Abstract Background Children with subtotally resected pediatric low-grade glioma (pLGG) often face multiple lines of treatment, which are seldom capable of eliminating the entire tumor. Genomics-based biomarkers are often used to select targeted therapies, but this paradigm only yields overall response rates of âź50% optimally. Functional precision medicine (FPM), where patient-specific therapeutic efficacy is evaluated by directly treating individualsâ tumor outside their body, can predict individualized drug responses for some cancers, but pLGG is notoriously difficult to maintain outside the body, limiting development of FPM for pLGG. Methods We describe what is, to our knowledge, the first platform that can maintain, treat, and analyze zero-passage pLGG tumor tissue ex vivo , facilitating FPM testing. We engraft pLGG tumors onto a previously validated organotypic brain slice culture (OBSC) platform. After ensuring reproducible engraftment and maintenance of living pLGG tumor tissue on OBSCs, we measured MAPK pathway response to targeted therapies via immunoblotting. We then measured tumor ex vivo response to targeted therapies. Results Each zero-passage pLGG tumor tissue specimen exhibited reproducible growth on the OBSC platform. Western blot demonstrated each BRAF KIAA1549 fusion+ tumor exhibited expected paradoxical MAPK upregulation to dabrafenib treatment. Two of three tumors demonstrated cytotoxicity from trametinib as predicted, whereas one tumor did not. No clinical correlates were measured in this proof-of-concept study, though this mixed response to MEK inhibition may be in line with real-world clinical responses. Conclusion The OBSC platform supports ex vivo maintenance of passage-zero pLGG tumor tissue and enables personalized drug screening to yield a new functional biomarker of pLGG drug response.
Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.