网络水平的激酶活性与B细胞淋巴瘤细胞系的差异性药物敏感性相关

Background: B-cell lymphomas display heterogeneous responses to kinase inhibitors, reflecting underlying variability in kinase activity and signaling network states. Understanding how baseline kinase activity and broader signaling network patterns relate to drug sensitivity could improve therapeutic stratification and uncover mechanisms of drug response. Methods: Drug sensitivity data (IC₅₀ values) for B-cell lymphoma cell lines were retrieved from the CancerRxGene database (Genomics of Drug Sensitivity in Cancer) and response variability for small-molecule inhibitors targeting serine/threonine kinase (STK) families was quantified using the standard deviation of its LN(IC₅₀) values across cell lines. For 11 B-cell lymphoma cell lines, kinase activity profiling was performed using KinomePro platform (PamGene International B.V.) and Upstream Kinase Analysis was used to predict kinases from the phosphorylation signatures. We conducted Multi-Omics Factor Analysis (MOFA) to integrate phosphorylation signatures and drug sensitivity data, identifying latent factors that capture correlations between kinases and drug responses. Results: Drug responses showed substantial heterogeneity across cell lines: 7% of drugs were homogeneous (LN(IC₅₀) SD < 0.5), 41% moderately heterogeneous (SD 0.5-1), and 52% heterogeneous (SD > 1). In cell lines where drug sensitivity was observed, only 10-30% showed elevated activity of the drug target kinase, and this correlation was not statistically significant. Correlation analysis (MOFA) identified latent factors that mapped kinase activity to drug sensitivity and revealed two distinct sensitivity clusters: one comprising PI3K/AKT/mTOR, central to cell growth, survival, metabolism, and proliferation; and a second cluster including CDK, ATM/ATR, Wee1, AURKA, and MAPK, central to DNA damage response, cell cycle regulation and checkpoint signaling. Upstream Kinase Analysis validated that cell lines sensitive to AKT/PI3K/mTOR inhibitors exhibited relatively high AKT and RSK signaling activity, whereas cell lines sensitive to inhibitors targeting cell cycle pathway showed higher baseline CDK and MAPK family kinases. Conclusions: Our integrative analysis of drug sensitivity and kinase-activity data demonstrates that B-cell lymphoma response is shaped not only by individual kinase activities but also by the architecture of signaling networks. The strong concordance between baseline kinase activity signatures and drug sensitivity highlights network-level determinants of response. Signal-network signatures associated with sensitivity may serve as biomarkers for stratification and provide mechanistic insight into heterogeneous drug responses. These findings lay a foundation for functional validation and the development of combination therapies guided by network-level dependencies.