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      MAP17 predicts sensitivity to platinum-based therapy, EGFR inhibitors and the proteasome inhibitor bortezomib in lung adenocarcinoma

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          Abstract

          Background

          The high incidence and mortality of lung tumours is a major health problem. Therefore, the identification both of biomarkers predicting efficacy for therapies in use and of novel efficacious therapeutic agents is crucial to increase patient survival. MAP17 (PDZK1IP1) is a small membrane-bound protein whose upregulation is reported as a common feature in tumours from diverse histological origins. Furthermore, MAP17 is correlated with tumour progression.

          Methods

          We assessed the expression of MAP17 in preclinical models, including cell lines and patient-derived xenografts (PDXs), assessing its correlation with sensitivity to different standard-of-care drugs in lung adenocarcinoma, as well as novel drugs. At the clinical level, we subsequently correlated MAP17 expression in human tumours with patient response to these therapies.

          Results

          We show that MAP17 expression is induced during lung tumourigenesis, particularly in lung adenocarcinomas, and provide in vitro and in vivo evidence that MAP17 levels predict sensitivity to therapies currently under clinical use in adenocarcinoma tumours, including cisplatin, carboplatin and EGFR inhibitors. In addition, we show that MAP17 expression predicts proteasome inhibitor efficacy in this context and that bortezomib, an FDA-approved drug, may be a novel therapeutic approach for MAP17-overexpressing lung adenocarcinomas.

          Conclusions

          Our results indicate a potential prognostic role for MAP17 in lung tumours, with particular relevance in lung adenocarcinomas, and highlight the predictive pot0065ntial of this membrane-associated protein for platinum-based therapy and EGFR inhibitor efficacy. Furthermore, we propose bortezomib treatment as a novel and efficacious therapy for lung adenocarcinomas exhibiting high MAP17 expression.

          Electronic supplementary material

          The online version of this article (10.1186/s13046-018-0871-7) contains supplementary material, which is available to authorized users.

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          Most cited references26

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          Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

          Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
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            SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

            Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.
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              The ubiquitin-proteasome pathway is required for processing the NF-kappa B1 precursor protein and the activation of NF-kappa B.

              We demonstrate an essential role for the proteasome complex in two proteolytic processes required for activation of the transcription factor NF-kappa B. The p105 precursor of the p50 subunit of NF-kappa B is processed in vitro by an ATP-dependent process that requires proteasomes and ubiquitin conjugation. The C-terminal region of p105 is rapidly degraded, leaving the N-terminal p50 domain. p105 processing can be blocked in intact cells with inhibitors of the proteasome or in yeast with proteasome mutants. These inhibitors also block the activation of NF-kappa B and the rapid degradation of I kappa B alpha induced by tumor necrosis factor alpha. Thus, the ubiquitin-proteasome pathway functions not only in the complete degradation of polypeptides, but also in the regulated processing of precursors into active proteins.
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                Author and article information

                Contributors
                iferrer@ext.cnio.es
                alvacot@hotmail.com
                s-molina-ibis@us.es
                jmgheredia@us.es
                marcoperez-ibis@us.es
                rsuarez@ext.cnio.es
                sponceaix@gmail.com
                lpazaresr@seom.org
                acarnero-ibis@us.es
                Journal
                J Exp Clin Cancer Res
                J. Exp. Clin. Cancer Res
                Journal of Experimental & Clinical Cancer Research : CR
                BioMed Central (London )
                0392-9078
                1756-9966
                17 August 2018
                17 August 2018
                2018
                : 37
                : 195
                Affiliations
                [1 ]ISNI 0000 0000 8700 1153, GRID grid.7719.8, H12O-CNIO Lung Cancer Clinical Research Unit, Institute i+12O and CNIO, ; Madrid, Spain
                [2 ]ISNI 0000 0000 9314 1427, GRID grid.413448.e, CIBER de Cáncer, ISCIII, ; Madrid, Spain
                [3 ]Instituto de Biomedicina de Sevilla (IBIS) (HUVR, CSIC, Hospital Universitario Virgen del Rocio, University of Seville, Avda. Manuel Siurot s/n, 41013), Seville, Spain
                [4 ]ISNI 0000 0001 2168 1229, GRID grid.9224.d, Department of Vegetal Biochemistry and Molecular Biology, , University of Seville, ; Seville, Spain
                [5 ]ISNI 0000 0001 1945 5329, GRID grid.144756.5, Medical Oncology Department, , Hospital Universitario Doce de Octubre, ; Madrid, Spain
                [6 ]ISNI 0000 0001 2157 7667, GRID grid.4795.f, University Complutense of Madrid, ; Madrid, Spain
                [7 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Present address: Program in Molecular Pharmacology, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                Article
                871
                10.1186/s13046-018-0871-7
                6098621
                30119639
                4fbfb018-713e-4115-9e4a-d4d0477b222d
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 May 2018
                : 3 August 2018
                Funding
                Funded by: Spanish Ministry of Economy and Competitivity
                Award ID: Fis: PI15/00045
                Funded by: CIBER de Cáncerco-funded by FEDER from Regional Development European Funds
                Award ID: CB16/12/00275)
                Funded by: FundRef http://dx.doi.org/10.13039/501100002878, Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía;
                Award ID: CTS-1848
                Funded by: and Consejeria de Salud of the Junta de Andalucia
                Award ID: PI-0096-2014
                Funded by: AECC Foundation
                Funded by: ISCIII
                Award ID: PI1401964, PI17/00778, DTS1700089 and PIE15/00076
                Award Recipient :
                Funded by: CIBERONC , co-funded by FEDER from Regional Development European Funds
                Award ID: (CD16/12/00442)
                Award Recipient :
                Funded by: RTICC
                Award ID: (R12/0036/0028, PI10/00033)
                Award Recipient :
                Funded by: Consejería de Salud y Bienestar Social
                Award ID: (PI-0046-2012)
                Award Recipient :
                Funded by: Fundación Mutua Madrileña (ES)
                Funded by: Instituto de Salud Carlos III and co-funded by the European Union (ERDF/ESF, “Investing in yourfuture”).
                Award ID: (PI16/01311)
                Award Recipient :
                Funded by: ISCIII .
                Award ID: (FI12/00429)
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Oncology & Radiotherapy
                biomarkers,lung cancer,pdzk1ip1,treatment efficacy
                Oncology & Radiotherapy
                biomarkers, lung cancer, pdzk1ip1, treatment efficacy

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