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

Metabolic rewiring in cancer cells facilitates the supply of essential precursors for uncontrolled tumor growth. Exploring these cancer-specific metabolic alterations 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. Targeting multiple genes has been proposed as a solution to overcome these issues, e.g., a synthetic lethal (SL) set, defined as a minimal combination of non-lethal genetic perturbations that lead to cell death. This study theoretically examined the potential of SL sets to identify selective drug targets across 13 cancer types 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 lack of harmful effects of the 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 SL sets with more than two targets in each set. Interestingly, for specific cancers where single essential or SL genes could not provide viable therapeutic solutions, SSDTs were provided by higher-order SL sets. Thus, for the first time, this study demonstrates that leveraging higher-order SL sets may offer promising strictly selective therapeutic 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 essential to identify the most promising treatment candidates for future clinical studies/applications.