics and vaccines. Methodology: We report the development of a Microarray comprised of Proteins expressed from 96% (1433/1493) of the predicted ORFs encoded by the genome of the zoonotic pathogen Bartonella henselae. The array was probed with a collection of 62 uninfected, 62 infected, and 8 ‘‘specific-pathogen free’’ nao ¨ve cat sera, to profile the antibody repertoire elicited during natural Bartonella henselae infection. Conclusions: We found that 7.3% of the B. henselae Proteins on the Microarray were seroreactive and that seroreactivity was not evenly distributed between predicted Protein function or subcellular localization. Membrane Proteins were significantly most likely to be seroreactive, although only 23% of the membrane Proteins were reactive. Conversely, we found that Proteins involved in amino acid transport and metabolism were significantly underrepresented and did not contain any seroreactive antigens. Of all seroreactive antigens, 52 were differentially reactive with sera from infected cats, and 53 were equally reactive with sera from infected and uninfected cats. Thirteen of the seroreactive antigens were found to be differentially seroreactive between B. henselae type I and type II. Based on these results, we developed a classifier algorithm that was capable of accurately discerning 93% of the infected animals using the Microarray platform. The seroreactivity and diagnostic potential of these antigens was then validated on an immunostrip platform, which correctly identified 98% of the infected cats. Our Protein Microarray platform provides a high-throughput, comprehensive analysis of the feline humoral immune response to natural infection with the alpha-proteobacterium B. henselae at an antigen-specific, sera-specific, and genome-wide level. Furthermore, these results provide novel insight and utility in diagnostics, vaccine development, and understanding of host-pathogen interaction.
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serological profiling of a candida albicans Protein Microarray reveals permanent host pathogen interplay and stage specific responses during candidemia
PLOS Pathogens, 2010Co-Authors: Brian A Mochon, Matthew A Kayala, Philip L Felgner, Pierre Baldi, John R Wingard, Cornelius J Clancy, Hong M Nguyen, Haoping LiuAbstract:Candida albicans in the immunocompetent host is a benign member of the human microbiota. Though, when host physiology is disrupted, this commensal-host interaction can degenerate and lead to an opportunistic infection. Relatively little is known regarding the dynamics of C. albicans colonization and pathogenesis. We developed a C. albicans cell surface Protein Microarray to profile the immunoglobulin G response during commensal colonization and candidemia. The antibody response from the sera of patients with candidemia and our negative control groups indicate that the immunocompetent host exists in permanent host-pathogen interplay with commensal C. albicans. This report also identifies cell surface antigens that are specific to different phases (i.e. acute, early and mid convalescence) of candidemia. We identified a set of thirteen cell surface antigens capable of distinguishing acute candidemia from healthy individuals and uninfected hospital patients with commensal colonization. Interestingly, a large proportion of these cell surface antigens are involved in either oxidative stress or drug resistance. In addition, we identified 33 antigenic Proteins that are enriched in convalescent sera of the candidemia patients. Intriguingly, we found within this subset an increase in antigens associated with heme-associated iron acquisition. These findings have important implications for the mechanisms of C. albicans colonization as well as the development of systemic infection.
Lance A Liotta - One of the best experts on this subject based on the ideXlab platform.
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a pilot study exploring the molecular architecture of the tumor microenvironment in human prostate cancer using laser capture microdissection and reverse phase Protein Microarray
Molecular Oncology, 2016Co-Authors: Elisa Pin, Lance A Liotta, Elisa Baldelli, Alex K Hodge, Steven P Stratton, Claudio Belluco, Ray B Nagle, Jianghong Deng, Ting Dong, Emanuel F PetricoinAbstract:The cross-talk between tumor epithelium and surrounding stromal/immune microenvironment is essential to sustain tumor growth and progression and provides new opportunities for the development of targeted treatments focused on disrupting the tumor ecology. Identification of novel approaches to study these interactions is of primary importance. Using laser capture microdissection (LCM) coupled with reverse phase Protein Microarray (RPPA) based Protein signaling activation mapping we explored the molecular interconnection between tumor epithelium and surrounding stromal microenvironment in 18 prostate cancer (PCa) specimens. Four specimen-matched cellular compartments (normal-appearing epithelium and its adjacent stroma, and malignant epithelium and its adjacent stroma) were isolated for each case. The signaling network analysis of the four compartments unraveled a number of molecular mechanisms underlying the communication between tumor cells and stroma in the context of the tumor microenvironment. In particular, differential expression of inflammatory mediators like IL-8 and IL-10 by the stroma cells appeared to modulate specific cross-talks between the tumor cells and surrounding microenvironment.
