Cancer ResearchCancer BiologyPeer-reviewed journal article
Lipid composition differentiates ferroptosis sensitivity between in vitro and in vivo systems
Vivian S. ParkiD, Lauren E. PopeiD, Justin P. IngramiD, Grace A. AlchemyiD, Julie J. PurkaliD, Magdalena B. MurrayiD, Sha JiniD, Eli Y. Andino-FrydmaniD, Sanjana SinghiD, Anlu CheniD, Priya NarayananiD, Sarah KongpachithiD, Darren C. PhillipsiD, Scott J. DixoniD, Relja PopoviciD
Abstract
Ferroptosis is a regulated non-apoptotic cell death process characterized by iron-dependent lipid peroxidation. This process has recently emerged as a promising approach for cancer therapy. Peroxidation of polyunsaturated fatty acid-containing phospholipids (PUFA-PLs) is necessary for the execution of ferroptosis. Ferroptosis is normally suppressed by glutathione peroxidase 4 (GPX4), which reduces lipid hydroperoxides to lipid alcohols. Some evidence indicates that GPX4 may be a useful target for drug development, yet factors that govern GPX4 inhibitor sensitivity in vivo are poorly understood. We find that pharmacological and genetic loss of GPX4 function was sufficient to induce ferroptosis in multiple adherent ("2D") cancer cell cultures. However, reducing GPX4 protein levels did not affect tumor xenograft growth when these cells were implanted in mice. Furthermore, sensitivity to GPX4 inhibition was markedly reduced when cells were cultured as spheroids ("3D"). Mechanistically, growth in 3D versus 2D conditions reduced the abundance of PUFA-PLs. 3D culture conditions upregulated the monounsaturated fatty acid (MUFA) biosynthetic gene stearoyl-CoA desaturase (SCD). SCD-derived MUFAs appear to protect against ferroptosis in 3D conditions by displacing PUFAs from phospholipids. Various structurally related long chain MUFAs can inhibit ferroptosis through this PUFA-displacement mechanism. These findings suggest that growth-condition-dependent lipidome remodeling is an important mechanism governing GPX4 inhibitor effects. This resistance mechanism may specifically limit GPX4 inhibitor effectiveness in vivo.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell signaling & trafficking
- approach: experimental assay
- approach: statistical model
- Citations
- 37
- FWCI
- 22.19
- Field percentile
- top 1%
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