Abstract B027: A pathway-informed framework to infer synthetic lethal relationships in pediatric cancer

Abstract

Abstract Synthetic lethality, the combination of two mutated genes which results in cell death, has been greatly investigated due to its therapeutic potential in cancer. Despite the common practice of experimentally detecting synthetic lethality, here we extend a computational framework that employs co-occurrence of gene mutations to infer synthetic lethality by incorporating pathways. Pathways aggregate the mutated genes into a functional unit and give power to uncover relationships containing genes of low mutation frequency e.g. in pediatric cancer, which otherwise would not be observed by testing individual gene pairs. Our proposed framework is an alternative avenue to a more focused synthetic lethality search by exploiting mutation data & pathway knowledge. We infer potentially synthetic lethal relationships based on mutated gene co-occurrence. A less often than expected co-occurrence, or so-called mutual exclusivity (ME), indicates a potentially synthetic lethal relationship whereas one that occurs more often than expected (CO) indicates a potentially advantageous relationship. In this framework we use a selected set of biological pathways from Reactome to aggregate gene mutations per pathway for each individual tumor (SNVs and indels with high/moderate impact) within two public pediatric cancer datasets, TARGET and DKFZ. This results in mutated-pathway profiles per tumor which are tested for co-occurrence using rediscover. The test outputs significant co-occurring and mutually exclusive pathways from which we extract the underlying mutated gene pairs. The co-occurring and mutually exclusive gene pairs are then annotated for pathway epistasis, protein complexes and their presence in BioGRID and SynLethDB. Finally, the validity of the proposed gene pairs is examined in literature. The test detected 439 (ME: 369, CO:70) significant pathway pairs in TARGET and 49 (ME: 31, CO:18) in DKFZ across several cancer types. Out of these, 3185 gene pairs were extracted in TARGET (ME: 2671, CO: 514) and 331 (ME: 16, CO: 315) in DKFZ. These relationships are significantly more than the ones found when testing solely for gene pairs. Pathways aid in partially decreasing findings due to subtype or pathway epistasis. For example, FLT3-KRAS found in TARGET-B-ALL by the gene test is not detected by the pathway test. The test was, also, able to uncover mutually exclusive (ME) gene pairs in smaller datasets. The initially found ME genes TP53-DROSHA in Wilms tumors and KIT-NRAS in AML tumors in TARGET by the gene test, were detected in the corresponding cancer types’ DKFZ datasets. In addition to TP53-DROSHA, TP53-DGCR8 were found ME in Wilms tumors of DKFZ, indicating the microprocessor complex mutations ME to TP53 mutations. To sum up, we present a pathway-informed synthetic lethality inference framework for pediatric cancer to explore synthetic lethal relationships and other complex functional relationships between mutated genes by exploiting already existing data and database information as an alternative avenue for follow-up experimentation. Citation Format: Anastasia Spinou, Richard Gremmen, Puck Veen, Jarno Drost, Patrick Kemmeren. A pathway-informed framework to infer synthetic lethal relationships in pediatric cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B027.