Early Detection and Personalized Treatment of Breast Cancer, Using Comparative Genomics
Prof. Levy-Lahad Ephrat – Director of the Medical Genetics Institute at Shaare Zedek Hospital, Jerusalem
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Category |
LifeSciences and BioTechnology |
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Keywords |
AI, Artificial Intelligence, Machine Learning, Digital Health, Personalized Medicine, Oncology, Breast cancer, Data Integration |
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Current development stage |
General list: TRL4 Technology validated in lab |
Application
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Breast cancer is the most common cancer among women.
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In >70% of cases with suspected hereditary, no mutations could be found in the known susceptibility genes (BRCA1, BRCA2, PALB2, etc.).
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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.
Technology
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.
Our Innovation
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:
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Identify the causative mutations
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Optimize and guide treatments
Woman at risk:
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Risk predictor
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Optimized treatments (PARPi)
