B-cell receptor (BCR) engagement is widely acknowledged to sustain aberrant cell behavior and uncontrolled monoclonal proliferation in chronic lymphocytic leukemia (CLL), as well as in most leukemias and lymphomas arising from mature B lymphocytes. The precise mechanisms by which BCR signaling controls neoplastic B-cell proliferation are ill characterized. In this work, primary leukemic cells of untreated patients at initial stage of CLL (Binet stage A / Rai 0) presenting biological characteristics of aggressive form of the disease (unmutated IGHV genes and ZAP70 protein expression) were studied.

Proliferation of CLL cells was induced ex vivo in six CLL samples using anti-IgM, together with mandatory co-stimulating factors (CD40L, IL-4 and IL-21) (Schleiss, Sci Rep, 2019). Using this model, we generated a unique set of 108 transcriptional and proteomic profiles during four days after activation (9 points from T0 to 96 hours after stimulation). A total of 23,348 transcripts and 50,503 unique peptides, the latter corresponding to 4,664 unique proteins, were identified and quantified. Statistical analysis of genes and proteins expression patterns identified a structured proliferative signature. In unsupervised analysis, principal component analysis (PCA) representation of the whole transcriptional profile showed the temporality of the response. Moreover, unsupervised temporal gene expression analysis using iterative optimization revealed clusters of temporal patterns exhibiting structured expression modulations during the time course after cell stimulation. Although the overall proteomic response appeared less structured than the transcriptional one at early time points after stimulation, samples showed a tendency for segregation with respect of their proliferative response at the two later time points. Hierarchical clustering, aimed to search for correlations between the transcriptional profiles, confirmed the temporal organization of the samples, while proliferative and non-proliferative cells were still distinguishable.

Also, we identified a CLL cell activation signature corresponding to 3,097 differentially expressed genes (DE) and 1,209 differentially-abundant (DA) proteins. This unique dataset provided a unique opportunity to model the proliferative program of CLL cells after BCR engagement. We used the reverse engineering approach based on regression and system of equations previous published (Vallat et al., PNAS 2013). Thus, a temporal label was assigned to each gene and protein, restricting its potential link to actors with later temporal labels. The temporal model inferred with the genes and proteins datasets of proliferating CLL cells resulted in a regulatory network composed of 2,167 genes and 1,074 proteins representing 2,848 unique symbols, among which 395 gene-protein pairs, connected by 53,131 oriented links. Different temporal layers of actors were identified. At the earliest time-point after cell activation a first group was identified, involving transcriptional repression, negative regulation of BCR signaling, apoptotic process actors and a second group involving G1/S transition, DNA-replication genes. Later, expression of G0/G1 switch genes and regulators of cell proliferation and differentiation were identified.

In conclusion, using a large dataset of temporal transcriptional and proteomic measurements coupled with mathematical modelling, we were able to unravel the molecular program downstream the signaling cascade activated by the engagement of the BCR and triggering primary CLL cell proliferation. This program was proven to organize around a limited number of hub genes and proteins whose sequential commitment drives the cellular response leading to proliferation days after cell activation. These hubs represent potential candidates for the development of novel therapeutic strategies for the treatment of aggressive CLL.


No relevant conflicts of interest to declare.

Author notes


Asterisk with author names denotes non-ASH members.