美国加州大学圣迭戈分校Trey Ideker团队发现，一种多谱系筛选方法识别出人类癌症中可操作的合成致死相互作用。相关论文于2024年11月18日在线发表在《自然—遗传学》杂志上。

据了解，癌症由多种基因的改变驱动，形成可通过治疗靶向的依赖关系。然而，许多遗传依赖在不同肿瘤中表现不一致。

研究人员描述了SCHEMATIC，一种识别核心网络中高度渗透、可操作的遗传相互作用的策略。首先，通过对肿瘤谱系进行系统的组合性基因敲除来扰动基本细胞过程，研究人员识别了1805个合成致死相互作用（95%为未报道的）。

然后，通过分层合并分析这些相互作用，研究人员揭示了其中一半可靠地区分不同的组织类型或生物标志物状态（51%），并且有相当一部分在不同谱系中表现出渗透性（34%）。

这些相互作用集中在49个多基因系统上，包括MAPK信号通路和BAF转录调控复合物，这些系统在聚合酶功能受损时变得至关重要。

大约266个相互作用转化为药物敏感性的稳健生物标志物，包括KDM5C/6A组蛋白去甲基化酶中的常见遗传改变，这些改变对TIPARP（PARP7）抑制敏感。

SCHEMATIC提供了一种基于数据的、考虑背景的策略，可用于将遗传改变与靶向治疗匹配。

附：英文原文

Title: A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Author: Fong, Samson H., Kuenzi, Brent M., Mattson, Nicole M., Lee, John, Sanchez, Kyle, Bojorquez-Gomez, Ana, Ford, Kyle, Munson, Brenton P., Licon, Katherine, Bergendahl, Sarah, Shen, John Paul, Kreisberg, Jason F., Mali, Prashant, Hager, Jeffrey H., White, Michael A., Ideker, Trey

Issue&Volume: 2024-11-18

Abstract: 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.

DOI: 10.1038/s41588-024-01971-9

Source: https://www.nature.com/articles/s41588-024-01971-9