Abstract A46: Charting the synthetic lethality landscape in pediatric cancer to advance whole-exome precision-based treatments

Abstract One of the challenges in pediatric cancer (PC) research is that cancers in children are uncommon and are quite different from adults. Much of the research in adult cancers is focused on studying cancer driver genes, aiming at their therapeutic targeting. However, PCs are often driven by relatively few genetic alterations that are distinct from those that occur in adult cancers. Here we apply a novel data-driven approach to identify the synthetic lethality (SL) networks of several different pediatric cancers. These provide a new platform for discovering novel vulnerabilities in primary tumors from PCs that extends previous approaches commonly used in adult cancer. SL interactions denote the relationship between two genes whose combined inactivation is lethal to the cell, while their individual inactivation is not. To identify the SL landscape characteristic of a specific pediatric cancer, we mined the relevant pediatric cell line and patients’ tumor data in the TARGET database. Our computational framework consists of four inference steps: For SL interactions, we first identify putative SL gene pairs from the pediatric cell line dependency map generated by in vitro RNAi/CRISPR screens (depmap). Second, among the candidate gene pairs that pass the first step, we select those gene pairs whose co-inactivation is under-represented in pediatric tumors, indicating that they are selected against. Third, we further prioritize candidate SL pairs whose co-inactivation is associated with better prognosis, indicating that they may hamper tumor progression. Finally, we prioritize SL paired genes with similar evolutionary phylogenetic profiles. Applying this approach to analyze TARGET data, we identify the first genome-wide SL networks in five pediatric tumors including Wilms’ tumor, neuroblastoma, AML, ALL, and osteosarcoma. The predicted SL interactions are first tested and validated via experimental in vitro CRISPR screens. Second, we show that the PC specific SL networks are predictive of drug response in pediatric cell lines but not in adult cell lines of the corresponding tumor type. These results establish that the predicted SL interactions offer an exciting venue for developing predictive biomarkers specific for PC treatments. Importantly, these predictions were performed in an unsupervised manner, reducing the known risk of over-fitting and lack of generalizability commonly associated with supervised prediction methods. Notably, our analysis identifies many SL partners of key drivers of PCs such as WT1, MYCN, and ATRX, and the key interactions discovered include ATRX-MAP kinases, MYCN-CDC6 (cell cycle regulation), and DNMT1-HK2. These provide novel selective drug target candidates for the tumors driven by these genes and lay a basis for new treatment combinations. Taken together, these results lay a basis for a new paradigm for whole-exome SL-based precision treatments in pediatric oncology, complementing existing mutation- and fusion-based approaches. Citation Format: Fiorella Schischlik, Joo Sang Lee, Nirali Shah, Rosandra N. Kaplan, Carol J. Thiele, Brigitte Widemann, Eytan Ruppin. Charting the synthetic lethality landscape in pediatric cancer to advance whole-exome precision-based treatments [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A46.

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