Early Detection and Personalized Treatment of Breast Cancer, Using Comparative Genomics

Tabach Yuval, HUJI, School of Medicine - IMRIC, Developmental Biology and Cancer Research

Prof. Levy-Lahad Ephrat – Director of the Medical Genetics Institute at Shaare Zedek Hospital, Jerusalem


LifeSciences and BioTechnology  

Computer Science


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.

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:

  • Identify the causative mutations
  • Optimize and guide treatments

Woman at risk:

  • Risk predictor
  • Optimized treatments  (PARPi)

Contact for more information:

Mel Larrosa
VP Business Development Healthcare
Contact ME: