Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

癌症是由不同基因的改变驱动的，从而产生可以治疗靶向的依赖性。然而，许多遗传依赖性已被证明在肿瘤之间不一致。在这里，我们描述了 SCHEMATIC，这是一种识别高度渗透、可操作的遗传相互作用核心网络的策略。首先，跨肿瘤谱系的系统组合敲除扰乱了基本的细胞过程，确定了 1,805 种合成致死相互作用（95% 未报道）。然后通过分层池分析相互作用，揭示一半按组织类型或生物标志物状态可靠地分离 （51%），而相当少数 （34%） 跨谱系渗透。相互作用集中在 49 个多基因系统上，包括 MAPK 信号转导和 BAF 转录调节复合物，它们在破坏聚合酶时至关重要。大约 266 种相互作用转化为药物敏感性的强大生物标志物，包括 KDM5C/6A 组蛋白去甲基化酶的频繁遗传改变，这些酶对 TIPARP （PARP7） 的抑制敏感。SCHEMATIC 提供了一种上下文感知、数据驱动的方法，可将基因改变与靶向治疗相匹配。