Maier Julia, Schwab Julian D, Werle Silke D, Marienfeld Ralf, Stilgenbauer Stephan, Möller Peter, Ikonomi Nensi, Kestler Hans A
Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany.
Institute of Pathology, University Hospital of Ulm, 89081 Ulm, Germany.
Sci Adv. 2025 Sep 12;11(37):eadu7705. doi: 10.1126/sciadv.adu7705.
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.
慢性淋巴细胞白血病（CLL）是一种常见的肿瘤，存在转化为里氏综合征（RS）的风险，RS是一种侵袭性很强、预后很差的B细胞淋巴瘤。动物模型和细胞系的可用性有限，阻碍了对转化机制的理解。为了填补这一空白，我们建立了首个该疾病的计算机动态模型。我们的方法将数学逻辑建模与实验数据相结合，以确定疾病驱动因素、机制和潜在治疗靶点。我们通过将模型的输出结果与来自不同生物学水平的实验数据进行比较来验证模型，这些实验数据来自单细胞RNA测序分析和一个CLL/RS患者福尔马林固定石蜡包埋（FFPE）组织队列。我们的分析确定BMI1原癌基因和TP53缺失是RS进展的关键调节因子。此外，我们进行了计算机靶点筛选，以个性化方式确定有前景的靶点组合。通过数学建模与实验输出结果的协同作用，我们的模型为研究CLL向RS的转化过程提供了一种补充方法。