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phosphoProtein stability in clinical tissue and its relevance for reverse phase Protein Microarray technology
Methods of Molecular Biology, 2011Co-Authors: Virginia Espina, Claudius Mueller, Lance A LiottaAbstract:Phosphorylated Proteins reflect the activity of specific cell signaling nodes in biological kinase Protein networks. Cell signaling pathways can be either activated or deactivated depending on the phosphorylation state of the constituent Proteins. The state of these kinase pathways reflects the in vivo activity of the cells and tissue at any given point in time. As such, cell signaling pathway information can be extrapolated to infer which phosphorylated Proteins/pathways are driving an individual tumor’s growth. Reverse Phase Protein Microarrays (RPMA) are a sensitive and precise platform that can be applied to the quantitative measurement of hundreds of phosphorylated signal Proteins from a small sample of tissue. Pre-analytical variability originating from tissue procurement and preservation may cause significant variability and bias in downstream molecular analysis. Depending on the ex vivo delay time in tissue processing, and the manner of tissue handling, Protein biomarkers such as signal pathway phosphoProteins will be elevated or suppressed in a manner that does not represent the biomarker levels at the time of excision. Consequently, assessment of the state of these kinase networks requires stabilization, or preservation, of the phosphoProteins immediately post tissue procurement. We have employed reverse phase Protein Microarray analysis of phosphoProteins to study the factors influencing stability of phosphoProteins in tissue following procurement. Based on this analysis we have established tissue procurement guidelines for clinical research with an emphasis on quantifying phosphoProteins by RPMA.
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the needle in the haystack application of breast fine needle aspirate samples to quantitative Protein Microarray technology
Cancer, 2007Co-Authors: Amy V Rapkiewicz, Virginia Espina, Julia Wulfkuhle, Emanuel F Petricoin, Lance A Liotta, Jo Anne Zujewski, Peter F Lebowitz, Armando C Filie, Kevin Camphausen, Andrea AbatiAbstract:BACKGROUND. There is an unmet clinical need for economic, minimally invasive procedures that use a limited number of cells for the molecular profiling of tumors in individual patients. Reverse-phase Protein Microarray (RPPM) technology has been applied successfully to the quantitative analysis of breast, ovarian, prostate, and colorectal cancers using frozen surgical specimens. METHODS. For this report, the authors investigated the novel use of RPPM technology for the analysis of both archival cytology aspirate smears and frozen fine-needle aspiration (FNA) samples. RPPMs were printed with 63 breast FNA samples that were obtained before, during, and after treatment from 21 patients who were enrolled in a Phase II trial of neoadjuvant capecitabine and docetaxel therapy for breast cancer. RESULTS. Based on an MCF7 cell line model of breast adenocarcinoma, the sensitivity of the RPPM detection method was in the femtomolar range with a coefficient of variance <13.5% for the most dilute sample. Assay linearity was noted from 1.0 μg/μL to 7.8 ng/μL total Protein/array spot (R2 = 0.9887) for a membrane receptor Protein (epidermal growth factor receptor; R2 = 0.9935). CONCLUSIONS. The results from this study indicated that low-abundance analytes and phosphorylated and nonphosphorylated Proteins in specimens that consist of a few thousand cells obtained through FNA can be quantified with RPPM technology. The ability to monitor the in vivo state of cell-signaling Proteins before and after treatment potentially will augment the ability to design individualized therapy regimens through the mapping of aberrant cell-signaling phenotypes. The mapping of these Protein pathways will further the development of rational drug targets. Cancer (Cancer Cytopathol) 2007. Published 2007 by the American Cancer Society.
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Protein Microarray detection strategies focus on direct detection technologies
Journal of Immunological Methods, 2004Co-Authors: Virginia Espina, Elisa C Woodhouse, Julia Wulfkuhle, Heather D Asmussen, Emanuel F Petricoin, Lance A LiottaAbstract:Protein Microarrays are being utilized for functional proteomic analysis, providing information not obtainable by gene arrays. Microarray technology is applicable for studying Protein-Protein, Protein-ligand, kinase activity and posttranslational modifications of Proteins. A precise and sensitive Protein Microarray, the direct detection or reverse-phase Microarray, has been applied to ongoing clinical trials at the National Cancer Institute for studying phosphorylation events in EGF-receptor-mediated cell signaling pathways. The variety of Microarray applications allows for multiple, creative Microarray designs and detection strategies. Herein, we discuss detection strategies and challenges for Protein Microarray technology, focusing on direct detection of Protein Microarrays.
