Inhibition of VEGF binding to neuropilin-2 enhances chemosensitivity and inhibits metastasis in triple-negative breast cancer.
Zhiwen XuHira Lal GoelChristoph BurkartLuke BurmanYeeting E ChongAlison G BarberYanyan GengLiting ZhaiMengdie WangAyush KumarAnn MenefeeClara PolizziLisa EideKaitlyn RauchJustin RahmanKristina HamelZachary FogassySofia Klopp-SavinoSuzanne PazMingjie ZhangAndrea CubittLeslie A NangleArthur M MercurioPublished in: Science translational medicine (2023)
Although blocking the binding of vascular endothelial growth factor (VEGF) to neuropilin-2 (NRP2) on tumor cells is a potential strategy to treat aggressive carcinomas, a lack of effective reagents that can be used clinically has hampered this potential therapy. Here, we describe the generation of a fully humanized, high-affinity monoclonal antibody (aNRP2-10) that specifically inhibits the binding of VEGF to NRP2, conferring antitumor activity without causing toxicity. Using triple-negative breast cancer as a model, we demonstrated that aNRP2-10 could be used to isolate cancer stem cells (CSCs) from heterogeneous tumor populations and inhibit CSC function and epithelial-to-mesenchymal transition. aNRP2-10 sensitized cell lines, organoids, and xenografts to chemotherapy and inhibited metastasis by promoting the differentiation of CSCs to a state that is more responsive to chemotherapy and less prone to metastasis. These data provide justification for the initiation of clinical trials designed to improve the response of patients with aggressive tumors to chemotherapy using this monoclonal antibody.
Keyphrases
- monoclonal antibody
- vascular endothelial growth factor
- cancer stem cells
- endothelial cells
- locally advanced
- clinical trial
- high grade
- randomized controlled trial
- oxidative stress
- electronic health record
- squamous cell carcinoma
- binding protein
- cancer therapy
- dna binding
- human health
- bone marrow
- radiation therapy
- stem cells
- transcription factor
- climate change
- drug delivery
- mesenchymal stem cells
- risk assessment
- deep learning
- open label
- phase ii