APPLICATIONS OF TECHNOLOGY:
- Personalized cancer treatment
- Clinical trials
- Development of secondary therapeutic combinations
- Identifies molecular markers with greater specificity
- Distinctly models diverse drug response patterns
- Improves clinical outcomes
- Applies to multiple organ-specific cancers (breast, colon, pancreatic, ovarian, etc.)
A team of Berkeley Lab researchers headed by Joe Gray has developed a new computational method, based on spline functions, that more accurately predicts how cancer cells will respond to therapeutics. The Berkeley Lab method models physiological responses including drug sensitivity, apoptosis, metabolic phenotype, and metastatic phenotype. It predicts the magnitude of the response in new cancer samples and gives a physician information enabling therapeutics to be targeted much more effectively. The method also makes it possible to rank-order drugs according to their likely efficacy. It requires no data discretization and avoids subjective thresholds for identifying the responders.
Because of the molecular diversity of cancer cells, a specific treatment may be effective for some cancer patients and not for others. Therefore, identification of molecular predictors of response to therapeutic agents is an increasingly important aspect of the efforts to individualize treatment of cancer and other diseases. Improvements in this field will also address the increasing costs of discovering new cancer treatments by minimizing failed clinical trials resulting from ineffective therapeutics.
Existing computational methods for identifying biomarkers are optimized for heterogeneous cell line systems, such as NCI-60. Thus, they do not take full advantage of the dynamic response patterns observed in the organ-specific cancer cell line panels. The Berkeley Lab technology improves on the current method, and the researchers have shown that the predictors identified by this method in vitro translate to the clinic.
- PCT publication WO/2009/123634 available at www.wipo.int. Available for licensing or collaborative research.
To learn more about licensing a technology from LBNL see here.
REFERENCE NUMBER: IB-2546
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