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similarities of prosurvival signals in bcl 2 positive and bcl 2 negative follicular lymphomas identified by reverse phase Protein Microarray
Laboratory Investigation, 2004Co-Authors: Mark Raffeld, Emanuel F Petricoin, Lance A Liotta, Lu Charboneau, Stefania Pittaluga, Larry W Kwak, Elaine S JaffeAbstract:Similarities of prosurvival signals in Bcl-2-positive and Bcl-2-negative follicular lymphomas identified by reverse phase Protein Microarray
Elisa Baldelli - One of the best experts on this subject based on the ideXlab platform.
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abstract 5656 quantitative measurement of pdl1 expression across tumor types using laser capture microdissection and reverse phase Protein Microarray
Cancer Research, 2017Co-Authors: Elisa Baldelli, Valerie S Calvert, Alex K Hodge, Maria Isabella Sereni, Guido Gambara, Eric B Haura, Lucio Crino, Bryant Dunetz, Sergio Pecorelli, David J PerryAbstract:Background: The efficacy of immunotherapy, including therapeutic strategies capable of modulating innate/adaptive immune resistance, varies greatly across tumor types. As the number of available immunotherapies accelerates, the study of predictive markers by IHC (e.g. PDL1 expression) is under intense investigation. However, staining protocol inconsistency, variation across scoring systems, and subjective interpretation of the immunostaining have produced conflicting results thus far. This work explored the role of Laser Capture Microdissection (LCM) coupled with Reverse Phase Protein Microarray (RPPA) as an alternative high throughput, quantitative, operator independent platform for measuring PDL1 expression across tumor types. Material and Methods: Pure epithelial cells were isolated via LCM from 178 samples including: 72 ovarian cancers (OC), 57 lung adenocarcinomas (LC), 30 metastatic breast cancers (MBC), and 19 pancreatic cancers (PC). PDL1 expression was measured on a continuous scale using quantitative RPPA based analysis. Each tumor type was processed and arrayed independently. Experimental samples and reference standards used for inter-assay normalization were printed in triplicates. Results: PDL1 expression varied greatly across tumor types. LC were characterized by the greatest intra-tumor fold dynamic range (> 35-fold), followed by OC ( Conclusions: The LCM-RPPA workflow has the unique ability to capture immune checkpoint expression on a continuous quantitative scale as well as capture its broad dynamic range. Because RPPA is unconstrained by antigen retrieval issues as well as subjectivity of IHC interpretation, this approach may generate more accurate cut-point of therapeutic response prediction. Overall the dynamic range of PDL1 was broader in LC compared to other solid tumors, and LC had a much higher proportion of patients with tumors expressing high levels of PDL1. These quantitative differences may explain therapeutic efficacy of PDL1 inhibition across tumor types. Such speculative hypothesis should be further validated in prospective clinical trials. Finally, these preliminary data suggest that organ specific microenvironments more than specific driving mutations (e.g. KRAS) may strongly influence PDL1 expression in malignant lesions. Citation Format: Elisa Baldelli, Valerie Calvert, K. Alex Hodge, Maria Isabella Sereni, Guido Gambara, Eric B. Haura, Lucio Crino9, Bryant Dunetz, Sergio Pecorelli, David J. Perry, Stephen P. Anthony, Nicholas Robert, Donald W. Northfelt, Mohammad Jahanzeb, Emanuel F. Petricoin, Mariaelena Pierobon. Quantitative measurement of PDL1 expression across tumor types using laser capture microdissection and reverse phase Protein Microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5656. doi:10.1158/1538-7445.AM2017-5656
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a pilot study exploring the molecular architecture of the tumor microenvironment in human prostate cancer using laser capture microdissection and reverse phase Protein Microarray
Molecular Oncology, 2016Co-Authors: Elisa Pin, Lance A Liotta, Elisa Baldelli, Alex K Hodge, Steven P Stratton, Claudio Belluco, Ray B Nagle, Jianghong Deng, Ting Dong, Emanuel F PetricoinAbstract:The cross-talk between tumor epithelium and surrounding stromal/immune microenvironment is essential to sustain tumor growth and progression and provides new opportunities for the development of targeted treatments focused on disrupting the tumor ecology. Identification of novel approaches to study these interactions is of primary importance. Using laser capture microdissection (LCM) coupled with reverse phase Protein Microarray (RPPA) based Protein signaling activation mapping we explored the molecular interconnection between tumor epithelium and surrounding stromal microenvironment in 18 prostate cancer (PCa) specimens. Four specimen-matched cellular compartments (normal-appearing epithelium and its adjacent stroma, and malignant epithelium and its adjacent stroma) were isolated for each case. The signaling network analysis of the four compartments unraveled a number of molecular mechanisms underlying the communication between tumor cells and stroma in the context of the tumor microenvironment. In particular, differential expression of inflammatory mediators like IL-8 and IL-10 by the stroma cells appeared to modulate specific cross-talks between the tumor cells and surrounding microenvironment.
