Are T cells at the origin of B cell lymphomas?

Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma

We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.

An integrated computational/experimental model of lymphoma growth

Non-Hodgkin's lymphoma is a disseminated, highly malignant cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. A critical element of molecular resistance has been traced to the loss of functionality in proteins such as the tumor suppressor p53. We investigate the tissue-scale physiologic effects of this loss by integrating in vivo and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of lymphoma growth. We compare between drug-sensitive Eμ-myc Arf-/- and drug-resistant Eμ-myc p53-/- lymphoma cell tumors grown in live mice. Initial values for the model parameters are obtained in part by extracting values from the cellular-scale from whole-tumor histological staining of the tumor-infiltrated inguinal lymph node in vivo. We compare model-predicted tumor growth with that observed from intravital microscopy and macroscopic imaging in vivo, finding that the model is able to accurately predict lymphoma growth. A critical physical mechanism underlying drug-resistant phenotypes may be that the Eμ-myc p53-/- cells seem to pack more closely within the tumor than the Eμ-myc Arf-/- cells, thus possibly exacerbating diffusion gradients of oxygen, leading to cell quiescence and hence resistance to cell-cycle specific drugs. Tighter cell packing could also maintain steeper gradients of drug and lead to insufficient toxicity. The transport phenomena within the lymphoma may thus contribute in nontrivial, complex ways to the difference in drug sensitivity between Eμ-myc Arf-/- and Eμ-myc p53-/- tumors, beyond what might be solely expected from loss of functionality at the molecular scale. We conclude that computational modeling tightly integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma and provides a platform to generate confirmable predictions of tumor growth.

Non-Hodgkin's lymphoma is a cancer that develops from white blood cells called lymphocytes in the immune system, whose role is to fight disease throughout the body. This cancer can spread throughout the whole body and be very lethal – in the US, one third of patients will die from this disease within five years of diagnosis. Chemotherapy is a usual treatment for lymphoma, but the cancer can become highly resistant to it. One reason is that a critical gene called p53 can become mutated and help the cancer to survive. In this work we investigate how cells with this mutation affect the cancer growth by performing experiments in mice and using a computer model. By inputting the model parameters based on data from the experiments, we are able to accurately predict the growth of the tumor as compared to tumor measurements in living mice. We conclude that computational modeling integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma, and provides a platform to generate confirmable predictions of tumor growth.

Exploration of factors affecting the onset and maturation course of follicular lymphoma through simulations of the germinal center

Genetic mutations frequently observed in human follicular lymphoma (FL) B-cells result in aberrant expression of the anti-apoptotic protein bcl-2 and surface immunoglobulins (Igs) which display one or more novel variable (V) region N-glycosylation motifs. In the present study, we develop a simulation model of the germinal center (GC) to explore how these mutations might influence the emergence and clonal expansion of key mutants which provoke FL development. The simulations employ a stochastic method for calculating the cellular dynamics, which incorporates actual IgV region sequences and a simplified hypermutation scheme. We first bring our simulations into agreement with experimental data for well-characterized normal and bcl-2(+) anti-hapten GC responses in mice to provide a model for understanding how bcl-2 expression leads to permissive selection and memory cell differentiation of weakly competitive B-cells. However, as bcl-2 expression in the GC alone is thought to be insufficient for FL development, we next monitor simulated IgV region mutations to determine the emergence times of key mutants displaying aberrant N-glycosylation motifs recurrently observed in human FL IgV regions. Simulations of 26 germline V(H) gene segments indicate that particular IgV regions have a dynamical selective advantage by virtue of the speed with which one or more of their key sites can generate N-glycosylation motifs upon hypermutation. Separate calculations attribute the high occurrence frequency of such IgV regions in FL to an ability to produce key mutants at a fast enough rate to overcome stochastic processes in the GC that hinder clonal expansion. Altogether, these simulations characterize three pathways for FL maturation through positively selected N-glycosylations, namely, via one of two key sites within germline V(H) region gene segments, or via a site in the third heavy chain complementarity-determining region (CDR-H3) that is generated from VDJ recombination.