MDB-39. ASSESSMENT OF MEDULLOBLASTOMA PATIENTS THROUGH MONITORING EXTRACELLULAR VESICLES VIA SURFACE-ENHANCED RAMAN SPECTROSCOPY COMBINED WITH MACHINE LEARNING
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Conference abstract on monitoring medulloblastoma via Raman spectroscopy of extracellular vesicles; a clinical biomarker question.
It develops a cancer-monitoring assay using extracellular vesicles and machine learning, not research practice.
Biomedical liquid-biopsy diagnostic study of medulloblastoma, not metaresearch.
Résumé
Abstract Medulloblastoma (MB) is the most common brain pediatric tumor associated with considerable morbidity. While MB is treated with multimodal approaches, including surgery, the currently available technologies fail to provide non-invasive access for continuous assessment of disease progression and therapy efficacy. There is a need to develop monitoring systems for frequent use with minimum invasiveness that provides actionable information incorporating the heterogeneous nature of MB. Liquid biopsy is a non-invasive approach using circulating biomarkers, including nano-sized extracellular vesicles (EVs). EVs are secreted by all cells, even cancerous ones. Molecular composition of cancer EVs contains fingerprints of their parental cell, reflective of salient features of the underlying disease. We used 54 pediatric plasma samples of MB patients to determine feasibility of assessing disease progression through EVs analysis. EVs were isolated from plasma using size exclusion chromatography prior to loading onto unique MoSERS chip. MoSERS platform contains nanostructured capture element for single EV detection capable of profiling liquid biopsy samples for surface-enhanced Raman Spectroscopy. A total of 54 datasets were generated, each comprised of EV spectra collected with the MoSERS chip at a single EV resolution. A spectral library was integrated with the datasets of patients with confirmed MB diagnosis (n=34) and healthy controls (n=20), required to train and test a machine-learning implementation. The combination of MoSERS with robust machine learning algorithm provides a strong analysis and a simple interpretation of the patient’s health status. We optimized a pipeline for the data collection and analysis, demonstrating the feasibility of the MoSERS platform for pediatric cancer indication and need for less than 10 μl of sample to generate each unique dataset. Preliminary results demonstrate that MoSERS performance correlates with the results of clinical standards, providing a proof-of-concept for its implementation as a non-invasive and accessible alternative to monitor the health status of MB patients.
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La notice
- Revue
- Neuro-Oncology
- Thématique
- Extracellular vesicles in disease
- Domaine
- Biochemistry, Genetics and Molecular Biology
- Établissements canadiens
- McGill University Health CentreMcGill University
- Organismes subventionnaires
- —
- Mots-clés
- Extracellular vesiclesLiquid biopsyMedulloblastomaMachine learningComputer scienceRaman spectroscopyExtracellular vesicleArtificial intelligenceMedicineBioinformaticsBiomedical engineeringPathologyCancerChemistryInternal medicineBiology
- Résumé présent dans OpenAlex
- oui