RESUMEN
Cancer cells frequently alter their lipids to grow and adapt to their environment1-3. Despite the critical functions of lipid metabolism in membrane physiology, signalling and energy production, how specific lipids contribute to tumorigenesis remains 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 tumour growth in multiple syngeneic models. Blocking sphingolipid production in cancer cells enhances the anti-proliferative effects of natural killer and CD8+ T cells partly via interferon-γ (IFNγ) signalling. Mechanistically, depletion of glycosphingolipids increases surface levels of IFNγ receptor subunit 1 (IFNGR1), which mediates IFNγ-induced growth arrest and pro-inflammatory signalling. Finally, pharmacological inhibition of glycosphingolipid synthesis synergizes with checkpoint blockade therapy to enhance anti-tumour immune response. Altogether, our work identifies glycosphingolipids as necessary and limiting metabolites for cancer immune evasion.
Asunto(s)
Glicoesfingolípidos , Evasión Inmune , Neoplasias , Proteínas Proto-Oncogénicas p21(ras) , Escape del Tumor , Animales , Femenino , Ratones , Linfocitos T CD8-positivos/inmunología , Línea Celular Tumoral , Proliferación Celular , Glicoesfingolípidos/biosíntesis , Glicoesfingolípidos/deficiencia , Glicoesfingolípidos/inmunología , Glicoesfingolípidos/metabolismo , Inhibidores de Puntos de Control Inmunológico/farmacología , Inhibidores de Puntos de Control Inmunológico/uso terapéutico , Receptor de Interferón gamma/metabolismo , Interferón gamma/inmunología , Células Asesinas Naturales/inmunología , Ratones Endogámicos C57BL , Neoplasias/inmunología , Neoplasias/metabolismo , Neoplasias/patología , Proteínas Proto-Oncogénicas p21(ras)/metabolismo , Transducción de Señal , LipidómicaRESUMEN
Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.