Abstract
The clinical course of B-CLL is heterogeneous. This heterogeneity leads to a clinical dilemma: can we identify those patients who will benefit from early treatment and predict the survival? In recent years, mathematical modelling has contributed significantly in understanding the complexity of diseases. In order to build a mathematical model for determining prognosis of B-CLL one has to identify, characterise and quantify key molecules involved in the disease. Here we discuss the need and role of mathematical modelling in predicting B-CLL disease pathogenesis and suggest a new systems biology approach for a personalised therapy of B-CLL patients.
Highlights
B-cell chronic lymphocytic leukemia (B-CLL), is the most common leukaemia in the western world
Historical reports exists for identical twins [4], mother and son [5], grandfather, son, and grandson [6] affected due to familial aggregation of chronic lymphocytic leukaemia pathogenesis associated with immune defects
This review focuses on our current understanding of clinical course and molecular mechanisms in B-CLL disease, and suggests the application of systems biology as an approach in personalized treatment for B-CLL patients
Summary
B-cell chronic lymphocytic leukemia (B-CLL), is the most common leukaemia in the western world. It is not curable [1] and the ability of radiation and chemotherapeutic agents to induce clinically significant regression of the disease in patients with CLL has not improved for the last decades [2]. Historical reports exists for identical twins [4], mother and son [5], grandfather, son, and grandson [6] affected due to familial aggregation of chronic lymphocytic leukaemia pathogenesis associated with immune defects. CLL risks were similar in parents, siblings, and offspring cases, and in male and female relatives. They were not affected by the case’s age at diagnosis. This review focuses on our current understanding of clinical course and molecular mechanisms in B-CLL disease, and suggests the application of systems biology as an approach in personalized treatment for B-CLL patients
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