Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

K. Hoang

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

PACM-03 TUMOR–BRAIN CROSSTALK DRIVES NEURAL MICROENVIRONMENT REMODELING AND PROMOTE DEMYELINATION IN BRAIN METASTASES

Abstract Brain metastases(BM) represent the most common intracranial tumors and are associated with a markedly worse prognosis compared with metastases at other sites. Metastatic lesions that colonize the brain adapt to its unique cellular and metabolic microenvironment. This adaptation drives the emergence of specialized tumor subpopulations with altered biological behavior. Understanding how metastatic tumors interact with and influence the surrounding neural tissue is therefore essential for developing strategies that preserve neurological function. The objective of this study was to investigate the interactions between BM and the surrounding brain parenchyma, with particular emphasis on the mechanisms underlying demyelination and neuronal impairment. Brain metastasis organoids(BMOs) were generated from surgically resected patient BM specimens and co-cultured with long-term viable human brain slices. Immunofluorescence staining was used to evaluate changes in myelination and cellular responses within the brain tissue. In parallel, transcriptomic profiling and tumor–brain parenchymal ligand–receptor interaction analyses were performed to identify dysregulated molecular signaling pathways with potential therapeutic relevance. BMOs successfully integrated with human brain slices and reproduced key characteristics of tumor behavior within the neural microenvironment. Myelin basic protein(MBP) immunostaining demonstrated significant demyelination in brain slices co-cultured with BMOs compared with control slices(P < 0.05). Transcriptomic analysis revealed clusters of differentially expressed genes, including marked downregulation of pathways associated with myelination and neurogenesis. Further investigation identified critical regulators of neuronal and glial function, including a downregulated gene network involved in myelin maintenance. Ligand–receptor interaction analysis revealed prominent dysregulation of NGF and TGF-β signaling. Pharmacologic targeting of these pathways in co-culture experiments significantly restored MBP signal intensity(P < 0.05). These findings demonstrate that BM actively disrupt myelin homeostasis and neural signaling within surrounding brain tissue. This ex-vivo platform provides a robust experimental system to study tumor–brain interactions and to evaluate therapeutic strategies aimed at limiting tumor-induced neurological damage.

Youssef M. Zohdy, Arman Jahangiri, Amelia Tong et al. · 0 citations
Review Open access Aug 2026

Integrating single-cell transcriptomics with deep learning for glioblastoma treatment

Glioblastomas are aggressive, heterogeneous tumors that present significant challenges in both diagnosis and treatment. Despite advances in surgical resection, radiotherapy, and chemotherapy with temozolomide (TMZ), the prognosis for glioblastoma patients remains poor, largely due to tumor heterogeneity and resistance mechanisms, such as genetic mutations in DNA repair pathways. To address these specific heterogenous qualities of glioblastoma, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for characterizing glioblastoma tumors, enabling the identification of subpopulations that respond differently to treatment. However, utilizing the vast amount of data generated by scRNA-seq poses challenges in clinical applications. To overcome this challenge, computational models have been introduced to more effectively process patient scRNA-seq data into more digestible information for clinicians. More specifically, advanced deep learning approaches show promise for processing and analyzing patient scRNA-seq data, enhancing informed treatment approaches for highly heterogenous glioblastoma. This review aims to explain how scRNA-seq can be used to identify important areas of glioblastoma treatment resistance, evaluate current glioblastoma scRNA-seq-based deep learning models, and outline relevant training datasets to overcome patient scRNA-seq data availability limitations. Ultimately, these deep learning models can be utilized by researchers and clinicians to provide more informed and precise treatment to glioblastoma patients.

Marybeth G. Yonk, Mainak Mustafi, Megan A Lim et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.