Survival of chronic lymphocytic leukemia (CLL) cells strictly depends on the support of an appropriate tumor microenvironment. Here, we demonstrate that LYN kinase is essential for CLL progression. Lyn deficiency results in a significantly reduced CLL burden in vivo. Loss of Lyn within leukemic cells reduces B cell receptor (BCR) signaling including BTK phosphorylation, but surprisingly does not affect leukemic cell expansion. Instead, syngeneic CLL transplantation of CLL cells into Lyn- or Btk-deficient recipients results in a strongly delayed leukemic progression and prolonged survival. Moreover, Lyn deficiency in macrophages hinders nursing functions for CLL cells, which is mediated by direct contact rather than secretion of soluble factors. Taken together, LYN and BTK seem essential for the formation of a microenvironment supporting leukemic growth.

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI—statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF- κ B single—cell signaling responses to TNF- α reveals that NF- κ B signaling dynamics improves discrimination of high concentrations of TNF- α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.