Summary of the technology
Using cutting-edge comparative genomics, data integration, and machine learning, the researchers will develop a comprehensive gene panel that can identify increased risk of breast cancer and offer personalized treatment options based on PARP inhibitors.
Project ID : 6-2018-6690
Description of the technology
Prof. Levy-Lahad Ephrat – Director of the Medical Genetics Institute at Shaare Zedek Hospital, Jerusalem
LifeSciences and BioTechnology
AI, Artificial Intelligence, Machine Learning, Digital Health, Personalized Medicine, Oncology, Breast cancer, Data Integration
Current development stage
General list: TRL4 Technology validated in lab
- Breast cancer is the most common cancer among women.
- In >70% of cases with suspected hereditary, no mutations could be found in the known susceptibility genes (BRCA1, BRCA2, PALB2, etc.).
- Establishing a comprehensive panel that identifies the driver of mutations in patients is of particular importance for personalized risk assessment, prevention and treatment of breast cancer.
Using cutting-edge comparative genomics, data integration and machine learning, we will develop a comprehensive gene panel that can identify an increased risk of breast cancer and offer personalized treatment options based on PARP inhibitors.
We aim to develop a platform for predicting the risk for hereditary breast cancer in significantly more patients and offer personalized drug treatment based on the identified mutations.
Breast cancer patient:
- Identify the causative mutations
- Optimize and guide treatments
Woman at risk:
- Risk predictor
- Optimized treatments(PARPi)
VP Business Development Healthcare
HUJI, School of Medicine - IMRIC
Developmental Biology and Cancer Research