Patient-specific B-cell lymphoma modeling identifies cooperating genetic alterations and the critical influence of patient context

Diffuse large B-cell lymphoma (DLBCL) is a molecularly heterogeneous disease with high genetic complexity and interpatient variability. Sequencing studies of representative patient cohorts have identified a comprehensive set of genetic driver alterations, enabling patient stratification for personalized treatment strategies. To date, it remains insufficiently understood how these ncogenic driver alterations operate in concert and shape malignant cell states. Here, we use a computational approach that embeds patient data and experimentally characterized molecular perturbations in mechanism-based mathematical modeling to study the effect of genetic alterations in a network context. Based on a detailed pathway model capturing key cellular processes, including apoptosis, cell division, and B-cell differentiation, we created personalized models for a cohort of 284 patients, of which 90.5% reflect an aberrant cell state. Systematic assessment of the functional effects of individual and combinatorial alterations within these models identified previously not appreciated cooperating alterations that operate in synergy, such as mutated NFKBIE and BCL6 structural variant. Notably, we identify a strong context dependency of functional effects, as identical alterations exert varying effects in different patient models. Incorporation of the network context is therefore essential for understanding DLBCL heterogeneity and selecting therapeutic targets for personalized and more efficient treatment strategies.