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      Prospect for application of mathematical models in combination cancer treatments

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      Informatics in Medicine Unlocked
      Elsevier BV

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          Combination therapy in combating cancer

          Combination therapy, a treatment modality that combines two or more therapeutic agents, is a cornerstone of cancer therapy. The amalgamation of anti-cancer drugs enhances efficacy compared to the mono-therapy approach because it targets key pathways in a characteristically synergistic or an additive manner. This approach potentially reduces drug resistance, while simultaneously providing therapeutic anti-cancer benefits, such as reducing tumour growth and metastatic potential, arresting mitotically active cells, reducing cancer stem cell populations, and inducing apoptosis. The 5-year survival rates for most metastatic cancers are still quite low, and the process of developing a new anti-cancer drug is costly and extremely time-consuming. Therefore, new strategies that target the survival pathways that provide efficient and effective results at an affordable cost are being considered. One such approach incorporates repurposing therapeutic agents initially used for the treatment of different diseases other than cancer. This approach is effective primarily when the FDA-approved agent targets similar pathways found in cancer. Because one of the drugs used in combination therapy is already FDA-approved, overall costs of combination therapy research are reduced. This increases cost efficiency of therapy, thereby benefiting the “medically underserved”. In addition, an approach that combines repurposed pharmaceutical agents with other therapeutics has shown promising results in mitigating tumour burden. In this systematic review, we discuss important pathways commonly targeted in cancer therapy. Furthermore, we also review important repurposed or primary anti-cancer agents that have gained popularity in clinical trials and research since 2012.
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            Irradiation and anti-PD-L1 treatment synergistically promote antitumor immunity in mice.

            High-dose ionizing irradiation (IR) results in direct tumor cell death and augments tumor-specific immunity, which enhances tumor control both locally and distantly. Unfortunately, local relapses often occur following IR treatment, indicating that IR-induced responses are inadequate to maintain antitumor immunity. Therapeutic blockade of the T cell negative regulator programmed death-ligand 1 (PD-L1, also called B7-H1) can enhance T cell effector function when PD-L1 is expressed in chronically inflamed tissues and tumors. Here, we demonstrate that PD-L1 was upregulated in the tumor microenvironment after IR. Administration of anti-PD-L1 enhanced the efficacy of IR through a cytotoxic T cell-dependent mechanism. Concomitant with IR-mediated tumor regression, we observed that IR and anti-PD-L1 synergistically reduced the local accumulation of tumor-infiltrating myeloid-derived suppressor cells (MDSCs), which suppress T cells and alter the tumor immune microenvironment. Furthermore, activation of cytotoxic T cells with combination therapy mediated the reduction of MDSCs in tumors through the cytotoxic actions of TNF. Our data provide evidence for a close interaction between IR, T cells, and the PD-L1/PD-1 axis and establish a basis for the rational design of combination therapy with immune modulators and radiotherapy.
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              SynergyFinder 2.0: visual analytics of multi-drug combination synergies

              Abstract SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.
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                Author and article information

                Contributors
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                Journal
                Informatics in Medicine Unlocked
                Informatics in Medicine Unlocked
                Elsevier BV
                23529148
                2021
                2021
                : 23
                : 100534
                Article
                10.1016/j.imu.2021.100534
                708fc43b-91db-4782-9f64-12a5fb596879
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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