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2022, Nature Metabolism. DOI: 10.1038/s42255-022-00636-3
Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer
Abhinav Achreja , Tao Yu , Anjali Mittal , Srinadh Choppara , Olamide Animasahun , Minal Nenwani , Fulei Wuchu , Noah Meurs , Aradhana Mohan , Jin Heon Jeon , Itisam Sarangi , Anusha Jayaraman , Sarah Owen , Reva Kulkarni , Michele Cusato , Frank Weinberg , Hye Kyong Kweon , Chitra Subramanian , Max S. Wicha , Sofia D. Merajver , Sunitha Nagrath , Kathleen R. Cho , Analisa DiFeo , Xiongbin Lu , Deepak Nagrath
Abstract:
Recurrent loss-of-function deletions cause frequent inactivation of tumour suppressor genes but often also involve the collateral deletion of essential genes in chromosomal proximity, engendering dependence on paralogues that maintain similar function. Although these paralogues are attractive anticancer targets, no methodology exists to uncover such collateral lethal genes. Here we report a framework for collateral lethal gene identification via metabolic fluxes, CLIM, and use it to reveal MTHFD2 as a collateral lethal gene in UQCR11-deleted ovarian tumours. We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation. This UQCR11–MTHFD2 collateral lethality is confirmed in vivo, with MTHFD2 inhibition leading to complete remission of UQCR11-deleted ovarian tumours. Using CLIM’s machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumours.
2022-10-31 22:14:00
#paper doi:https://doi.org/10.1038/s42255-022-00636-3 Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer 基因组结构改变导致肿瘤抑制基因功能缺失失活,是肿瘤发生的重要驱动因素。这些缺失为癌细胞提供了功能和适应性优势,但由于邻近染色体中的必要基因的缺失,为了避免癌细胞死亡,这些细胞会找到一种具有类似功能的基因以保持细胞存活。本文作者设计了一个集成的工作流程(CLIM),利用癌症患者(TCGA)的基因组和转录组学特征来识别代谢基因缺失,重建基因组规模代谢模型(GSMMs),进行基于细胞目标的代谢通量分析,来揭示副致死靶向的代偿代谢途径。通过抑制副致死靶向的代偿代谢途径,达到精准杀死肿瘤细胞的目的。通过该算法,该团队成功在高级别浆液性卵巢癌(HGSOC)中 预测出 伴随 19p13.3 缺失 的代谢基因 UQCR11 及其 旁系途径 MTHFD2。实验部分通过示踪剂动态追踪实验、UQCR11/MTHFD2 缺失细胞代谢变化、 MTHFD2 基因是否敲除 的19p13.3 肿瘤小鼠模型的肿瘤生长等实验验证了预测的副致死靶点的有效性。
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