The technology platform provides methods for the isolation and sequencing of serum-derived extracellular vesicles coupled with machine learning analysis to enable early detection, non-invasive monitoring, and classification of brain tumors.
Currently, brain tumor diagnosis is achieved through a radiologic assessment followed by tissue biopsy. Further progression monitoring efforts via MRI is challenging. Additionally, there are no clinically validated circulating biomarkers for the management of glioma patients.
In initial studies, the technology has been shown to enable high accuracy (up to 93%) of glioma subtype classification. The ability to non-invasively classify prognostically significant brain tumor types is supportive of timely progression monitoring efforts.
- Enhanced personalized medicine - potential for more near “real-time” monitoring of treatment effectiveness in glioma, including glioblastoma.
- Non-invasive molecular method to augment brain tumor detection, classification, monitoring, and recurrence.
- Initial data generated for brain cancer and pancreatic cyst stratification with potential for broader utility in other diseases
- Potential for early detection of primary tumor
- Potential to complement tissue biopsy for diagnosis, particularly for location-restricted tumors
- Early detection of tumor recurrence after initial resection
- Non-invasive progression monitoring
- Potential to reduce the number of required MRIs following primary treatment
- “Real-time” drug response/treatment effectiveness monitoring
- Support development of new therapeutics - identification of patients and responsive subgroups, determination of drug response (particularly in earlier trials)
- On-market therapies – identification of patients & recurring tumor monitoring
Name: Lindsay Sanford
Email : Lindsay.Sanford@ucsf.edu