Higher order synthetic lethals are keys to minimize cancer treatment effects on non-tumor cells

Abstract

Metabolic rewiring in cancer cells facilitates the provision of essential precursors for the unbridled growth of tumors. Exploring these cancer-specific metabolic changes offers potential selective therapeutic strategies. However, targeting a single essential gene in cancer treatment often faces challenges, including resistance, lack of targetable oncogenes, and potential harm to non-tumor cells. Attacking multiple targets is hypothesized as a solution to overcome these issues, e.g., a synthetic lethal set, defined as a minimal combination of non-lethal genetic mutations leading to cell death. This study examined the potential of synthetic lethal sets to identify selective drug targets for 13 cancers and the corresponding non-tumor tissues, utilizing context-specific genome-scale metabolic models. To ensure the minimization of therapeutic side effects, this work introduced the concept of strictly-selective drug targets (SSDTs) and the harmlessness of identified targets in all 13 different non-tumor tissues was meticulously verified. Accordingly, for 13 types of cancers, over 500 SSDTs were identified, predominantly including higher-order synthetic lethal sets with more than two targets in each set. Interestingly, for specific cancers where single essential or synthetic lethal genes could not provide acceptable solutions, SSDTs were provided by higher-order synthetic lethal sets. Therefore, for the first time, this study successfully showed that leveraging higher-order synthetic lethal sets holds the key to promising strictly-selective solutions. Furthermore, nine quadruple SSDTs were identified which commonly target five different cancers without harming any of the 13 non-tumor tissues. Further experimental validation of these findings is required to select the most promising treatments for clinical studies.