Summary of the technology
From big data analysis to personalized medicine in cancer:
towards the development of a computational tool for the rational design of patient-specific combination therapy.
Project ID : 10-2016-4322
Description of the technology
Medical Device, BioInformatics, Oncology/Cancer
This technology provides a way to design patient-specific drug combinations through an accurate tumor classification, such that every single tumor can be mapped precisely and unambiguously according to the molecular aberrations that it harbors.
- Addressing individual patients, in the opposite to conventional statistical analysis method relating to pre-defined categories of the patient population
- Once the information from multiple patients is transformed into a low-dimensional space, the technology can provide patient-specific unbalanced network structures. These structures can be used to rationally design patient specific drug therapies.
- Prediction of direction change in biological systems, thereby generating rationalized strategies for the manipulation of their phenotypes.
The developed algorithm allows to decoding of altered molecules as well as the structure of the altered molecular networks in each tumor. Specifically, the patient-specific signaling signature, representing the flow of cellular information. The algorithm predicts drug combinations that will target particular components of these networks, in order to restore balance, and correct the tissues, preferentially by killing the tumor cell.
- Accurate resolution of patient-specific network structures, or a patient-specific signaling protein network signature.
- The algorithm helps clinicians to rationally design patient-specific drug combinations. Inhibition of the entire set of tumor-specific processes should stop the disease and significantly decrease the chances for the development of drug resistance.
Current development stage
TRL 5 – Alpha testing for of the software by in-house developers/testers
The plan is to extend the approach above to additional cancer types, in vivo animal models and patient derived cancer tissues.