A causal Bayesian network model of disease progression mechanisms in chronic myeloid leukemia

Highlights

• A mathematical model of chronic myeloid leukemia based on causal Bayesian networks was developed to study disease progression mechanisms.

• Our results indicate that increase in Bcr-Abl expression levels is not sufficient to explain the phenotype of blast crisis.

• Positive feedback loops caused by secondary changes such as additional mutations are plausible candidates for disease progression mechanisms.

• Imatinib seems to prevent disease progression by interfering with feedback loops that drive the accumulation of secondary changes.

Abstract

Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system initiated by a single genetic mutation which results in the oncogenic fusion protein Bcr-Abl. Untreated, patients pass through different phases of the disease beginning with the rather asymptomatic chronic phase and ultimately culminating into blast crisis, an acute leukemia resembling phase with a very high mortality. Although many processes underlying the chronic phase are well understood, the exact mechanisms of disease progression to blast crisis are not yet revealed. In this paper we develop a mathematical model of CML based on causal Bayesian networks in order to study possible disease progression mechanisms. Our results indicate that an increase of Bcr-Abl levels alone is not sufficient to explain the phenotype of blast crisis and that secondary changes such as additional mutations are necessary to explain disease progression and the poor therapy response of patients in blast crisis.

Introduction

Chronic myeloid leukemia (CML) is a type of cancer which progressively disturbs the balance of the hematopoietic system by dysregulated growth of myeloic cells in the bone marrow. In the vast majority of cases, the disease is caused by a mutation resulting from a reciprocal translocation between chromosomes 9 and 22 (Hehlmann et al., 2007, Quintás-Cardama et al., 2010). The translocation gives rise to the so called BCR-ABL fusion gene, triggering the characteristic pathophysiological processes of CML. BCR-ABL encodes for an overactive Bcr-Abl tyrosine kinase and disturbs many cellular processes such as differentiation, proliferation and apoptosis (Ren, 2005, Quintás-Cardama and Cortes, 2009). BCR-ABL positive cells therefore gain a growth advantage leading to an expanding population of cancer cells which impedes physiological hematopoiesis in the bone marrow.

CML can be divided into three phases: the chronic phase (CP), accelerated phase (AP) and blast crisis (BC), the final stage which resembles an acute leukemia. In the chronic phase, the disease is usually asymptomatic or symptoms are unspecific. Hematologically, CP-CML is characterized by an increased proliferation of myeloic precursor cells such as blasts, increased blood levels of mature granulocytes or platelets and often splenomegaly (Apperley, 2015). After 7–10 years without treatment the disease progresses to AP-CML, characterized by increased cell numbers in the peripheral blood, accumulation of additional mutations, lower treatment response and a more severe symptomatic burden. Although an established clinical category, the concept of AP as a biological entity sui generis has been challenged by the finding of its high genetic similarity to BC, to which the disease ultimately progresses (Radich et al., 2006). Depending on the definition, the final phase is characterized by an increase in blasts to >20–30%, although blast numbers up to 90% and more can be found in some patients (Palandri et al., 2008). While the abundance of blasts in the peripheral blood frequently causes thrombotic events, the excessive growth of cancer cells in the bone marrow successively displaces physiological hematopoiesis leading to anemia, immunodeficiency and susceptibility for infections (Chereda and Melo, 2015). The joint of effect of these processes dramatically limits the survival prospect of patients in BC (Kantarjian et al., 2001, Kantarjian et al., 2012).

First-line therapy with tyrosine kinase inhibitors (TKIs) such as imatinib re-establishes a normal level of proliferation and apoptosis by effectively inhibiting the activity of the Bcr-Abl tyrosine kinase. Imatinib treatment during CP-CML diminishes disease activity usually below the limit of detection, prevents disease progression and thereby has led to a remarkably increased overall survival (Druker et al., 2006). Yet, due to residual cancer cells in dormancy, lifelong TKI therapy is mandatory for most patients, otherwise disease relapse is likely (Apperley, 2015). Once BC has occurred, the response to TKIs is dramatically worse (Jabbour et al., 2014).

Although recent decades have shed light on many details of the disease's pathophysiology, the mechanisms of disease progression to BC are still poorly understood. Several cellular and molecular changes are believed to contribute to this process, e.g. increase of Bcr-Abl expression, altered adhesion behavior, additional mutations, differential gene expression or altered mRNA metabolism (Shet et al., 2002, Silver, 2009, Perrotti, 2010, Chereda and Melo, 2015). However, neither of these changes have been proven to be necessary or sufficient for disease progression. Moreover, the mentioned alterations are only an excerpt of frequently observed changes and do not exclude each other (Calabretta and Perrotti, 2004, Chereda and Melo, 2015).

The majority of the hitherto developed mathematical models of CML have focused on aspects like cell population dynamics, mechanisms for residual disease and drug resistance based on mathematical frameworks like differential equations or rule based stochastic processes (Michor et al., 2005, Roeder et al., 2006, Foo et al., 2009). Yet, to our knowledge, few mathematical models so far have dealt with disease progression (e.g. Michor, 2007 and Sachs et al., 2011). Further theoretical investigations can therefore help to better evaluate the proposed mechanisms of disease progression (Michor, 2008). In this study we present a mathematical model of CML based on a causal Bayesian network in order to study such mechanisms. The paper is organized as follows: Section 2 gives a short introduction to the employed formalism and describes the model and the validation procedure. Section 3 presents the simulation results. Starting with a very simple model, different mechanisms will be added to the model in order to investigate their possible influence on disease progression. Disease progression is assessed by taking into account hematological and clinical variables that are essential for the phenotype of CML as well as the response to therapy. In Section 4 the model and its results are discussed with respect to biological plausibility, other mathematical models, limitations of the current approach and future research directions.