ArticleScience advances2020
Efficient blockade of locally reciprocated tumor-macrophage signaling using a TAM-avid nanotherapy.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- Dynamic remodeling of amino acid metabolism in tumor-associated macrophages: Fueling immunosuppression, reshaping tumor niches, and unlocking metabolic checkpoints.Journal of advanced research · 2026Review
- Macrophage-derived lncRNAs in cancer: regulators of tumor progression and therapeutic targets.Medical oncology (Northwood, London, England) · 2025Review
- Proteolysis of TAM receptors in autoimmune diseases and cancer: what does it say to us?Cell death & disease · 2025Review
- Ocular Effects of MEK Inhibitor Therapy: Literature Review, Clinical Presentation, and Best Practices for Mitigation.The oncologist · 2024Review
- The causal relationship between trace element status and upper gastrointestinal ulcers: a Mendelian randomization study.Frontiers in nutrition · 2024Article
- Dual-Specificity Phosphatases in Regulation of Tumor-Associated Macrophage Activity.International journal of molecular sciences · 2023Review
- Supramolecular nanofibers co-loaded with dabrafenib and doxorubicin for targeted and synergistic therapy of differentiated thyroid carcinoma.Theranostics · 2023Article
- Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy.Science advances · 2022Article
- Probing immune infiltration dynamics in cancer by in vivo imaging.Current opinion in chemical biology · 2022Review
- Towards targeting of shared mechanisms of cancer metastasis and therapy resistance.Nature reviews. Cancer · 2022Review
- Cell-Cell Communication Networks in Tissue: Toward Quantitatively Linking Structure with Function.Current opinion in systems biology · 2021Article
- Macrophage imaging and subset analysis using single-cell RNA sequencing.Nanotheranostics · 2021Article
- Detecting Immune Response to Therapies Targeting PDL1 and BRAF by Using Ferumoxytol MRI and Macrin in Anaplastic Thyroid Cancer.Radiology · 2021Article
- Immunostimulatory biomaterials to boost tumor immunogenicity.Biomaterials science · 2020Review
- In vivo microscopy reveals macrophage polarization locally promotes coherent microtubule dynamics in migrating cancer cells.Nature communications · 2020Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Interpreting how multicellular interactions in the tumor affect resistance pathways to BRAF and MEK1/2 MAPK inhibitors (MAPKi) remains a challenge. To investigate this, we profiled global ligand-receptor interactions among tumor and stromal/immune cells from biopsies of MAPK-driven disease. MAPKi increased tumor-associated macrophages (TAMs) in some patients, which correlated with poor clinical response, and MAPKi coamplified bidirectional tumor-TAM signaling via receptor tyrosine kinases (RTKs) including AXL, MERTK, and their ligand GAS6. In xenograft tumors, intravital microscopy simultaneously monitored in situ single-cell activities of multiple kinases downstream of RTKs, revealing MAPKi increased TAMs and enhanced bypass signaling in TAM-proximal tumor cells. As a proof-of-principle strategy to block this signaling, we developed a multi-RTK kinase inhibitor nanoformulation that accumulated in TAMs and delayed disease progression. Thus, bypass signaling can reciprocally amplify across nearby cell types, offering new opportunities for therapeutic design.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.