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      Activation of the EGFR/PI3K/AKT pathway limits the efficacy of trametinib treatment in head and neck cancer

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          Abstract

          Blocking the mitogen‐activated protein kinase (MAPK) pathway with the MEK1/2 inhibitor trametinib has produced promising results in patients with head and neck squamous cell carcinoma (HNSCC). In the current study, we showed that trametinib treatment leads to overexpression and activation of the epidermal growth factor receptor (EGFR) in HNSCC cell lines and patient‐derived xenografts. Knockdown of EGFR improved trametinib treatment efficacy both in vitro and in vivo. Mechanistically, we demonstrated that trametinib‐induced EGFR overexpression hyperactivates the phosphatidylinositol 3‐kinase (PI3K)/AKT pathway. In vitro, blocking the PI3K pathway with GDC‐0941 (pictilisib), or BYL719 (alpelisib), prevented AKT pathway hyperactivation and enhanced the efficacy of trametinib in a synergistic manner. In vivo, a combination of trametinib and BYL719 showed superior antitumor efficacy vs. the single agents, leading to tumor growth arrest. We confirmed our findings in a syngeneic murine head and neck cancer cell line in vitro and in vivo. Taken together, our findings show that trametinib treatment induces hyperactivation of EGFR/PI3K/AKT; thus, blocking of the EGFR/PI3K pathway is required to improve trametinib efficacy in HNSCC.

          Abstract

          MEK inhibition by trametinib leads to rapid hyperactivation and upregulation of YAP1. YAP1 overexpression and localization in the nucleus are associated with the upregulation of epidermal growth factor receptor (EGFR) transcription. Overactivation of EGFR induces phosphatidylinositol‐4,5‐bisphosphate 3‐kinase/protein‐kinase‐B (PI3K/AKT) pathway signaling, which limits treatment efficacy. The combination of MEK and PI3K inhibition prevents AKT hyperactivation, resulting in superior antitumor activity compared with monotherapy.

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          GSVA: gene set variation analysis for microarray and RNA-Seq data

          Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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            QuPath: Open source software for digital pathology image analysis

            QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
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              Next-generation characterization of the Cancer Cell Line Encyclopedia

              Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous backbone upon which to study genetic variants, candidate targets, small molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from various lineages and ethnicities. Integrating these data with functional characterizations such as drug-sensitivity data, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource to accelerate cancer research using model cancer cell lines.
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                Author and article information

                Contributors
                moshee@bgu.ac.il
                Journal
                Mol Oncol
                Mol Oncol
                10.1002/(ISSN)1878-0261
                MOL2
                Molecular Oncology
                John Wiley and Sons Inc. (Hoboken )
                1574-7891
                1878-0261
                31 August 2023
                December 2023
                : 17
                : 12 ( doiID: 10.1002/mol2.v17.12 )
                : 2618-2636
                Affiliations
                [ 1 ] The Shraga Segal Department of Microbiology, Immunology, and Genetics Ben‐Gurion University of the Negev Beer‐Sheva Israel
                [ 2 ] Division of Radiooncology‐Radiobiology German Cancer Research Center (DKFZ) Heidelberg Germany
                [ 3 ] Department of Chemical Engineering Ben‐Gurion University of the Negev Beer‐Sheva Israel
                [ 4 ] Department of Biochemistry University of Oxford Oxford UK
                [ 5 ] School of Pharmaceutical Sciences Tianjin Medical University Tianjin China
                [ 6 ] Immunogenomics and Precision Oncology Platform, Department of Surgery Memorial Sloan Kettering Cancer Center New York New York USA
                [ 7 ] Section Experimental and Translational Head and Neck Oncology, Department of Otolaryngology, Head and Neck Surgery University Hospital Heidelberg Germany
                [ 8 ] Research Group Molecular Mechanisms of Head and Neck Tumors Deutsches Krebsforschungszentrum (DKFZ) Heidelberg Germany
                Author notes
                [*] [* ] Correspondence

                M. Elkabets, The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben‐Gurion University of the Negev, Beer‐Sheva 84105, Israel

                Fax: +972 86477626

                Tel: +972 86428846

                E‐mail: moshee@ 123456bgu.ac.il

                Author information
                https://orcid.org/0000-0002-8064-1435
                https://orcid.org/0000-0003-3634-9098
                Article
                MOL213500 MOLONC-22-0860.R2
                10.1002/1878-0261.13500
                10701778
                37501404
                573730e1-ded2-4bc7-a3b3-9bf2d17772eb
                © 2023 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 May 2023
                : 26 October 2022
                : 26 July 2023
                Page count
                Figures: 6, Tables: 0, Pages: 2636, Words: 12360
                Funding
                Funded by: German Cancer Research Center‐ Ministry of Science and Technology
                Award ID: 001192
                Funded by: Israel Science Foundation , doi 10.13039/501100003977;
                Award ID: 302/21
                Funded by: Natural Science Foundation of China and Israel Science Foundation
                Award ID: 3409/20
                Funded by: United States Israel Binational Science Foundation
                Award ID: 2021055
                Funded by: NIH , doi 10.13039/100000002;
                Award ID: R01 (DE027738)
                Funded by: US Department of Defense , doi 10.13039/100010210;
                Award ID: CA210784
                Funded by: NIH/NCI Cancer Center Support , doi 10.13039/100000002;
                Award ID: P30 (CA008748)
                Categories
                Cancer and Oncology
                Research Article
                Research Articles
                Custom metadata
                2.0
                December 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.5 mode:remove_FC converted:07.12.2023

                Oncology & Radiotherapy
                drug resistance,head and neck cancer,pi3k and egfr signaling,trametinib

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