Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma

Abstract

Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduce resistance towards treatment. However, integration of patient-specific information into a computational model is challenging and has not been achieved for DLBCL. Here, we developed a computational model describing signaling pathways and expression of critical germinal center markers. The model integrates the regulatory mechanism of the signaling and gene expression network and covers more than 50 components, many carrying genetic lesions common in DLBCL. Using clinical and genomic data of 164 primary DLBCL patients, we implemented mutations, structural variants and copy number alterations as perturbations in the model using the CoLoMoTo notebook. Leveraging patient-specific genotypes and simulation of the expression of marker genes in specific germinal center conditions allows us to predict the consequence of the modeled pathways for each patient. Finally, besides modeling how genetic perturbations alter physiological signaling, we also predicted for each patient model the effect of rational inhibitors, such as Ibrutinib, that are currently discussed as possible DLBCL treatments, showing patient-dependent variations in effectiveness and synergies.

A multiscale spatial modeling framework for the germinal center response

“…Many mathematical GC models have been developed in the past two decades using a variety of computational approaches, including deterministic, stochastic, agent-based, and hybrid ones. Focusing on various aspects of the GCR and simulating at various biological scales, these models have greatly aided our understanding of this long known phenomenon by investigating possible modes of mechanisms of GC dynamics ( 34 , 35 , 72 , 90 – 94 ), exploring optimal design of vaccination schemes ( 95 – 99 ), and predicting GC-associated disease outcomes ( 100 ). While these models focused on the complex processes of affinity maturation and B cell population dynamics, rarely were molecular networks included to drive the B cell behaviors and GC evolution.…”

A computational model of the DNA damage-induced IKK/ NF-κB pathway reveals a critical dependence on irradiation dose and PARP-1

“…It will also be an important extension for disease models capturing the cellular response to oncogenic perturbations. 76 , 77 …”

Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

“…Performing large-scale computational simulations can be computationally challenging and require substantial computational resources given the size and complexity of the molecular networks simulated here (194 equations, and 563 parameters). Previous work has applied logical modelling (molecular components are discretised into high/medium/low) to B cell lymphoma to overcome this challenge (40). Here, we found a continuous modelling approach was able to identify many gene dose-dependent effects that could not be identified with logical modelling, including how chromosomal gain and amplification confer distinct prognoses.…”

“…We used established multi-scale, agent-based models of B cells (18,19), in which cellular signalling networks were encoded in continuous ODE models. Logical modelling is frequently used to simulate networks of this size and complexity, and indeed has been previously applied to B-cell lymphoma (37). Here, through leveraging a continuous modelling framework, we were able to identify many dose-dependent effects, including how chromosomal gain and amplification confer distinct prognoses.…”