Alternative strategies for optimizing treatment of chronic lymphocytic leukemia with complex clonal architecture.

In silico simulation of pre-clinical and clinical data may accelerate pre-clinical and clinical trial advances, leading to benefits for therapeutic outcomes, toxicity and cost savings. Combining this with clonal architecture data may permit truly personalized therapy. Chronic lymphocytic leukemia (CLL) exhibits clonal diversity, evolution and selection, spontaneously and under treatment pressure. We apply a dynamic simulation model to published CLL clonal architecture data to explore alternative therapeutic strategies, focusing on BTK inhibition. By deriving parameters of clonal growth and death behavior we model continuous vs time-limited ibrutinib therapy, and find that, despite persistence of disease, time to clinical progression may not differ. This is a testable hypothesis. We model IgVH-mutated CLL vs unmutated CLL by varying proliferation and find, based on the limited available data about clonal dynamics after such therapy, that there are differences predicted in response to anti-CD20 efficacy. These models can suggest potential clinical trials, and also indicate what additional data are needed to improve predictions. Ongoing work will expand modeling to agents such as venetoclax and to T cell therapies.

Progression and Transformation of Clonally Heterogeneous B-cell Lymphoma.

Indolent B- cell non-Hodgkin lymphoma can transform into aggressive lymphoma. We extend our prior mathematical model to analyze and predict transformation. To provide additional confidence in our model, we compare it with SCID mouse data for combination therapy of Diffuse Large B-cell Lymphoma, an aggressive form of the disease. We develop a two cell model that includes indolent and aggressive clones but no immune response and use it to predict transformation. An approximate model for the time to transformation is derived that provides insight regarding scaling effects. We then add an immune response and therapeutic measures, and illustrate the complex interactions among the various processes, with a focus on transformation. The implications for initial diagnosis and treatment of non-Hodgkin lymphoma are discussed.

Immune response to combination therapy for non-Hodgkin lymphomas.

A parametric model of tumor response to combination therapy in the presence of an immune system is described. Synergistic mechanisms which induce tumor regression are simulated with a coupled set of equations. The simulations are first compared to tumor history data obtained with a SCID mouse model to determine key parameters; predictions are then made for an immune-competent animal. The minimum immune cell birth rate relative to malignant B-cell birth rate necessary to induce tumor regression is determined, and optimization of drug combinations in the presence of an immune response is explored. The delayed effect of an immune response relative to drug scheduling is examined, and a mechanism for disease transformation in heterogeneous tumors is proposed.

Parametric model of combination therapy for Non-Hodgkin Lymphoma.

The development and clinical testing of drug combinations for the treatment of Non-Hodgkin Lymphoma (NHL) and other cancers has recently shown great promise. However, determining the optimum combination and its associated dosages for maximum efficacy and minimum side effects is still a challenge. This paper describes a parametric analysis of the dynamics of malignant B-cells and the effects of an anti-sense oligonucleotide targeted to BCL-2 (as-bcl-2), anti-CD-20 (rituximab) and their combination, for a SCID mouse human lymphoma xenograft model of NHL. Our parametric model is straightforward. Several mechanisms of malignant B-cell birth and death in the nodal micro-environment are simulated. Cell death is accelerated by hypoxia and starvation induced by tumor scale, by modification of anti-apoptosis with as-bcl-2, and by direct kill effects of rituximab (cell kill by cytotoxic immune cells is not included, due to the absence of an immune system in the corresponding experiments). We show that the cell population dynamics in the control animals are primarily determined by K*, the ratio of rate constants for malignant cell death, K(d), and cell birth, K(b). Tumor growth with independent treatments is reproduced by the model, and is used to predict their effect when administered in combination. Malignant cell lifetimes are derived to provide a quantitative comparison of the efficacy of these treatments. Future experimental and clinical applications of the model are discussed.