Spatially Tuned Localization of Interleukins and OX40 Agonists EnhancesSynergistic Anti-Tumor Immunity
Klich, J.; Nejatfard, A.; Meany, E.; Ou, B. S.; Baillet, J.; Appel, E.
Show abstract
Advances in immunotherapy have revolutionized the current standard of care for cancer patients, but unfortunately, most approaches still fail to mount a robust anti-cancer effect. This is in part due to a highly immunosuppressive tumor microenvironment which has developed bio-orthogonal mechanisms of immune escape. To address this challenge, the field has turned to combination immunotherapies, but systemic administration of potent combination therapies has resulted in severe immune related adverse effects and toxicity. Enabling potent combination immunotherapies requires administering these immune agonists in a way that more closely resembles the endogenous cancer immunity cycle, a tightly regulated sequence of cues in both space and time. Here, we explore the ability of an injectable hydrogel depot to enable the rational localization of potent immunotherapeutic cytokines (IL-12, IL-2) and antibodies (OX40a). We hypothesized that selectively altering the biodistribution of these cargo would enable tolerable and synergic anti-cancer combinations, so we leveraged a previously characterized injectable polymer-nanoparticle (PNP) hydrogel system to deliver these agonists either intratumorally (IT) or peritumorally (PT). Using in vivo imaging, we demonstrated that site of administration is critical to redistributing cargo to either the tumor or tumor draining lymph node (tdLN). Further, we demonstrated that the targeted localization of cytokine and antibody therapies synergistically improved treatment efficacy in the B16F10 and MC38 tumor models and altered cellular phenotypes in these microenvironments. This approach thus represents a crucial new strategy for basic cancer immunology and materials-based immuno-engineering research while improving therapeutic efficacy.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Single-cell profiling guided combinatorial immunotherapy for fast-evolving CDK4/6 inhibitor resistant HER2-positive breast cancer 95%
- Dextran-based T-cell expansion nanoparticles for manufacturing CAR T cells with augmented efficacy 95%
- Systemic Brain Tumor Delivery of Synthetic Protein Nanoparticles for Glioblastoma Therapy 95%
Similar papers in this journal
- Hydrogel-based slow release of a receptor-binding domain subunit vaccine elicits neutralizing antibody responses against SARS-CoV-2 95%
- Extracellular matrix scaffold-assisted tumor vaccines induce tumor regression and long-term immune memory 95%
- Physicochemical Targeting of Lipid Nanoparticles to the Lungs Induces Clotting: Mechanisms and Solutions 93%
Similar papers in this journal
- Dormancy-inducing 3D-engineered matrix uncovers mechanosensitive and drug protective FHL2-p21 signaling axis 94%
- Immunotherapy of glioblastoma explants induces interferon-γ responses and immune cell rearrangements in tumor center, but not periphery 93%
- In vivo mRNA delivery to virus-specific T cells by light-induced ligand exchange of MHC class I antigen-presenting nanoparticles 93%
Similar papers in this journal
- Collagen-binding IL-12 expressing STEAP1 CAR-T cells reduce toxicity and eradicate mouse prostate cancer in combination with checkpoint inhibitors 94%
- An Injectable Subcutaneous Colon-Specific Immune Niche For The Treatment Of Ulcerative Colitis 94%
- Immunometabolic cues recompose and reprogram the microenvironment around biomaterials 93%
Similar papers in this journal
- SARS-CoV-2 B Epitope-Guided Neoantigen NanoVaccines Enhance Tumor-Specific CD4/CD8 T Cell Immunity Through B Cell Antigen Presentation 96%
- Immunofilaments Provide a Nanoscale Platform for In Vivo T Cell Expansion and Cancer Immunotherapy 96%
- Multivalent, Bispecific ????B7-H3-????CD3 Chemically Self-Assembled Nanorings Direct Potent T-cell Responses Against Medulloblastoma 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.