Glycosphingolipid synthesis mediates immune evasion in Kras-driven cancer

Cancer cells frequently alter their lipids to grow and adapt to their environment1–3. Despite the critical functions of lipid metabolism in membrane physiology, signaling, and energy production, how specific lipids contribute to tumorigenesis is incompletely understood. Here, using functional genomics and lipidomic approaches, we identified de novo sphingolipid synthesis as an essential pathway for cancer immune evasion. Synthesis of sphingolipids is surprisingly dispensable for cancer cell proliferation in culture or in immunodeficient mice but required for tumor growth in multiple syngeneic models. Blocking sphingolipid production in cancer cells enhances the anti-proliferative effects of natural killer (NK) and CD8+ T cells partly via interferon gamma (IFNg) signaling. Mechanistically, depletion of glycosphingolipids increases surface levels of IFNg receptor subunit 1 (Ifngr1), mediating IFNg-induced growth arrest and proinflammatory signaling. Finally, pharmacological inhibition of glycosphingolipid synthesis synergizes with checkpoint blockade therapy to enhance anti-tumor immune response. Altogether, our work identifies glycosphingolipids as necessary and limiting metabolites for cancer immune evasion. Tumors were removed from mice 12-13 days after injection and placed into DMEM+ media (DMEM/F-12 (Gibco #11320033, 10% FBS) and kept on ice. Once all tumors were removed, they were washed once with ice cold PBS and placed into a 1.5 mL tube containing dissociation buffer (DMEM+, 1 mg/mL Collagenase D (Sigma-Aldrich #11088858001), 25 ug/mL DNAse (NEB #M0303S)). Tumors were briefly dissociated using scissors and then incubated on a shaking heat block for 35 minutes at 37°C. Each sample was then passed through a 70 µm cell trainer which was rinsed with 5 mL of DMEM+. Cell suspensions were centrifuged at 300 X g for 5 minutes at 4°C. Cells were resuspended in 3 mL of ACK buffer and incubated at room temperature for 4 minutes. Then, 7 mL of DMEM+ was added before centrifuging again at 300 X g for 5 minutes at 4°C. For isolation of CD45+ tumor-infiltrating immune cells, cells were resuspended in 30 µL of FACS buffer containing Fc-block (1:200, BD BioSciences #553141) and incubated on ice for 10 minutes. 30 µL of CD45 antibody in FACS buffer was added to cells (1:10 final concentration) and incubated on ice for 20 minutes. Cells were washed by adding 1 mL of FACS buffer prior to centrifugation at 1000 X g for 5 minutes at 4°C. Cells were resuspended in 200 µL of DAPI-containing FACS buffer. 10,000 CD45+/ DAPI negative cells/ mouse were sorted into a pre-coated Eppendorf tube for a total of 60,000 cells/ condition. 5,000 cells/ condition were targeted for single-cell RNA-sequencing on a Chromium Single Cell System (10x Genomics). Samples were processed as/ the manufacturer’s instructions (Chromium single cell 3’ reagents, v3.1 chemistry) and libraries were sequenced using Illumina NovaSeq. Single cell datasets for each experiment were independently assessed for data quality following the guidelines described in Luecken and Theis, 2019 and Amezquita et al., 202048,49. Cells with more than 10% mitochondrial transcripts as well as cells that had fewer than 250 feature counts or expressed fewer than 500 genes were removed. After QC, Seurat (v4.0.3) was used for normalization, graph-based clustering and differential expression analysis50. Each dataset was normalized using SCTransform and the 5000 most variable genes were identified with SelectIntegrationFeatures. Both PMXS and AB datasets were integrated into a singular dataset via using the PrepSCTIntegration, FindIntegrationAnchors, and IntegrateData function51. MAGIC imputation was conducted on integrated data to impute missing values and account for technical noise52. RunPCA was implemented on the integrated datasets to identify the top 50 principal components (PCs) that were used for UMAP analysis and clustering. Louvain clustering at a resolution of 1 was implemented. Clusters were labeled in accordance with expression levels of CD45 tumor infiltrating lymphocyte subtype signatures . Differential expression analysis was conducted between PMXS vs AB using the FindMarkers function with the MAST method to evaluate differences within the transcriptome54. Wilcoxon rank-sum tests to determine if gene expression was significant was conducted using the wilcox.test function in stats (v4.1.0, R Core Team 2021). For the density map of the differential expression of Ifng, cells were binned based on proximity in the uniform manifold approximation and Projection (UMAP). Log fold change of Ifng expression (KO versus AB) was calculated using the cells in each bin and transposed back onto the UMAP at the generalized location of the cells.