ReviewNeuron2025
Novel approaches to clinical trial design in cancer neuroscience.
Review in Neuron, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Cancer Neuroscience: From Local Neuro-Immune Microenvironments to Systemic Brain-Body Circuitry.MedComm · 2026Review
- Decoding osteosarcoma mechanobiology: PIEZO channels in tumor progression, immune remodeling, and pain.Cancer metastasis reviews · 2026Review
- Neuron-tumor crosstalk in cancer: molecular mechanisms and translational advances.Molecular cancer · 2026Review
- Prognostic value and potential upstream regulator of Schwann cells in esophageal squamous cell carcinoma.Translational cancer research · 2026Article
- Neuro-glioma activity-dependent growth mechanisms: an actionable circuit from NLGN3-ADAM10 to AMPA synapses.Translational cancer research · 2026Review
- The neuro-vascular-immune triad: the interactive network in the tumor microenvironment.Cell communication and signaling : CCS · 2026Review
- Biomarker-Guided Drug Delivery Systems and Oral Bioavailability Enhancement.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Enteric neuro-immune-tumor ecosystem in pancreatic, colorectal, and gastric malignancies: context dependence and translational priorities.Frontiers in immunology · 2026Review
- Immunotherapy and targeted therapy for high grade gliomas: current and future directions.Journal of neuro-oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The emerging field of cancer neuroscience has revealed profound bidirectional interactions between the nervous system and cancer cells, identifying novel therapeutic vulnerabilities across diverse malignancies. This review examines the unique challenges and strategies for translating these insights into effective therapies. We propose innovative approaches to overcome these barriers through drug repurposing, enhanced biomarker development, and optimized trial designs. Repurposing neuroactive drugs with established safety profiles offers an accelerated path to clinical impact, particularly for targeting glutamatergic, adrenergic, and neurotrophic signaling pathways. Emphasizing mitigation of neurotoxicity and improved patient quality of life will be paramount moving forward. Repurposed agents that show preliminary potential for "dual use" (i.e., simultaneous toxicity mitigation and synergistic anti-tumor effects) are highlighted for special consideration. Master protocols and window-of-opportunity trials provide platforms to rapidly validate mechanisms while addressing patient-centered outcomes. By systematically addressing these foundational elements across disciplines, cancer neuroscience can translate its profound mechanistic insights into meaningful therapeutic advances for patients with treatment-resistant malignancies.
Indexed as
Identifiers
What OpenQuestion holds
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.