Multiomics Integration Elucidates Metabolic Modulators of Drug Resistance in Lymphoma

BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL). B-cell NHLs rely on Brutons tyrosine kinase (BTK) mediated B-cell receptor signaling for survival and disease progression. However, they are often resistant to BTK inhibitors or soon acquire resistance after drug exposure resulting in the drug tolerant form. The drug tolerant clones proliferate faster, have increased metabolic activity, and shift to oxidative phosphorylation; however, how this metabolic programming occurs in the drug resistant tumor is poorly understood. MethodsIn this study, we explored for the first time the metabolic regulators of ibrutinib-resistant activated B-cell (ABC) DLBCL using a multi-omics analysis that integrated metabolomics (using high-resolution mass spectrometry) and transcriptomic (gene expression analysis). Overlay of the unbiased statistical analyses, genetic perturbation and pharmaceutical inhibition, were further used to identify the key players that contribute to the metabolic reprograming of the drug resistant clone. ResultsGene-metabolite integration revealed interleukin 4 induced 1 (IL4I1) at the crosstalk of two significantly altered metabolic pathways involved in the production of various amino acids. We showed for the first time that drug resistant clones undergo metabolic reprogramming from glycolysis towards oxidative phosphorylation & is activated via the BTK-PI3K-AKT-IL4I1 axis and can be targeted therapeutically. ConclusionsOur report shows how these cells become dependent on PI3K/AKT signaling for survival after acquiring ibrutinib resistance and shift to sustained Oxidative phosphorylation, additionally we outline the compensatory, pathway that regulates this metabolic reprogramming in the drug resistant cells. These findings from our unbiased analyses highlight the role of metabolic reprogramming during drug resistance development. Furthermore, our work demonstrates that a multi-omics approach can be a powerful and unbiased strategy to uncover genes and pathways that drive metabolic dysregulation in cancer cells.