CLL to Richter syndrome: Integrating network strategies with experiments elucidating disease drivers and personalized therapies.

Abstract

Chronic lymphocytic leukemia (CLL) is a common neoplasm that carries the risk of transformation into Richter's syndrome (RS), a highly aggressive B cell lymphoma with poor prognosis. Limited availability of animal models and cell lines hinders understanding of transformation mechanisms. Addressing this gap, we established the first in silico dynamic model of the disease. Our methodology integrates mathematical logic modeling with experimental data to identify disease drivers, mechanisms, and potential therapeutic targets. We validated the model by comparing the model's readout with experimental data from different biological levels, such as single-cell RNA sequencing analyses and a CLL/RS patient formalin-fixed paraffin-embedded (FFPE) tissue cohort. Our analyses identified BMI1 proto-oncogene and TP53 loss as key RS progression regulators. In addition, we performed an in silico target screening to identify promising target combinations in a personalized fashion. Through the synergy of mathematical modeling with experimental readouts, our model provides a complementary approach to investigate the process of CLL transformation to RS.