Mapping homologous recombination repair genes through omics data integration

Irene Unterman ¹ Dolev Rahat ¹ Dana Sherill-Rofe ¹ Steven Findlay ^2,3 Idit Bloch ¹ Arash Samiei ^2,3 Alexandre Orthwein ^2,3,5,6 Aviad Zick ⁴ Yuval Tabach ¹

¹Department of Developmental Biology and Cancer Research, Institute for Medical Research-Israel-Canada, Hebrew University of Jerusalem, Israel
²Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Canada
³Division of Experimental Medicine,, McGill University, Canada
⁴Sharett Institute of Oncology, Hadassah Medical Center, Ein-Kerem, Israel
⁵Department of Microbiology and Immunology, McGill University, Canada
⁶Gerald Bronfman Department of Oncology, McGill University, Canada

Mutations in homologous recombination repair (HRR) genes trigger numerous genetic disorders. In hereditary breast and ovarian cancer (HBOC), these mutations are the drivers underlying ~30% of cases. Despite the importance of the HRR pathway it has not been fully characterized. To systematically map novel HRR genes, we previously used a computational algorithm that screens 578 eukaryote genomes at different evolutionary scales [1]. To generate a complete cohort of genes that are functionally related to the HRR, here we integrate 24 different genetic screens and databases into a database (HRRbase) that maps the interaction of every gene with the HRR pathway. Machine learning analysis of this database yields over 400 genes predicted to be highly involved in HRR. We characterize their specific interactions with the different functional modules within the HRR pathway and validate a subset of these predictions using the DR-GFP assay in human cell lines. These genes may play a significant role in HRR related genetic disorders and HBOC, potentially yielding new targets for therapeutics.