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A novel approach for a genome-wide detection of synthetically-lethal genes in cancer: Towards rational drug target identification and personalized treatments (Ramot)

A universal computational framework for identifying molecular pathways underlying cancer viability and resistance. Our approach is applied to mining large collections of cancer patients’ data and identifying classes of genetic interactions termed synthetic lethality (SL).

Synthetic lethality - The diagram above shows how a combination of mutations in two or more genes leads to cell death (lethality), whereas a mutation in only one of these genes is said to be viable.

The Need: A personalized, customized treatment targeting each tumor's weak spot
Each cancer tumor has its own genetic profile and therefore reacts differently to the treatments used to eliminate it. The world of personalized medicine needs technologies that can link a specific tumor with a specific treatment.
Often, tumors develop resistance to the drugs used to treat them. There is a great unmet need in predicting which tumors have a high probability of developing such resistance, as well as ways of preventing such resistance from occurring in the first place.
Technology today has reached the point where data generation is not a problem. The challenge is how to use sophisticated algorithms to analyze big data and identify the precise genes to be targeted in order to eliminate specific subsets of tumors.
How it works – statistical analysis is performed on various data from actual tumors and a network of gene interactions is formed. Then, genetic data from a specific patient's tumor is fed into the network and the tumor's weak spot is identified for different applications
Technology Application
A 'personalized medicine' data analysis platform for predicting tumor weak spots for the following applications:
Stratification of clinical trial patients – Clinical trials can be designed to include only patients whose tumor is predicted to interact with the proposed drug.
Prediction of drug response - A diagnostic tool to assist oncologists in choosing the correct course of treatment. The technology predicts positive/negative drug response based on the analysis of the patient’s tumor.
Drug repurposing – Using this technology, FDA approved drugs (not necessarily cancer drugs) with a known 'Mode of Action' can be repurposed to treat subsets of certain cancers.
New targets for drug development – Using this technology, a large number of new targets for drug development can be identified.
Novel adjuvants that can prevent the tumor from developing drug resistance – Based on the predicted type of resistance, this technology can match a relevant adjuvant that will block the tumor from developing the specific resistance.
Patent Information
Provisional patent filed in December 2015 - SLICK: A clinically based pipeline for the identification and utilization of synthetic lethal interactions to advance cancer therapy
The algorithms were validated using large-scale prediction models and actual treatment of cancer cells.
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