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      Amino Acid Metabolism in Cancer Drug Resistance

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      Cells
      MDPI AG

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          Abstract

          Despite the numerous investigations on resistance mechanisms, drug resistance in cancer therapies still limits favorable outcomes in cancer patients. The complexities of the inherent characteristics of tumors, such as tumor heterogeneity and the complicated interaction within the tumor microenvironment, still hinder efforts to overcome drug resistance in cancer cells, requiring innovative approaches. In this review, we describe recent studies offering evidence for the essential roles of amino acid metabolism in driving drug resistance in cancer cells. Amino acids support cancer cells in counteracting therapies by maintaining redox homeostasis, sustaining biosynthetic processes, regulating epigenetic modification, and providing metabolic intermediates for energy generation. In addition, amino acid metabolism impacts anticancer immune responses, creating an immunosuppressive or immunoeffective microenvironment. A comprehensive understanding of amino acid metabolism as it relates to therapeutic resistance mechanisms will improve anticancer therapeutic strategies.

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          CD8 + T cells regulate tumor ferroptosis during cancer immunotherapy

          Summary Cancer immunotherapy restores and/or enhances effector function of CD8+ T cells in the tumor microenvironment 1,2 . CD8+ T cells activated by cancer immunotherapy execute tumor clearance mainly by inducing cell death through perforin-granzyme- and Fas/Fas ligand-pathways 3,4 . Ferroptosis is a form of cell death that differs from apoptosis and results from iron-dependent lipid peroxide accumulation 5,6 . Although it was mechanistically illuminated in vitro 7,8 , emerging evidence has shown that ferroptosis may be implicated in a variety of pathological scenarios 9,10 . However, the involvement of ferroptosis in T cell immunity and cancer immunotherapy is unknown. Here, we find that immunotherapy-activated CD8+ T cells enhance ferroptosis-specific lipid peroxidation in tumor cells, and in turn, increased ferroptosis contributes to the anti-tumor efficacy of immunotherapy. Mechanistically, interferon gamma (IFNγ) released from CD8+ T cells downregulates expression of SLC3A2 and SLC7A11, two subunits of glutamate-cystine antiporter system xc-, restrains tumor cell cystine uptake, and as a consequence, promotes tumor cell lipid peroxidation and ferroptosis. In preclinical models, depletion of cyst(e)ine by cyst(e)inase in combination with checkpoint blockade synergistically enhances T cell-mediated anti-tumor immunity and induces tumor cell ferroptosis. Expression of system xc- is negatively associated with CD8+ T cell signature, IFNγ expression, and cancer patient outcome. Transcriptome analyses before and during nivolumab therapy reveal that clinical benefits correlate with reduced expression of SLC3A2 and increased IFNγ and CD8. Thus, T cell-promoted tumor ferroptosis is a novel anti-tumor mechanism. Targeting tumor ferroptosis pathway constitutes a therapeutic approach in combination with checkpoint blockade.
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            Cancer drug resistance: an evolving paradigm.

            Resistance to chemotherapy and molecularly targeted therapies is a major problem facing current cancer research. The mechanisms of resistance to 'classical' cytotoxic chemotherapeutics and to therapies that are designed to be selective for specific molecular targets share many features, such as alterations in the drug target, activation of prosurvival pathways and ineffective induction of cell death. With the increasing arsenal of anticancer agents, improving preclinical models and the advent of powerful high-throughput screening techniques, there are now unprecedented opportunities to understand and overcome drug resistance through the clinical assessment of rational therapeutic drug combinations and the use of predictive biomarkers to enable patient stratification.
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              A view on drug resistance in cancer

              The problem of resistance to therapy in cancer is multifaceted. Here we take a reductionist approach to define and separate the key determinants of drug resistance, which include tumour burden and growth kinetics; tumour heterogeneity; physical barriers; the immune system and the microenvironment; undruggable cancer drivers; and the many consequences of applying therapeutic pressures. We propose four general solutions to drug resistance that are based on earlier detection of tumours permitting cancer interception; adaptive monitoring during therapy; the addition of novel drugs and improved pharmacological principles that result in deeper responses; and the identification of cancer cell dependencies by high-throughput synthetic lethality screens, integration of clinico-genomic data and computational modelling. These different approaches could eventually be synthesized for each tumour at any decision point and used to inform the choice of therapy.
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                Author and article information

                Journal
                CELLC6
                Cells
                Cells
                MDPI AG
                2073-4409
                January 2022
                January 02 2022
                : 11
                : 1
                : 140
                Article
                10.3390/cells11010140
                35011702
                650b8d16-33e9-4a7e-add4-1c7481426ccd
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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