Gene expression risk score and algorithm for clinically aggressive meningioma identification and therapy guidance

Tech ID: 31917 / UC Case 2020-146-0

Technology Description

This invention is a novel prognostic and predictive diagnostic test that can be used to better risk-stratify patients and to guide therapy for clinically aggressive meningioma. Consisting of a prognostic gene expression signature and a gene signature-based risk score between 0 and 1, it significantly outperforms the current gold standard (the pathology-based WHO grading system) in predicting tumor control, time to treatment failure, and overall survival. The improved performance of this test is due to the application of bioinformatics and machine learning algorithms to a unique UCSF dataset, with subsequent validation in a publicly available dataset. This discovery dataset was significantly enriched for clinically relevant endpoints, allowing for targeted identification of novel gene expression patterns with direct correlation to patient outcomes. In contrast, the validation dataset was more representative of the general population of meningioma patients, thus lending credence to the generalizability of this diagnostic test. While further validation and optimization are still required to bring this invention to market, the core technology has been developed and tested, and several hundred more samples from the dataset are available for continued assessment. As there are currently no other commercial technologies available to supplement pathologic review of meningioma samples, this invention will help address the dire need for more informed therapy decisions through improved risk stratification and recommendations for adjuvant radiotherapy to patients with meningioma.

 


Advantages

  • Outperforms current gold standard (pathology-based WHO grading system) across multiple measures
  • Allows for risk stratification of patients in a clinically tractable manner, unlike genetic/epigenetic profiling of meningiomas and gene mutation panels, which are either not reliably prognostic, do not necessarily predict aggressive behavior, or are not in routine clinical use
  •  Gene expression signature and risk score based on biologically and clinically relevant measures
  •  Cost and complexity are low compared to whole genome/exome and epigenetic profiling, and are anticipated to be lower than that of gene mutation panels used in routine clinical practice such as the UCSF 500 panel
  • Potential to reduce unnecessary radiation treatment and associated costs following meningioma diagnosis

 

Looking for Partners

To commercialize the technology for public benefit

Patent Status

Patent Pending

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Other Information

Keywords

diagnostic, oncology, meningioma, precision medicine, prognostic

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