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1.
Oncoimmunology ; 12(1): 2217737, 2023.
Article in English | MEDLINE | ID: mdl-37288324

ABSTRACT

Immune checkpoint inhibition (ICI) has revolutionized cancer treatment; however, only a subset of patients benefit long term. Therefore, methods for identification of novel checkpoint targets and development of therapeutic interventions against them remain a critical challenge. Analysis of human genetics has the potential to inform more successful drug target discovery. We used genome-wide association studies of the 23andMe genetic and health survey database to identify an immuno-oncology signature in which genetic variants are associated with opposing effects on risk for cancer and immune diseases. This signature identified multiple pathway genes mapping to the immune checkpoint comprising CD200, its receptor CD200R1, and the downstream adapter protein DOK2. We confirmed that CD200R1 is elevated on tumor-infiltrating immune cells isolated from cancer patients compared to the matching peripheral blood mononuclear cells. We developed a humanized, effectorless IgG1 antibody (23ME-00610) that bound human CD200R1 with high affinity (KD <0.1 nM), blocked CD200 binding, and inhibited recruitment of DOK2. 23ME-00610 induced T-cell cytokine production and enhanced T cell-mediated tumor cell killing in vitro. Blockade of the CD200:CD200R1 immune checkpoint inhibited tumor growth and engaged immune activation pathways in an S91 tumor cell model of melanoma in mice.


Subject(s)
Leukocytes, Mononuclear , T-Lymphocytes , Humans , Mice , Animals , Genome-Wide Association Study , Immunoglobulins
2.
Cell ; 177(2): 414-427.e13, 2019 04 04.
Article in English | MEDLINE | ID: mdl-30951669

ABSTRACT

PD-L1 on the surface of tumor cells binds its receptor PD-1 on effector T cells, thereby suppressing their activity. Antibody blockade of PD-L1 can activate an anti-tumor immune response leading to durable remissions in a subset of cancer patients. Here, we describe an alternative mechanism of PD-L1 activity involving its secretion in tumor-derived exosomes. Removal of exosomal PD-L1 inhibits tumor growth, even in models resistant to anti-PD-L1 antibodies. Exosomal PD-L1 from the tumor suppresses T cell activation in the draining lymph node. Systemically introduced exosomal PD-L1 rescues growth of tumors unable to secrete their own. Exposure to exosomal PD-L1-deficient tumor cells suppresses growth of wild-type tumor cells injected at a distant site, simultaneously or months later. Anti-PD-L1 antibodies work additively, not redundantly, with exosomal PD-L1 blockade to suppress tumor growth. Together, these findings show that exosomal PD-L1 represents an unexplored therapeutic target, which could overcome resistance to current antibody approaches.


Subject(s)
B7-H1 Antigen/metabolism , B7-H1 Antigen/physiology , Tumor Microenvironment/immunology , Animals , Antibodies, Monoclonal/therapeutic use , Cell Line, Tumor , Exosomes/metabolism , Humans , Immunotherapy , Lymphocyte Activation/immunology , Male , Mice , Mice, Inbred C57BL , Programmed Cell Death 1 Receptor/antagonists & inhibitors , T-Lymphocytes/immunology , Tumor Microenvironment/physiology
3.
Nat Med ; 25(2): 301-311, 2019 02.
Article in English | MEDLINE | ID: mdl-30643286

ABSTRACT

Cancer cells develop mechanisms to escape immunosurveillance, among which modulating the expression of immune suppressive messenger RNAs is most well-documented. However, how this is molecularly achieved remains largely unresolved. Here, we develop an in vivo mouse model of liver cancer to study oncogene cooperation in immunosurveillance. We show that MYC overexpression (MYCTg) synergizes with KRASG12D to induce an aggressive liver tumor leading to metastasis formation and reduced mouse survival compared with KRASG12D alone. Genome-wide ribosomal footprinting of MYCTg;KRASG12 tumors compared with KRASG12D revealed potential alterations in translation of mRNAs, including programmed-death-ligand 1 (PD-L1). Further analysis revealed that PD-L1 translation is repressed in KRASG12D tumors by functional, non-canonical upstream open reading frames in its 5' untranslated region, which is bypassed in MYCTg;KRASG12D tumors to evade immune attack. We show that this mechanism of PD-L1 translational upregulation was effectively targeted by a potent, clinical compound that inhibits eIF4E phosphorylation, eFT508, which reverses the aggressive and metastatic characteristics of MYCTg;KRASG12D tumors. Together, these studies reveal how immune-checkpoint proteins are manipulated by distinct oncogenes at the level of mRNA translation, which can be exploited for new immunotherapies.


Subject(s)
Immunotherapy , Liver Neoplasms/immunology , Liver Neoplasms/therapy , Protein Biosynthesis , 5' Untranslated Regions/genetics , Animals , B7-H1 Antigen/metabolism , Base Sequence , Disease Progression , Down-Regulation , Eukaryotic Initiation Factor-4E/metabolism , Gene Expression Regulation, Neoplastic , Immune Evasion , Kaplan-Meier Estimate , Liver Neoplasms/genetics , Liver Neoplasms/pathology , Mice, Inbred C57BL , Neoplasm Metastasis , Open Reading Frames/genetics , Proto-Oncogene Proteins c-myc/metabolism , Proto-Oncogene Proteins p21(ras)/metabolism , Pyridines/pharmacology , Pyrimidines/pharmacology , Transcription, Genetic , Tumor Microenvironment , Up-Regulation/genetics
4.
IEEE Trans Biomed Eng ; 59(10): 2838-44, 2012 Oct.
Article in English | MEDLINE | ID: mdl-22875240

ABSTRACT

We apply the time-frequency analysis to the endocavitarian signal of patients suffering from paroxysmal atrial fibrillation. The time-frequency spectrum reveals the components of the endocavitarian signal. These components are located in the regions of the time-frequency domain that differ for in-rhythm and in-atrial fibrillation signals. By using experimental data, we perform a statistical study of these regions, and we obtain their average value. The difference in the shape of these regions is caused by the re-entry circuits that characterize atrial fibrillation. We propose a propagation model for atrial fibrillation based on the re-entry circuits, which explains the shape of the time-frequency spectrum.


Subject(s)
Atrial Fibrillation/physiopathology , Electrocardiography/methods , Signal Processing, Computer-Assisted , Humans , Models, Statistical , Time Factors
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