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abstract b1 14 a machine learning approach applied to reverse phase Protein Microarray data for pathways activation mapping of kras wild type and mutated adenocarcinomas of the lung
Cancer Research, 2015Co-Authors: Fortunato Bianconi, Emanuel F Petricoin, Elisa Baldelli, Eric B Haura, Lucio Crino, Federico Patiti, Paolo Valigi, Vienna Ludovini, Mariaelena PierobonAbstract:Background: KRAS proto-oncogene is one of the most commonly mutated genes in Non-Small Cell Lung Cancer (NSCLC) with greater frequency in the adenocarcinoma (AD) histotype. Patients with mutant KRAS often do not benefit from standard therapy and effective targeted therapy are still not available for these patients. Little is known about which pathways are activated in KRAS mutant ADs. Understanding which interactions drive KRAS mutant lesions can lead to the identification of novel targets for treating more effectively patients harboring a KRAS mutation. The aim of this study was to apply machine learning techniques to Reverse Phase Protein Microarray (RPPA) data to select Proteins whose activity can better describe the KRAS status (wild type or mutated). Materials and methods: A total of 58 samples (24 KRAS wild type and 34 KRAS mutated) were collected from surgically treated AD patients of the lung at the H. Lee Moffitt Cancer Center & Research Institute (Tampa, FL) and at the S. Maria della Misericordia Hospital (Perugia, Italy). Tumor cells were isolated with laser capture microdissection and RPPA was performed to quantitatively measure the expression/activation levels of 155 Proteins. Recursive Feature Elimination with Support Vector Machine (RFE-SVM) was used to rank Proteins according to the absolute value of their weight in the hyperplane defined by the SVM to separate the 2 groups (KRAS wild type or mutated). LSimpute algorithm was used to impute missing data due to depletion of biological sample. Stability and robustness of the results were achieved using the RFE-SVM algorithm within an ensemble feature selection framework. Results: The LSimpute algorithm was applied to impute missing data in 11 patients that presented a number of missing Proteins between 1% and 48% in the single record and of 5% of the overall dataset. The tested algorithm accuracy was 0.90. The RFE-SVM algorithm was then applied to the entire dataset (58 samples). The analysis of the RPPA data revealed that the activation of many signaling Proteins involved in the ERK pathway is also discriminative relatively to KRAS WT/MUT. Among the Proteins with higher rank were found p70S6K, ERK1/2T202/Y204, EGFR, PP2A and Akt S473. Stability and robustness of the output of the algorithm was confirmed in the completed dataset RFE-SVM algorithm. Conclusion: The proposed methodology is the first example of computational approach based on machine learning algorithms applied to the analysis of proteomic data in cancer translational research. The output of the procedure is a ranking of Proteins that could play potential key roles in the signal pathways of patients harboring KRAS mutations when compared to KRAS wild type patients. Results obtained from this study could make important contributions to the identification of Proteins that can be targeted to develop more effective treatments for AD patients with KRAS mutations. Furthermore this methodology can overcome the issue of missing values in RPPA datasets generating a stable and robust complete output. Citation Format: Fortunato Bianconi, Elisa Baldelli, Federico Patiti, Paolo Valigi, Eric B. Haura, Lucio Crino, Vienna Ludovini, Emanuel Petricoin, Mariaelena Pierobon. A machine learning approach applied to Reverse Phase Protein Microarray data for pathways activation mapping of KRAS wild type and mutated adenocarcinomas of the lung. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-14.
