Tumors survive by creating a tumor microenvironment(TME)that suppresses antitumor immunity.The TME suppresses the immune system by limiting antigen presentation,inhibiting lymphocyte and natural killer(NK)cell activat...Tumors survive by creating a tumor microenvironment(TME)that suppresses antitumor immunity.The TME suppresses the immune system by limiting antigen presentation,inhibiting lymphocyte and natural killer(NK)cell activation,and facilitating T cell exhaustion.Checkpoint inhibitors like anti-PD-1 and anti-CTLA4 are immunostimulatory antibodies,and their blockade extends the survival of some but not all cancer patients.Extracellular adenosine triphosphate(ATP)is abundant in inflamed tumors,and its metabolite,adenosine(ADO),is a driver of immunosuppression mediated by adenosine A2A receptors(A2AR)and adenosine A2B receptors(A2BR)found on tumor-associated lymphoid and myeloid cells.This review will focus on adenosine as a key checkpoint inhibitor-like immunosuppressive player in the TME and how reducing adenosine production or blocking A2AR and A2BR enhances antitumor immunity.展开更多
Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these drugs.Identifying these mechanism-independent interactions could improve drug s...Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these drugs.Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing.Here,we develop iDTPnd(integrated Drug Target Predictor with negative dataset),a computational approach for large-scale discovery of novel targets for known drugs.For a given drug,we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites.To facilitate assessment of unintended targets,iDTPnd also provides a docking-based interaction score and its statistical significance.We confirm the interactions of sorafenib,imatinib,dasatinib,sunitinib,and pazopanib with their known targets at a sensitivity of 52%and a specificity of 55%.We also validate 10 predicted novel targets by using in vitro experiments.Our results suggest that proteins other than kinases,such as nuclear receptors,cytochrome P450,and MHC class I molecules,can also be physiologically relevant targets of kinase inhibitors.Our method is general and broadly applicable for the identification of protein–small molecule interactions,when sufficient drug–target 3D data are available.The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.展开更多
文摘Tumors survive by creating a tumor microenvironment(TME)that suppresses antitumor immunity.The TME suppresses the immune system by limiting antigen presentation,inhibiting lymphocyte and natural killer(NK)cell activation,and facilitating T cell exhaustion.Checkpoint inhibitors like anti-PD-1 and anti-CTLA4 are immunostimulatory antibodies,and their blockade extends the survival of some but not all cancer patients.Extracellular adenosine triphosphate(ATP)is abundant in inflamed tumors,and its metabolite,adenosine(ADO),is a driver of immunosuppression mediated by adenosine A2A receptors(A2AR)and adenosine A2B receptors(A2BR)found on tumor-associated lymphoid and myeloid cells.This review will focus on adenosine as a key checkpoint inhibitor-like immunosuppressive player in the TME and how reducing adenosine production or blocking A2AR and A2BR enhances antitumor immunity.
基金supported by funding from King Abdullah University of Science and Technology,Office of Sponsored Research(Grant No.FCC/1/1976-25).
文摘Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these drugs.Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing.Here,we develop iDTPnd(integrated Drug Target Predictor with negative dataset),a computational approach for large-scale discovery of novel targets for known drugs.For a given drug,we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites.To facilitate assessment of unintended targets,iDTPnd also provides a docking-based interaction score and its statistical significance.We confirm the interactions of sorafenib,imatinib,dasatinib,sunitinib,and pazopanib with their known targets at a sensitivity of 52%and a specificity of 55%.We also validate 10 predicted novel targets by using in vitro experiments.Our results suggest that proteins other than kinases,such as nuclear receptors,cytochrome P450,and MHC class I molecules,can also be physiologically relevant targets of kinase inhibitors.Our method is general and broadly applicable for the identification of protein–small molecule interactions,when sufficient drug–target 3D data are available.The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.
