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      High-throughput chemogenetic drug screening reveals PKC-RhoA/PKN as a targetable signaling vulnerability in GNAQ-driven uveal melanoma

      research-article
      1 , 2 , 3 , 14 , 16 , 1 , 5 , 4 , 1 , 1 , 1 , 1 , 1 , 3 , 4 , 7 , 8 , 9 , 7 , 8 , 9 , 1 , 6 , 1 , 7 , 8 , 9 , 7 , 8 , 9 , 1 , 3 , 10 , 11 , 11 , 12 , 13 , 4 , 1 , 3 , 15 , 17 ,
      Cell Reports Medicine
      Elsevier
      melanoma, GNAQ, chemogenetic drug screening, PKC, PKN/PRK, FAK, synthetic lethality, combination therapy, precision medicine

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          Summary

          Uveal melanoma (UM) is the most prevalent cancer of the eye in adults, driven by activating mutation of GNAQ/GNA11; however, there are limited therapies against UM and metastatic UM (mUM). Here, we perform a high-throughput chemogenetic drug screen in GNAQ-mutant UM contrasted with BRAF-mutant cutaneous melanoma, defining the druggable landscape of these distinct melanoma subtypes. Across all compounds, darovasertib demonstrates the highest preferential activity against UM. Our investigation reveals that darovasertib potently inhibits PKC as well as PKN/PRK, an AGC kinase family that is part of the “ dark kinome.” We find that downstream of the Gαq-RhoA signaling axis, PKN converges with ROCK to control FAK, a mediator of non-canonical Gαq-driven signaling. Strikingly, darovasertib synergizes with FAK inhibitors to halt UM growth and promote cytotoxic cell death in vitro and in preclinical metastatic mouse models, thus exposing a signaling vulnerability that can be exploited as a multimodal precision therapy against mUM.

          Graphical abstract

          Highlights

          • A chemogenetic drug screen in uveal melanoma reveals darovasertib as a top hit

          • Darovasertib is a dual inhibitor of PKC and PRK/PKN

          • Downstream of mutant Gαq, darovasertib blocks ERK and diminishes FAK activity

          • Darovasertib + FAKi act synergistically in preclinical uveal melanoma models

          Abstract

          Arang et al. define the druggable landscape of uveal melanoma and identify darovasertib as the top hit. Darovsertib acts as a dual PKC and PRK/PKN inhibitor, thereby blocking ERK and diminishing FAK activity. Darovasertib combined with FAK inhibition is highly synergistic in preclinical models of primary and metastatic uveal melanoma.

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          Most cited references82

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

            Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                18 October 2023
                21 November 2023
                18 October 2023
                : 4
                : 11
                : 101244
                Affiliations
                [1 ]Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
                [2 ]Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
                [3 ]Department of Pharmacology, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
                [4 ]Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
                [5 ]Department of Pharmacy, University of Pisa, Pisa, Italy
                [6 ]School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
                [7 ]Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA 94158, USA
                [8 ]J. David Gladstone Institutes, San Francisco, CA 94158, USA
                [9 ]Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA
                [10 ]Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
                [11 ]Verastem Oncology, Needham, MA 02494, USA
                [12 ]Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
                [13 ]Medical Research Council (MRC) Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK
                Author notes
                []Corresponding author sgutkind@ 123456health.ucsd.edu
                [14]

                Present address: Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA 94158, USA

                [15]

                X (formerly Twitter): @SilvioGutkind

                [16]

                X (formerly Twitter): @nadia_arang

                [17]

                Lead contact

                Article
                S2666-3791(23)00421-4 101244
                10.1016/j.xcrm.2023.101244
                10694608
                37858338
                68991360-5f38-415e-9acc-6ae0ba5162f3
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 February 2023
                : 8 September 2023
                : 22 September 2023
                Categories
                Article

                melanoma,gnaq,chemogenetic drug screening,pkc,pkn/prk,fak,synthetic lethality,combination therapy,precision medicine

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